{"id":97697,"date":"2026-09-18T03:11:29","date_gmt":"2026-09-18T00:11:29","guid":{"rendered":"https:\/\/twodots.gr\/?p=97697"},"modified":"2026-09-18T03:11:31","modified_gmt":"2026-09-18T00:11:31","slug":"clip-perifereiaki-geolocalization-prosarmogi-skinis","status":"publish","type":"post","link":"https:\/\/twodots.gr\/en\/clip-perifereiaki-geolocalization-prosarmogi-skinis\/","title":{"rendered":"\u03a4\u03b9 \u03bc\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 \u03c4\u03bf CLIP \u03b3\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03c0\u03cc\u03bb\u03b7; \u0397 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03b1\u03c0\u03bf\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03c4\u03b7 \u03b4\u03cd\u03bd\u03b1\u03bc\u03b7 \u03c4\u03b7\u03c2 \u03c3\u03ba\u03b7\u03bd\u03ae\u03c2"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>Answer first:<\/strong> \u03c4\u03bf CLIP \u03b4\u03b5\u03bd \u00ab\u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9\u00bb \u03bc\u03b9\u03b1 \u03c0\u03cc\u03bb\u03b7 \u03cc\u03c0\u03c9\u03c2 \u03ad\u03bd\u03b1\u03c2 \u03ac\u03bd\u03b8\u03c1\u03c9\u03c0\u03bf\u03c2. \u03a3\u03c4\u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b3\u03b9\u03b1 \u03bf\u03ba\u03c4\u03ce \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ad\u03c2 \u03c4\u03bf\u03c5 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03bf\u03c5 Los Angeles, \u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc 39,03% \u03c3\u03b5 82,10% \u03cc\u03c4\u03b1\u03bd \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03cc\u03c3\u03c4\u03b7\u03ba\u03b5 \u03bf visual encoder. \u0397 \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b8\u03b7\u03ba\u03b5 \u03bc\u03b5 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03ac\u03b8\u03b9\u03ba\u03c4\u03b7 \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7 \u03c4\u03b7\u03c2 \u03c3\u03ba\u03b7\u03bd\u03ae\u03c2, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b5\u03be\u03b1\u03c6\u03b1\u03bd\u03b9\u03c3\u03c4\u03b5\u03af \u03b7 \u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03b2\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7\u03c2, \u03c4\u03bf\u03c5 \u03bf\u03c5\u03c1\u03b1\u03bd\u03bf\u03cd \u03ba\u03b1\u03b9 \u03ac\u03bb\u03bb\u03c9\u03bd appearance cues.<\/p>\n<p>\u03a4\u03bf \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1 \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc computer-vision pilot \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b9\u03c0\u03bb\u03cc: \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 frozen readout, LoRA \u03ba\u03b1\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 fine-tuning \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf dataset, \u03b1\u03bb\u03bb\u03ac \u03bc\u03b7 \u03b8\u03b5\u03c9\u03c1\u03b5\u03af\u03c4\u03b5 \u03c4\u03bf \u03c5\u03c8\u03b7\u03bb\u03cc score \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03b3\u03b5\u03bd\u03af\u03ba\u03b5\u03c5\u03c3\u03b7\u03c2. \u0395\u03b4\u03ce \u03c4\u03bf 79,68% \u03c4\u03c9\u03bd test \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd \u03b2\u03c1\u03b9\u03c3\u03ba\u03cc\u03c4\u03b1\u03bd \u03ad\u03c9\u03c2 50 \u03bc\u03ad\u03c4\u03c1\u03b1 \u03b1\u03c0\u03cc coordinate \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2, \u03ac\u03c1\u03b1 \u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03bc\u03b5\u03c4\u03c1\u03ac \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03bd\u03ad\u03b5\u03c2 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03b5 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ad\u03c2 \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c3\u03af\u03b5\u03c2.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">Contents<\/div>\n<ul>\n<li><a href=\"#topikos-geoentopismos\">\u0393\u03b9\u03b1\u03c4\u03af \u03bf \u03c4\u03bf\u03c0\u03b9\u03ba\u03cc\u03c2 \u03b3\u03b5\u03c9\u03b5\u03bd\u03c4\u03bf\u03c0\u03b9\u03c3\u03bc\u03cc\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1<\/a><\/li>\n<li><a href=\"#exi-stratigikes\">\u0388\u03be\u03b9 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03cc zero-shot \u03ad\u03c9\u03c2 full fine-tuning<\/a><\/li>\n<li><a href=\"#allagi-encoder\">\u03a4\u03bf \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03ac\u03bb\u03bc\u03b1 \u03c3\u03c5\u03bd\u03ad\u03b2\u03b7 \u03cc\u03c4\u03b1\u03bd \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03bf encoder<\/a><\/li>\n<li><a href=\"#interventions-symperifora\">\u03a4\u03b1 interventions \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03c5\u03bd \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac, \u03cc\u03c7\u03b9 \u00ab\u03c3\u03ba\u03ad\u03c8\u03b7\u00bb<\/a><\/li>\n<li><a href=\"#vlastisi-ouranos\">\u0392\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bf\u03c5\u03c1\u03b1\u03bd\u03cc\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03b5\u03c0\u03b9\u03b4\u03c1\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03c3\u03ae\u03bc\u03b1\u03c4\u03b1<\/a><\/li>\n<li><a href=\"#edges-blur\">Edges \u03ba\u03b1\u03b9 blur: \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b4\u03bf\u03bc\u03b9\u03ba\u03ae \u03b5\u03c0\u03ac\u03c1\u03ba\u03b5\u03b9\u03b1<\/a><\/li>\n<li><a href=\"#patch-scrambling\">\u03a4\u03bf patch scrambling \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7<\/a><\/li>\n<li><a href=\"#prompts\">\u03a4\u03b1 prompts \u03bc\u03b5\u03c4\u03c1\u03bf\u03cd\u03bd, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03bb\u03cd\u03bd\u03bf\u03c5\u03bd \u03cc\u03bb\u03bf \u03c4\u03bf \u03bc\u03c5\u03c3\u03c4\u03ae\u03c1\u03b9\u03bf<\/a><\/li>\n<li><a href=\"#train-test-overlap\">\u0397 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03c0\u03c1\u03bf\u03b5\u03b9\u03b4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf train\u2013test overlap<\/a><\/li>\n<li><a href=\"#epicheirimatiki-aksia\">\u03a4\u03b9 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03bf\u03c5\u03bd e-commerce \u03ba\u03b1\u03b9 marketing teams<\/a><\/li>\n<li><a href=\"#praktiko-plaisio\">\u0388\u03bd\u03b1 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae<\/a><\/li>\n<li><a href=\"#symperasma\">\u03a4\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf CLIP \u00ab\u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9\u00bb \u03c4\u03bf Los Angeles<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"topikos-geoentopismos\">\u0393\u03b9\u03b1\u03c4\u03af \u03bf \u03c4\u03bf\u03c0\u03b9\u03ba\u03cc\u03c2 \u03b3\u03b5\u03c9\u03b5\u03bd\u03c4\u03bf\u03c0\u03b9\u03c3\u03bc\u03cc\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1<\/h2>\n<p>\u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03cc\u03c1\u03b1\u03c3\u03b7\u03c2 \u03bd\u03b1 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03c3\u03b5\u03b9 \u03c4\u03bf Hollywood \u03b1\u03c0\u03cc \u03c4\u03bf Koreatown \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b2\u03b1\u03c3\u03b9\u03c3\u03c4\u03b5\u03af \u03c3\u03b5 \u03bc\u03b9\u03b1 \u03b4\u03b9\u03ac\u03c3\u03b7\u03bc\u03b7 \u03c0\u03b9\u03bd\u03b1\u03ba\u03af\u03b4\u03b1; \u0397 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c3\u03c4\u03b1 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03b1 \u03c4\u03b7\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1\u03c2. \u0392\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf\u03bd \u03c4\u03c1\u03cc\u03c0\u03bf \u03bc\u03b5 \u03c4\u03bf\u03bd \u03bf\u03c0\u03bf\u03af\u03bf \u03b4\u03c1\u03cc\u03bc\u03bf\u03b9, \u03ba\u03c4\u03af\u03c1\u03b9\u03b1, \u03b2\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7, \u03bf\u03c5\u03c1\u03b1\u03bd\u03cc\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ad\u03c2 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b5\u03c2 \u03c3\u03c5\u03bd\u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03c5\u03bd \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03b7 \u03c3\u03ba\u03b7\u03bd\u03ae.<\/p>\n<p>\u03a3\u03c4\u03bf\u03bd \u03b3\u03b5\u03c9\u03b5\u03bd\u03c4\u03bf\u03c0\u03b9\u03c3\u03bc\u03cc \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7\u03c2 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b1\u03c2, \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03b5\u03bc\u03c6\u03b1\u03bd\u03ae \u03c3\u03ae\u03bc\u03b1\u03c4\u03b1: \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03c3\u03b5 \u03c0\u03b9\u03bd\u03b1\u03ba\u03af\u03b4\u03b5\u03c2, \u03ba\u03bb\u03af\u03bc\u03b1, \u03bf\u03b4\u03b9\u03ba\u03ae \u03c3\u03ae\u03bc\u03b1\u03bd\u03c3\u03b7, \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03ae \u03b3\u03bd\u03c9\u03c3\u03c4\u03ac \u03c4\u03bf\u03c0\u03cc\u03c3\u03b7\u03bc\u03b1. \u038c\u03c4\u03b1\u03bd \u03cc\u03bc\u03c9\u03c2 \u03bf\u03b9 \u03c5\u03c0\u03bf\u03c8\u03ae\u03c6\u03b9\u03b5\u03c2 \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c3\u03af\u03b5\u03c2 \u03b2\u03c1\u03af\u03c3\u03ba\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03bc\u03b7\u03c4\u03c1\u03bf\u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae, \u03c0\u03bf\u03bb\u03bb\u03ac \u03b1\u03c0\u03cc \u03b1\u03c5\u03c4\u03ac \u03c4\u03b1 \u03c3\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03ac. \u0397 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03bb\u03b5\u03c0\u03c4\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5\u03c2 \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03bf\u03cd\u03c2 \u03bc\u03bf\u03c1\u03c6\u03bf\u03bb\u03bf\u03b3\u03af\u03b1\u03c2 \u03c4\u03bf\u03c5 \u03b4\u03c1\u03cc\u03bc\u03bf\u03c5, \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c4\u03b9\u03ba\u03ae\u03c2 \u03bf\u03c1\u03b3\u03ac\u03bd\u03c9\u03c3\u03b7\u03c2.<\/p>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u00abWhat Does CLIP Learn for Regional Geolocalization? Probing Visual Cues and Scene Configuration After Adaptation\u00bb \u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf regional geolocalization \u03c9\u03c2 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ad\u03c2 \u03bc\u03b9\u03b1\u03c2 \u03c0\u03cc\u03bb\u03b7\u03c2. \u039f\u03b9 \u03bf\u03ba\u03c4\u03ce \u03ba\u03bb\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ae\u03c4\u03b1\u03bd Downtown Los Angeles, Pasadena, Beverly Hills, Santa Monica, Hollywood, Long Beach, Venice Beach \u03ba\u03b1\u03b9 Koreatown. \u03a0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03bc\u03b5\u03af\u03b3\u03bc\u03b1 \u03b4\u03ae\u03bc\u03c9\u03bd, \u03b3\u03b5\u03b9\u03c4\u03bf\u03bd\u03b9\u03ce\u03bd \u03ba\u03b1\u03b9 districts, \u03cc\u03c7\u03b9 \u03b3\u03b9\u03b1 \u03c0\u03bb\u03ae\u03c1\u03b7 \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03ac\u03c6\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 Los Angeles. \u03a4\u03bf dataset \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf testbed \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03cc\u03c4\u03b9 \u03b7 \u03af\u03b4\u03b9\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03b8\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03b9\u03c3\u03c4\u03b5\u03af \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03c0\u03cc\u03bb\u03b7.<\/p>\n<p>\u039f\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03c0\u03c1\u03bf\u03ae\u03bb\u03b8\u03b1\u03bd \u03b1\u03c0\u03cc Google Maps API. \u0391\u03c0\u03cc 28.550 \u03b1\u03c1\u03c7\u03b9\u03ba\u03ad\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, 28.520 \u03b8\u03b5\u03c9\u03c1\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd \u03b5\u03c0\u03b9\u03bb\u03ad\u03be\u03b9\u03bc\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03ad\u03b3\u03b9\u03bd\u03b5 \u03b4\u03b5\u03b9\u03b3\u03bc\u03b1\u03c4\u03bf\u03bb\u03b7\u03c8\u03af\u03b1 \u03bc\u03b5 \u03ba\u03bf\u03b9\u03bd\u03ae \u03c7\u03c9\u03c1\u03b9\u03ba\u03ae \u03c0\u03c5\u03ba\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 194,49 \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd \u03b1\u03bd\u03ac \u03c4\u03b5\u03c4\u03c1\u03b1\u03b3\u03c9\u03bd\u03b9\u03ba\u03cc \u03c7\u03b9\u03bb\u03b9\u03cc\u03bc\u03b5\u03c4\u03c1\u03bf. \u03a4\u03bf \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b5\u03af\u03c7\u03b5 9.085 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, \u03bc\u03b5 stratified split 70\/15\/15: 6.359 \u03b3\u03b9\u03b1 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7, 1.363 \u03b3\u03b9\u03b1 validation \u03ba\u03b1\u03b9 1.363 \u03b3\u03b9\u03b1 test.<\/p>\n<h2 id=\"exi-stratigikes\">\u0388\u03be\u03b9 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03cc zero-shot \u03ad\u03c9\u03c2 full fine-tuning<\/h2>\n<p>\u038c\u03bb\u03b1 \u03c4\u03b1 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1 \u03b2\u03b1\u03c3\u03af\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03c3\u03c4\u03bf\u03bd pretrained OpenCLIP ViT-L-14. \u03a4\u03bf zero-shot \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c3\u03c5\u03bd\u03ad\u03ba\u03c1\u03b9\u03bd\u03b5 \u03c4\u03b7\u03bd \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03bc\u03b5 text prompts \u03c7\u03c9\u03c1\u03af\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c6\u03b5\u03c1\u03b5\u03b9\u03b1\u03ba\u03ae \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7. \u03a4\u03bf LP-T \u03bc\u03ac\u03b8\u03b1\u03b9\u03bd\u03b5 \u03bc\u03cc\u03bd\u03bf scale \u03ba\u03b1\u03b9 bias \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03b1 text scores, \u03b5\u03bd\u03ce \u03c4\u03bf LP-C \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03b5 \u03ad\u03bd\u03b1\u03bd \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03cc classifier \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03b1 visual features. \u039a\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03b4\u03cd\u03bf \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03bf image encoder \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03bd\u03b5 \u03b1\u03bc\u03b5\u03c4\u03ac\u03b2\u03bb\u03b7\u03c4\u03bf\u03c2.<\/p>\n<p>\u039f\u03b9 \u03c4\u03c1\u03b5\u03b9\u03c2 \u03c0\u03b9\u03bf \u03c0\u03b1\u03c1\u03b5\u03bc\u03b2\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2 \u03c4\u03c1\u03bf\u03c0\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03c3\u03b1\u03bd \u03c4\u03b7\u03bd \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7. \u03a4\u03bf Partial Update \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03bd\u03b5 \u03c4\u03bf \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03bf transformer block, \u03c4\u03bf <code>ln_post<\/code> \u03ba\u03b1\u03b9 \u03c4\u03bf classification head. \u03a4\u03bf LoRA \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03b5 rank-8 adapters \u03bc\u03b5 alpha 16 \u03ba\u03b1\u03b9 dropout 0,1 \u03c3\u03c4\u03b1 attention \u03ba\u03b1\u03b9 MLP projections, \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03ce\u03bd\u03c4\u03b1\u03c2 \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03b1 \u03c4\u03b1 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ac weights. \u03a4\u03bf Full-FT \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03bd\u03b5 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf\u03bd image encoder \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 \u03c4\u03bf\u03bd classifier.<\/p>\n<div class=\"td-comparison td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid--three\">\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">Frozen readout \u00b7 \u03a7\u03b1\u03bc\u03b7\u03bb\u03ae \u03c0\u03b1\u03c1\u03ad\u03bc\u03b2\u03b1\u03c3\u03b7<\/p>\n<p>\u03a4\u03bf LP-T \u03ba\u03b1\u03b9 \u03c4\u03bf LP-C \u03b1\u03bb\u03bb\u03ac\u03b6\u03bf\u03c5\u03bd \u03bc\u03cc\u03bd\u03bf \u03c4\u03b7\u03bd \u03ad\u03be\u03bf\u03b4\u03bf \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03b1 features. \u0395\u03af\u03bd\u03b1\u03b9 \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf baseline, \u03b1\u03bb\u03bb\u03ac \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf task \u03ad\u03bc\u03b5\u03b9\u03bd\u03b1\u03bd \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03c4\u03bf zero-shot.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">39%\u201341%<\/span><span class=\"td-badge\">Frozen encoder<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<p class=\"td-comparison-title\">LoRA \u00b7 \u0395\u03c0\u03b9\u03bb\u03b5\u03ba\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae<\/p>\n<p>Rank-8 adapters \u03c3\u03c4\u03b1 attention \u03ba\u03b1\u03b9 MLP projections \u03ac\u03bb\u03bb\u03b1\u03be\u03b1\u03bd \u03c4\u03bf\u03bd \u03c4\u03c1\u03cc\u03c0\u03bf \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03c4\u03c9\u03bd features \u03c7\u03c9\u03c1\u03af\u03c2 \u03c0\u03bb\u03ae\u03c1\u03b7 \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03c3\u03b7 \u03c4\u03bf\u03c5 encoder.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">78,36%<\/span><span class=\"td-badge\">Parameter-efficient<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">Full-FT \u00b7 \u039c\u03ad\u03b3\u03b9\u03c3\u03c4\u03b7 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae<\/p>\n<p>\u0397 \u03c0\u03bb\u03ae\u03c1\u03b7\u03c2 \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03c3\u03b7 \u03c4\u03bf\u03c5 image encoder \u03ad\u03b4\u03c9\u03c3\u03b5 \u03c4\u03b7\u03bd \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 accuracy \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf centroid error, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c4\u03bf \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 deployment.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">82,10%<\/span><span class=\"td-badge\">3,86 km<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0391\u03c5\u03c4\u03ae \u03b7 \u03ba\u03bb\u03b9\u03bc\u03ac\u03ba\u03c9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b3\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd AI \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03b1. \u0394\u03b5\u03bd \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03ad\u03be\u03b9 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03b1\u03bb\u03bb\u03ac \u03ad\u03be\u03b9 \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03b1 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7\u03c2 \u03c3\u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03c0\u03c1\u03bf\u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03bc\u03ad\u03bd\u03b7 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7. \u03a4\u03bf \u03b5\u03c1\u03ce\u03c4\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd \u03bc\u03b9\u03b1 \u03bd\u03ad\u03b1 \u03ad\u03be\u03bf\u03b4\u03bf\u03c2 \u03b1\u03c1\u03ba\u03b5\u03af \u03ae \u03b1\u03bd \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c4\u03bf feature space \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 \u03c0\u03c1\u03bf\u03c3\u03b2\u03ac\u03c3\u03b9\u03bc\u03b7 \u03b7 \u03bb\u03b5\u03c0\u03c4\u03ae \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1. \u03a0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b1 \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03c5\u03bc\u03b5 <a href=\"https:\/\/twodots.gr\/geographic-domain-shift-ai-modelo-se-nees-agores\/\">geographic domain shift \u03c3\u03b5 \u03bd\u03ad\u03b5\u03c2 \u03b1\u03b3\u03bf\u03c1\u03ad\u03c2<\/a>: \u03c4\u03bf \u03af\u03b4\u03b9\u03bf model checkpoint \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03ac\u03b6\u03bf\u03c5\u03bd \u03bf\u03b9 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2.<\/p>\n<h2 id=\"allagi-encoder\">\u03a4\u03bf \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03ac\u03bb\u03bc\u03b1 \u03c3\u03c5\u03bd\u03ad\u03b2\u03b7 \u03cc\u03c4\u03b1\u03bd \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03bf encoder<\/h2>\n<p>\u03a4\u03bf zero-shot CLIP \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 39,03% accuracy \u03ba\u03b1\u03b9 \u03bc\u03ad\u03c3\u03bf centroid error 12,30 \u03c7\u03b9\u03bb\u03b9\u03cc\u03bc\u03b5\u03c4\u03c1\u03b1. \u03a4\u03b1 \u03b4\u03cd\u03bf frozen readouts \u03b4\u03b5\u03bd \u03ac\u03bb\u03bb\u03b1\u03be\u03b1\u03bd \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03c4\u03b7\u03bd \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1: \u03c4\u03bf LP-T \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 41,45% \u03ba\u03b1\u03b9 \u03c4\u03bf LP-C 38,52%. \u0391\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1, \u03c4\u03bf Partial Update \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5 \u03c3\u03c4\u03bf 75,94%, \u03c4\u03bf LoRA \u03c3\u03c4\u03bf 78,36% \u03ba\u03b1\u03b9 \u03c4\u03bf Full-FT \u03c3\u03c4\u03bf 82,10%. \u03a4\u03bf Full-FT \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03c4\u03bf centroid error \u03c3\u03c4\u03b1 3,86 \u03c7\u03b9\u03bb\u03b9\u03cc\u03bc\u03b5\u03c4\u03c1\u03b1.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-title\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b9\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03c0\u03bf\u03c5 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03b1\u03c3\u03c4\u03bf\u03cd\u03bd \u03bc\u03b1\u03b6\u03af<\/p>\n<p class=\"td-chart-subtitle\">\u0391\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c4\u03bf\u03c5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5 Greater Los Angeles testbed\u00b7 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc benchmark \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03c0\u03cc\u03bb\u03b7 \u03ae \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03cc \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1.<\/p>\n<\/div>\n<div class=\"td-chart-body\">\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">39,03%<\/span><span class=\"td-metric-label\">zero-shot accuracy \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c0\u03b5\u03c1\u03b9\u03c6\u03b5\u03c1\u03b5\u03b9\u03b1\u03ba\u03ae \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">82,10%<\/span><span class=\"td-metric-label\">accuracy \u03bc\u03b5 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 fine-tuning \u03c4\u03bf\u03c5 image encoder<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">3,86 km<\/span><span class=\"td-metric-label\">\u03bc\u03ad\u03c3\u03bf centroid error \u03c4\u03bf\u03c5 Full-FT \u03c3\u03c4\u03bf test set<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">79,68%<\/span><span class=\"td-metric-label\">test \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03ad\u03c9\u03c2 50 \u03bc\u03ad\u03c4\u03c1\u03b1 \u03b1\u03c0\u03cc coordinate \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0397 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae\u03c2 \u03b4\u03b9\u03b1\u03c4\u03cd\u03c0\u03c9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9, \u03bc\u03b5 \u03c4\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 probes \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf split, \u03b7 \u03c0\u03b5\u03c1\u03b9\u03c6\u03b5\u03c1\u03b5\u03b9\u03b1\u03ba\u03ae \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03ad\u03b3\u03b9\u03bd\u03b5 \u03c0\u03bf\u03bb\u03cd \u03c0\u03b9\u03bf \u03c0\u03c1\u03bf\u03c3\u03b2\u03ac\u03c3\u03b9\u03bc\u03b7 \u03cc\u03c4\u03b1\u03bd \u03b5\u03c0\u03b9\u03c4\u03c1\u03ac\u03c0\u03b7\u03ba\u03b5 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03c3\u03c4\u03b5\u03af \u03bf encoder. \u0397 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03b5\u03bd\u03cc\u03c2 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03bf\u03cd probe \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03bf CLIP \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03ad\u03c7\u03b5\u03b9 \u03ba\u03b1\u03b8\u03cc\u03bb\u03bf\u03c5 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1. \u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03b4\u03b5\u03bd \u03b1\u03bd\u03b1\u03ba\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b5\u03cd\u03ba\u03bf\u03bb\u03b1 \u03bc\u03b5 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf readout.<\/p>\n<aside class=\"td-article-note\"><strong>Probe failure \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 feature absence:<\/strong> \u03c4\u03bf LP-T \u03ba\u03b1\u03b9 \u03c4\u03bf LP-C \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5\u03bd \u03b4\u03cd\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5\u03c2 \u03c4\u03c1\u03cc\u03c0\u03bf\u03c5\u03c2 \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7\u03c2 \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03c9\u03bd features. \u0397 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf\u03c5\u03c2 \u03b1\u03c0\u03cc \u03c4\u03b1 adapted models \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03c0\u03b5\u03c1\u03b9\u03c6\u03b5\u03c1\u03b5\u03b9\u03b1\u03ba\u03cc \u03c3\u03ae\u03bc\u03b1 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc \u03c4\u03bf \u03bc\u03b7\u03b4\u03ad\u03bd.<\/aside>\n<p>\u03a4\u03b1 \u03ba\u03ad\u03c1\u03b4\u03b7 \u03b4\u03b5\u03bd \u03ae\u03c4\u03b1\u03bd \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03b1. \u03a4\u03bf Full-FT \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b5 \u03c4\u03bf Hollywood \u03ba\u03b1\u03c4\u03ac 60,56 \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03b9\u03b1\u03af\u03b5\u03c2 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03bf Downtown LA \u03ba\u03b1\u03c4\u03ac 55,03, \u03b1\u03bb\u03bb\u03ac \u03c4\u03b7 Venice Beach \u03bc\u03cc\u03bd\u03bf \u03ba\u03b1\u03c4\u03ac 6,67 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b5\u03c2. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c0\u03b9\u03b8\u03b1\u03bd\u03ad\u03c2 \u03b5\u03be\u03b7\u03b3\u03ae\u03c3\u03b5\u03b9\u03c2, \u03cc\u03c0\u03c9\u03c2 landmarks, class-name alignment \u03ba\u03b1\u03b9 sampling density, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b9\u03c3\u03c7\u03c5\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b9\u03c2 \u03b1\u03c0\u03bf\u03bc\u03cc\u03bd\u03c9\u03c3\u03b5 \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac.<\/p>\n<p>\u03a4\u03bf LoRA \u03b2\u03c1\u03ad\u03b8\u03b7\u03ba\u03b5 \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03c4\u03bf Full-FT, \u03b1\u03bb\u03bb\u03ac \u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac 3,74 \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03b9\u03b1\u03af\u03c9\u03bd \u03bc\u03bf\u03bd\u03ac\u03b4\u03c9\u03bd \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03b3\u03b9\u03b1 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03ae \u03c3\u03cd\u03c3\u03c4\u03b1\u03c3\u03b7. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b4\u03b5\u03bd \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc training cost, inference latency \u03ae \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c3\u03c5\u03bd\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7\u03c2. \u03a4\u03bf \u03ce\u03c1\u03b9\u03bc\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03bb\u03b7\u03c6\u03b8\u03b5\u03af \u03c4\u03bf LoRA \u03c9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc baseline, \u03cc\u03c0\u03c9\u03c2 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03ac\u03bb\u03bb\u03b7 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae <a href=\"https:\/\/twodots.gr\/hate-speech-roman-urdu-lora-prompting\/\">LoRA \u03c3\u03b5 task \u03bc\u03b5 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1<\/a>, \u03cc\u03c7\u03b9 \u03bd\u03b1 \u03b8\u03b5\u03c9\u03c1\u03b7\u03b8\u03b5\u03af \u03b5\u03ba \u03c4\u03c9\u03bd \u03c0\u03c1\u03bf\u03c4\u03ad\u03c1\u03c9\u03bd \u03bd\u03b9\u03ba\u03b7\u03c4\u03ae\u03c2.<\/p>\n<h2 id=\"interventions-symperifora\">\u03a4\u03b1 interventions \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03c5\u03bd \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac, \u03cc\u03c7\u03b9 \u00ab\u03c3\u03ba\u03ad\u03c8\u03b7\u00bb<\/h2>\n<p>\u0393\u03b9\u03b1 \u03bd\u03b1 \u03b4\u03b9\u03b5\u03c1\u03b5\u03c5\u03bd\u03ae\u03c3\u03bf\u03c5\u03bd \u03c4\u03b9 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b5\u03c4\u03ac \u03c4\u03b7\u03bd \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae, \u03bf\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b5\u03c6\u03ac\u03c1\u03bc\u03bf\u03c3\u03b1\u03bd \u03c4\u03c1\u03b5\u03b9\u03c2 \u03bf\u03b9\u03ba\u03bf\u03b3\u03ad\u03bd\u03b5\u03b9\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b5\u03bc\u03b2\u03ac\u03c3\u03b5\u03c9\u03bd. \u0391\u03c6\u03b1\u03af\u03c1\u03b5\u03c3\u03b1\u03bd \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03bc\u03ad\u03bd\u03b1 semantic cues \u2014\u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf, \u03bf\u03c7\u03ae\u03bc\u03b1\u03c4\u03b1, \u03b2\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bf\u03c5\u03c1\u03b1\u03bd\u03cc\u2014 \u03bc\u03b5 detection \u03ae segmentation \u03ba\u03b1\u03b9 inpainting. \u039c\u03b5 edge maps \u03ba\u03b1\u03b9 \u03ad\u03bd\u03c4\u03bf\u03bd\u03bf blur \u03bc\u03b5\u03af\u03c9\u03c3\u03b1\u03bd \u03c4\u03b7 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03b5\u03c1\u03ae \u03b5\u03bc\u03c6\u03ac\u03bd\u03b9\u03c3\u03b7, \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03ce\u03bd\u03c4\u03b1\u03c2 \u03c4\u03bc\u03ae\u03bc\u03b1 \u03c4\u03b7\u03c2 \u03c7\u03bf\u03bd\u03b4\u03c1\u03bf\u03b5\u03b9\u03b4\u03bf\u03cd\u03c2 \u03bf\u03c1\u03b3\u03ac\u03bd\u03c9\u03c3\u03b7\u03c2. \u03a4\u03ad\u03bb\u03bf\u03c2, \u03b1\u03bd\u03b1\u03ba\u03ac\u03c4\u03b5\u03c8\u03b1\u03bd patches \u03ce\u03c3\u03c4\u03b5 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03ae\u03c3\u03bf\u03c5\u03bd \u03c4\u03bf\u03c0\u03b9\u03ba\u03cc \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03b1\u03bb\u03bb\u03ac \u03bd\u03b1 \u03b4\u03b9\u03b1\u03c4\u03b1\u03c1\u03ac\u03be\u03bf\u03c5\u03bd \u03c4\u03b7 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ae \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7.<\/p>\n<p>\u039f\u03b9 \u03c0\u03b1\u03c1\u03b5\u03bc\u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03c0\u03b1\u03c1\u03ac\u03b8\u03c5\u03c1\u03bf \u03c3\u03b5 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae. \u039c\u03b5\u03c4\u03c1\u03bf\u03cd\u03bd \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03c3\u03c4\u03b7\u03bd \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1, \u03c3\u03c4\u03bf retention \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf prediction switch rate. \u03a4\u03bf retention \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf\u03bd \u03bc\u03b5\u03c4\u03b1\u03c3\u03c7\u03b7\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03cc \u03b4\u03b9\u03b1\u03b9\u03c1\u03b5\u03bc\u03ad\u03bd\u03b7 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03b9\u03ba\u03ae \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1. \u03a4\u03bf switch rate \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd \u03c3\u03c4\u03b9\u03c2 \u03bf\u03c0\u03bf\u03af\u03b5\u03c2 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03b7 \u03c0\u03c1\u03bf\u03b2\u03bb\u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b1. \u0391\u03c5\u03c4\u03ad\u03c2 \u03bf\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03bf\u03c5\u03bd \u03b5\u03c5\u03b1\u03b9\u03c3\u03b8\u03b7\u03c3\u03af\u03b1 \u03c4\u03b7\u03c2 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac\u03c2, \u03cc\u03c7\u03b9 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03c7\u03c9\u03c1\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03b1\u03bd\u03cc\u03b7\u03c3\u03b7.<\/p>\n<p>\u0397 \u03b1\u03be\u03af\u03b1 \u03c4\u03bf\u03c5\u03c2 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf\u03bd \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc. Cue removal, matched random masks, edges, blur, patch scrambling \u03ba\u03b1\u03b9 control \u03c3\u03b5 \u03ac\u03bb\u03bb\u03bf dataset \u03b1\u03c0\u03b1\u03bd\u03c4\u03bf\u03cd\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c1\u03c9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2. \u0397 \u03c0\u03c1\u03bf\u03c3\u03ad\u03b3\u03b3\u03b9\u03c3\u03b7 \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b5 \u03c4\u03b7 \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c1\u03c7\u03ae \u03cc\u03c4\u03b9 \u03b7 <a href=\"https:\/\/twodots.gr\/flavourbench-axiopisti-axiologisi-ai\/\">\u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 AI<\/a> \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ad\u03c2 \u03cc\u03c8\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5 \u03af\u03b4\u03b9\u03bf\u03c5 failure mode \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03ad\u03bd\u03b1 \u03bc\u03cc\u03bd\u03bf aggregate score.<\/p>\n<h2 id=\"vlastisi-ouranos\">\u0392\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bf\u03c5\u03c1\u03b1\u03bd\u03cc\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03b5\u03c0\u03b9\u03b4\u03c1\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03c3\u03ae\u03bc\u03b1\u03c4\u03b1<\/h2>\n<p>\u0397 \u03b1\u03c6\u03b1\u03af\u03c1\u03b5\u03c3\u03b7 \u03b2\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7\u03c2 \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03c4\u03bf 7,12% \u03c4\u03c9\u03bd zero-shot \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03c9\u03bd, \u03c4\u03bf 7,92% \u03c4\u03bf\u03c5 LoRA \u03ba\u03b1\u03b9 \u03c4\u03bf 7,63% \u03c4\u03bf\u03c5 Full-FT. \u0393\u03b9\u03b1 \u03c4\u03bf\u03bd \u03bf\u03c5\u03c1\u03b1\u03bd\u03cc, \u03c4\u03b1 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1 switch rates \u03ae\u03c4\u03b1\u03bd 4,40%, 5,65% \u03ba\u03b1\u03b9 4,70%. \u0397 \u03b1\u03c6\u03b1\u03af\u03c1\u03b5\u03c3\u03b7 \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03bf\u03c7\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03b5\u03c0\u03b7\u03c1\u03ad\u03b1\u03c3\u03b5 \u03c3\u03b1\u03c6\u03ce\u03c2 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03b9\u03c2.<\/p>\n<p>\u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03ac\u03bd\u03bf\u03c5\u03bc\u03b5 \u03cc\u03c4\u03b9 \u03b7 \u03b2\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03b9\u03c4\u03b9\u03c9\u03b4\u03ce\u03c2 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf. \u039f\u03b9 \u03bc\u03ac\u03c3\u03ba\u03b5\u03c2 \u03ad\u03c7\u03bf\u03c5\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2, \u03b5\u03bd\u03ce \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 detection, segmentation \u03ba\u03b1\u03b9 inpainting \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03af\u03b4\u03b9\u03b1 \u03b3\u03b9\u03b1 \u03cc\u03bb\u03b1 \u03c4\u03b1 cues. \u0395\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd, \u03b7 \u03b2\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bf \u03bf\u03c5\u03c1\u03b1\u03bd\u03cc\u03c2 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03c3\u03c5\u03c3\u03c7\u03b5\u03c4\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c0\u03b1\u03c1\u03ac\u03ba\u03c4\u03b9\u03b1 \u03b5\u03b3\u03b3\u03cd\u03c4\u03b7\u03c4\u03b1, \u03c0\u03c5\u03ba\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b4\u03cc\u03bc\u03b7\u03c3\u03b7\u03c2 \u03ae \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03cc \u03b4\u03c1\u03cc\u03bc\u03c9\u03bd, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bc\u03ac\u03c4\u03c9\u03c2 spurious features.<\/p>\n<p>\u03a4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03b5\u03cd\u03c1\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b5\u03c0\u03b9\u03bc\u03bf\u03bd\u03ae \u03c4\u03b7\u03c2 \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7\u03c2 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf adaptation. \u0397 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b5\u03c5\u03b1\u03b9\u03c3\u03b8\u03b7\u03c3\u03af\u03b1 \u03c3\u03c4\u03b7 \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7 \u03c4\u03b7\u03c2 \u03c3\u03ba\u03b7\u03bd\u03ae\u03c2 \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac \u03c4\u03b1 appearance cues. \u03a3\u03c5\u03bd\u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03bc\u03b1\u03b6\u03af \u03c4\u03bf\u03c5\u03c2. \u03a3\u03b5 \u03ad\u03bd\u03b1 e-commerce vision system, \u03c4\u03bf \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf \u03c3\u03ae\u03bc\u03b1 \u03b8\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03c3\u03b5 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf studio background, \u03b7 \u03b5\u03c0\u03bf\u03c7\u03ae \u03c4\u03bf\u03c5 catalog \u03ae \u03b7 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03b1\u03c3\u03af\u03b1\u00b7 \u03b7 \u03c3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03b1 \u03ac\u03c7\u03c1\u03b7\u03c3\u03c4\u03b7, \u03b1\u03bb\u03bb\u03ac \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ae \u03ba\u03b1\u03b9 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7.<\/p>\n<h2 id=\"edges-blur\">Edges \u03ba\u03b1\u03b9 blur: \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b4\u03bf\u03bc\u03b9\u03ba\u03ae \u03b5\u03c0\u03ac\u03c1\u03ba\u03b5\u03b9\u03b1<\/h2>\n<p>\u03a3\u03c4\u03b9\u03c2 edge-transformed \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, \u03c4\u03bf zero-shot \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b5\u03af\u03c7\u03b5 21,5% accuracy, \u03c4\u03bf LoRA 32,1% \u03ba\u03b1\u03b9 \u03c4\u03bf Full-FT 38,3%. \u03a3\u03c4\u03bf macro blur \u03bf\u03b9 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03ae\u03c4\u03b1\u03bd 22,1%, 42,0% \u03ba\u03b1\u03b9 45,2%. \u03a6\u03b1\u03b9\u03bd\u03bf\u03bc\u03b5\u03bd\u03b9\u03ba\u03ac, \u03c4\u03b1 adapted models \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03c4\u03b7\u03bd \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03b2\u03b9\u03ce\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03b5\u03bc\u03c6\u03ac\u03bd\u03b9\u03c3\u03b7.<\/p>\n<p>\u038c\u03bc\u03c9\u03c2 \u03c4\u03bf retention \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1. \u03a3\u03c4\u03b1 edges, \u03c4\u03bf zero-shot \u03b4\u03b9\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b5 \u03c4\u03bf 0,55 \u03c4\u03b7\u03c2 \u03b1\u03c1\u03c7\u03b9\u03ba\u03ae\u03c2 \u03c4\u03bf\u03c5 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7\u03c2, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 0,41 \u03b3\u03b9\u03b1 LoRA \u03ba\u03b1\u03b9 0,47 \u03b3\u03b9\u03b1 Full-FT. \u03a3\u03c4\u03bf blur \u03bf\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03ae\u03c4\u03b1\u03bd 0,57, 0,54 \u03ba\u03b1\u03b9 0,55. \u0386\u03c1\u03b1 \u03b7 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03b1\u03cd\u03be\u03b7\u03c3\u03b5 \u03c4\u03b7\u03bd \u03b1\u03c0\u03cc\u03bb\u03c5\u03c4\u03b7 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03b1 \u03b1\u03bb\u03bb\u03bf\u03b9\u03c9\u03bc\u03ad\u03bd\u03b1 inputs, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b1\u03cd\u03be\u03b7\u03c3\u03b5 \u03c4\u03bf \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc \u03c4\u03b7\u03c2 \u03b1\u03c1\u03c7\u03b9\u03ba\u03ae\u03c2 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7\u03c2 \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03b2\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03b5\u03c1\u03ae \u03b5\u03bc\u03c6\u03ac\u03bd\u03b9\u03c3\u03b7.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03ba\u03b5\u03bd\u03c4\u03c1\u03b9\u03ba\u03cc \u03b5\u03bd\u03bd\u03bf\u03b9\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03cc \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2: \u03ac\u03bb\u03bb\u03bf \u03b7 <strong>\u03b5\u03c5\u03b1\u03b9\u03c3\u03b8\u03b7\u03c3\u03af\u03b1 \u03c3\u03c4\u03b7 \u03b4\u03bf\u03bc\u03ae<\/strong> \u03ba\u03b1\u03b9 \u03ac\u03bb\u03bb\u03bf \u03b7 <strong>\u03b5\u03c0\u03ac\u03c1\u03ba\u03b5\u03b9\u03b1 \u03c4\u03b7\u03c2 \u03b4\u03bf\u03bc\u03ae\u03c2<\/strong>. \u0388\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c4\u03b7\u03bd \u03ac\u03b8\u03b9\u03ba\u03c4\u03b7 \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf, \u03c7\u03c9\u03c1\u03af\u03c2 \u03b7 \u03c7\u03bf\u03bd\u03b4\u03c1\u03bf\u03b5\u03b9\u03b4\u03ae\u03c2 \u03b4\u03bf\u03bc\u03ae \u03b1\u03c0\u03cc \u03bc\u03cc\u03bd\u03b7 \u03c4\u03b7\u03c2 \u03bd\u03b1 \u03b1\u03c1\u03ba\u03b5\u03af \u03b3\u03b9\u03b1 \u03c3\u03c9\u03c3\u03c4\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7.<\/p>\n<h2 id=\"patch-scrambling\">\u03a4\u03bf patch scrambling \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7<\/h2>\n<p>\u038c\u03c4\u03b1\u03bd \u03c4\u03b1 patches \u03bc\u03b5\u03b3\u03ad\u03b8\u03bf\u03c5\u03c2 16 \u03b1\u03bd\u03b1\u03ba\u03b1\u03c4\u03b5\u03cd\u03c4\u03b7\u03ba\u03b1\u03bd, \u03c4\u03b1 frozen \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ac\u03bb\u03bb\u03b1\u03be\u03b1\u03bd \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03c3\u03c4\u03bf 10,79%\u201314,60% \u03c4\u03c9\u03bd \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03c9\u03bd. \u03a4\u03b1 adapted models \u03ac\u03bb\u03bb\u03b1\u03be\u03b1\u03bd \u03c3\u03c4\u03bf 42,92%\u201345,56%. \u0397 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03b3\u03b9\u03b1 patches 32 \u03ba\u03b1\u03b9 64, \u03b1\u03bd \u03ba\u03b1\u03b9 \u03b7 \u03b5\u03c5\u03b1\u03b9\u03c3\u03b8\u03b7\u03c3\u03af\u03b1 \u03bc\u03b5\u03b9\u03ce\u03b8\u03b7\u03ba\u03b5 \u03cc\u03c3\u03bf \u03c4\u03b1 patches \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03bf\u03cd\u03c3\u03b1\u03bd \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03b9\u03bf \u03c3\u03c5\u03bd\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03c9\u03bd \u03ba\u03b1\u03b9 \u03c3\u03ba\u03b7\u03bd\u03ae\u03c2.<\/p>\n<p>\u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03bc\u03b5\u03c4\u03ac \u03c4\u03b7\u03bd \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03bf\u03b9 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03b9\u03c2 \u03b5\u03be\u03b1\u03c1\u03c4\u03ce\u03bd\u03c4\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03ac\u03b8\u03b9\u03ba\u03c4\u03b7 \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7. \u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03bc\u03c9\u03c2 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b1\u03c0\u03ad\u03ba\u03c4\u03b7\u03c3\u03b5 semantic spatial reasoning \u03ae \u03c4\u03c1\u03b9\u03c3\u03b4\u03b9\u03ac\u03c3\u03c4\u03b1\u03c4\u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03cc\u03b7\u03c3\u03b7. \u03a4\u03bf scrambling \u03b5\u03b9\u03c3\u03ac\u03b3\u03b5\u03b9 \u03bc\u03b7 \u03c6\u03c5\u03c3\u03b9\u03ba\u03ac artifacts \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03c0\u03bf\u03bb\u03bb\u03ac \u03b5\u03af\u03b4\u03b7 visual recognition.<\/p>\n<p>\u0393\u03b9\u03b1 \u03b1\u03c5\u03c4\u03cc \u03bf\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c3\u03b1\u03bd control \u03c3\u03c4\u03bf Caltech101. \u0397 zero-shot \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 89,65% \u03ad\u03c0\u03b5\u03c3\u03b5 \u03c3\u03c4\u03bf 33,90% \u03bc\u03b5 Patch-16, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ae \u03c0\u03c4\u03ce\u03c3\u03b7 62,19%. \u0386\u03c1\u03b1 \u03c4\u03bf scrambling \u03b4\u03b9\u03b1\u03c4\u03b1\u03c1\u03ac\u03c3\u03c3\u03b5\u03b9 \u03ad\u03bd\u03c4\u03bf\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03b1\u03bd\u03b1\u03b3\u03bd\u03ce\u03c1\u03b9\u03c3\u03b7 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03c9\u03bd. \u03a4\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf evidence \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac frozen\u2013adapted \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf geolocation task, \u03cc\u03c7\u03b9 \u03b7 \u03b9\u03b4\u03ad\u03b1 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03c6\u03b1\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03b3\u03b5\u03c9\u03b3\u03c1\u03b1\u03c6\u03b9\u03ba\u03cc.<\/p>\n<h2 id=\"prompts\">\u03a4\u03b1 prompts \u03bc\u03b5\u03c4\u03c1\u03bf\u03cd\u03bd, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03bb\u03cd\u03bd\u03bf\u03c5\u03bd \u03cc\u03bb\u03bf \u03c4\u03bf \u03bc\u03c5\u03c3\u03c4\u03ae\u03c1\u03b9\u03bf<\/h2>\n<p>\u03a3\u03c4\u03bf zero-shot setting, \u03c4\u03bf \u03b1\u03c1\u03c7\u03b9\u03ba\u03cc prompt \u03ad\u03b4\u03c9\u03c3\u03b5 39,03%, \u03ad\u03bd\u03b1 length-controlled prompt 38,30% \u03ba\u03b1\u03b9 visual-only descriptors 36,24%. \u038c\u03c4\u03b1\u03bd \u03bf\u03b9 descriptors \u03b1\u03bd\u03c4\u03b1\u03bb\u03bb\u03ac\u03c7\u03b8\u03b7\u03ba\u03b1\u03bd \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b9\u03c2 \u03ba\u03bb\u03ac\u03c3\u03b5\u03b9\u03c2, \u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03ad\u03c0\u03b5\u03c3\u03b5 \u03c3\u03c4\u03bf 11,15%, \u03ba\u03ac\u03c4\u03c9 \u03b1\u03c0\u03cc \u03c4\u03bf chance level 12,5% \u03b3\u03b9\u03b1 \u03bf\u03ba\u03c4\u03ce \u03ba\u03bb\u03ac\u03c3\u03b5\u03b9\u03c2.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 class-specific \u03b4\u03b9\u03b1\u03c4\u03cd\u03c0\u03c9\u03c3\u03b7 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf \u03bc\u03ae\u03ba\u03bf\u03c2 \u03c4\u03bf\u03c5 prompt. \u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 descriptor \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ac \u03b8\u03b5\u03bc\u03b5\u03bb\u03b9\u03c9\u03bc\u03ad\u03bd\u03bf \u03bc\u03b5 \u03c4\u03bf\u03bd \u03c4\u03c1\u03cc\u03c0\u03bf \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03c4\u03bf \u03b5\u03c1\u03bc\u03ae\u03bd\u03b5\u03c5\u03b5 \u03ad\u03bd\u03b1\u03c2 \u03ac\u03bd\u03b8\u03c1\u03c9\u03c0\u03bf\u03c2, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b7 \u03b1\u03bd\u03c4\u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03b1 \u03c4\u03bf \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03af\u03c7\u03b9\u03c3\u03b7 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b1. \u0395\u03c0\u03af\u03c3\u03b7\u03c2 \u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf\u03c2 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 \u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf zero-shot CLIP \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03c4\u03bf\u03bd \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03c3\u03bc\u03ad\u03bd\u03bf image encoder.<\/p>\n<p>\u0393\u03b9\u03b1 \u03ad\u03bd\u03b1 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1, \u03b7 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7. \u03a4\u03bf \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 <a href=\"https:\/\/twodots.gr\/multimodal-ai-agents-antilipsi-drasi\/\">multimodal AI \u03b1\u03bd\u03c4\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bf\u03c0\u03c4\u03b9\u03ba\u03cc context<\/a> \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c9\u03c3\u03c4\u03ae \u03ae \u03cc\u03c4\u03b9 \u03b7 \u03b5\u03be\u03ae\u03b3\u03b7\u03c3\u03ae \u03c4\u03bf\u03c5 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03c0\u03b9\u03c3\u03c4\u03ac \u03c4\u03bf \u03c3\u03ae\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5.<\/p>\n<h2 id=\"train-test-overlap\">\u0397 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03c0\u03c1\u03bf\u03b5\u03b9\u03b4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf train\u2013test overlap<\/h2>\n<p>\u0391\u03c0\u03cc \u03c4\u03b9\u03c2 1.363 test \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, \u03bf\u03b9 755 \u201455,39%\u2014 \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03bd \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03c4\u03bf requested coordinate \u03bc\u03b5 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2. \u0395\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd, 1.086 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u201479,68%\u2014 \u03b2\u03c1\u03af\u03c3\u03ba\u03bf\u03bd\u03c4\u03b1\u03bd \u03c3\u03b5 \u03b1\u03c0\u03cc\u03c3\u03c4\u03b1\u03c3\u03b7 \u03ad\u03c9\u03c2 50 \u03bc\u03ad\u03c4\u03c1\u03c9\u03bd \u03b1\u03c0\u03cc coordinate \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2. \u039c\u03cc\u03bd\u03bf 277, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae 20,32%, \u03b1\u03c0\u03b5\u03af\u03c7\u03b1\u03bd \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf.<\/p>\n<aside class=\"td-article-note\"><strong>\u03a4\u03bf \u03c5\u03c8\u03b7\u03bb\u03cc score \u03b1\u03c6\u03bf\u03c1\u03ac \u03ba\u03bf\u03bd\u03c4\u03b9\u03bd\u03ad\u03c2, \u03cc\u03c7\u03b9 \u03b3\u03b5\u03c9\u03b3\u03c1\u03b1\u03c6\u03b9\u03ba\u03ac \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b5\u03c2 \u03bb\u03ae\u03c8\u03b5\u03b9\u03c2:<\/strong> \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc heading \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03ac\u03bb\u03bb\u03b7 \u03c3\u03ba\u03b7\u03bd\u03ae, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03ba\u03c4\u03af\u03c1\u03b9\u03b1, \u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03af\u03b1 \u03b4\u03b9\u03b1\u03c3\u03c4\u03b1\u03cd\u03c1\u03c9\u03c3\u03b7\u03c2, \u03b5\u03c0\u03b9\u03c6\u03ac\u03bd\u03b5\u03b9\u03b5\u03c2 \u03b4\u03c1\u03cc\u03bc\u03bf\u03c5, \u03b2\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 \u03bb\u03ae\u03c8\u03b7\u03c2 \u03c4\u03b7\u03c2 \u03af\u03b4\u03b9\u03b1\u03c2 \u03bc\u03b9\u03ba\u03c1\u03bf-\u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c3\u03af\u03b1\u03c2.<\/aside>\n<p>\u039f\u03b9 \u03bb\u03ae\u03c8\u03b5\u03b9\u03c2 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac headings \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03ac\u03bb\u03bb\u03bf \u03c0\u03b5\u03b4\u03af\u03bf: \u03ad\u03bd\u03b1\u03bd \u03bf\u03b9\u03ba\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc \u03b4\u03c1\u03cc\u03bc\u03bf \u03b1\u03bd\u03c4\u03af \u03b3\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03bc\u03c0\u03bf\u03c1\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c3\u03c4\u03b1\u03cd\u03c1\u03c9\u03c3\u03b7. \u03a0\u03b1\u03c1\u03cc\u03bb\u03b1 \u03b1\u03c5\u03c4\u03ac, \u03b5\u03bd\u03b4\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ba\u03c4\u03af\u03c1\u03b9\u03b1, \u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03af\u03b1 \u03b4\u03b9\u03b1\u03c3\u03c4\u03b1\u03cd\u03c1\u03c9\u03c3\u03b7\u03c2, \u03b5\u03c0\u03b9\u03c6\u03ac\u03bd\u03b5\u03b9\u03b5\u03c2 \u03b4\u03c1\u03cc\u03bc\u03bf\u03c5, \u03b2\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 \u03bb\u03ae\u03c8\u03b7\u03c2. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2 \u03c4\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03bc\u03b5\u03c4\u03c1\u03ac \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03b1\u03bd\u03c4\u03bf\u03c7\u03ae \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03b5 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ad\u03c2 \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c3\u03af\u03b5\u03c2. \u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03c4\u03b5\u03c3\u03c4 generalization \u03c0\u03c1\u03bf\u03c2 \u03b3\u03b5\u03c9\u03b3\u03c1\u03b1\u03c6\u03b9\u03ba\u03ac \u03b1\u03c0\u03bf\u03bc\u03b1\u03ba\u03c1\u03c5\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03b8\u03ad\u03b1\u03c4\u03b5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ad\u03c2.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03b1\u03ba\u03c5\u03c1\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1. \u039a\u03b1\u03b8\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03c4\u03bf\u03c5 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b1. \u0393\u03b9\u03b1 \u03b1\u03c5\u03c3\u03c4\u03b7\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 coordinate-grouped \u03ae spatially buffered split, \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf near-duplicates. \u0395\u03af\u03bd\u03b1\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03bb\u03cc\u03b3\u03bf\u03c2 \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c4\u03b1 <a href=\"https:\/\/twodots.gr\/model-cards-open-weight-ai-governance\/\">model cards \u03c9\u03c2 \u03b1\u03c1\u03c7\u03ae \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03c9\u03c2 \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7<\/a>: \u03c4\u03bf split, \u03bf\u03b9 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ad\u03c2 \u03b5\u03c0\u03b9\u03ba\u03b1\u03bb\u03cd\u03c8\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b1 failure conditions \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c1\u03b1\u03c4\u03ac \u03c3\u03c4\u03bf\u03bd \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc \u03c5\u03c0\u03b5\u03cd\u03b8\u03c5\u03bd\u03bf.<\/p>\n<h2 id=\"epicheirimatiki-aksia\">\u03a4\u03b9 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03bf\u03c5\u03bd e-commerce \u03ba\u03b1\u03b9 marketing teams<\/h2>\n<p>\u0397 \u03ac\u03bc\u03b5\u03c3\u03b7 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c4\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf \u03bf\u03c0\u03c4\u03b9\u03ba\u03cc\u03c2 \u03b3\u03b5\u03c9\u03b5\u03bd\u03c4\u03bf\u03c0\u03b9\u03c3\u03bc\u03cc\u03c2. \u039f\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c0\u03c1\u03bf\u03b5\u03ba\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03bf\u03cd\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03b1\u03bb\u03c5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf\u03c5\u03c2 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2. \u03a4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 \u03ba\u03b1\u03bb\u03cc aggregate score \u03b4\u03b5\u03bd \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03c0\u03bf\u03b9\u03b1 \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u03a3\u03b5 product tagging, visual search, moderation \u03ae creative classification \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 interventions \u03c0\u03bf\u03c5 \u03b1\u03c6\u03b1\u03b9\u03c1\u03bf\u03cd\u03bd \u03ae \u03b4\u03b9\u03b1\u03c4\u03b1\u03c1\u03ac\u03c3\u03c3\u03bf\u03c5\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 cues.<\/p>\n<p>\u03a4\u03bf \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae adaptation. \u03a4\u03bf LoRA \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 78,36%, \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03c4\u03bf 82,10% \u03c4\u03bf\u03c5 Full-FT, \u03b5\u03bd\u03ce \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03c4\u03bc\u03ae\u03bc\u03b1 \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b4\u03b5\u03bd \u03b4\u03af\u03bd\u03b5\u03b9 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 deployment \u03bf\u03cd\u03c4\u03b5 latency comparison, \u03bf\u03c0\u03cc\u03c4\u03b5 \u03b4\u03b5\u03bd \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03ae \u03c3\u03cd\u03c3\u03c4\u03b1\u03c3\u03b7. \u0394\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03bc\u03c9\u03c2 \u03b3\u03b9\u03b1\u03c4\u03af \u03ad\u03bd\u03b1 pilot \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 frozen probe, parameter-efficient adaptation \u03ba\u03b1\u03b9 full fine-tuning \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf dataset.<\/p>\n<p>\u03a4\u03bf \u03c4\u03c1\u03af\u03c4\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bc\u03b5\u03c4\u03b1\u03c4\u03bf\u03c0\u03af\u03c3\u03b5\u03b9\u03c2. \u0393\u03b9\u03b1 \u03ad\u03bd\u03b1 e-commerce \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b1\u03c5\u03c4\u03cc \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03ad\u03bf studio, \u03ac\u03bb\u03bb\u03bf background \u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc catalog season. \u0393\u03b9\u03b1 marketing analytics \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03ad\u03bf template \u03ae \u03bd\u03ad\u03bf \u03ba\u03b1\u03bd\u03ac\u03bb\u03b9. \u0397 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b1 \u03bc\u03b5 \u03c4\u03bf spatial overlap \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ae: \u03cc\u03c4\u03b1\u03bd training \u03ba\u03b1\u03b9 test \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c5\u03c0\u03b5\u03c1\u03b2\u03bf\u03bb\u03b9\u03ba\u03ac \u03c0\u03bf\u03bb\u03cd context, \u03c4\u03bf score \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03ac \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c3\u03b5 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ad\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b3\u03b5\u03bd\u03af\u03ba\u03b5\u03c5\u03c3\u03b7.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-kicker\">Go\/no-go \u03b3\u03b9\u03b1 computer-vision pilot<\/p>\n<p class=\"td-decision-title\">\u039c\u03b7\u03bd \u03b5\u03c0\u03b9\u03bb\u03ad\u03be\u03b5\u03c4\u03b5 adaptation \u03c0\u03c1\u03b9\u03bd \u03b5\u03bb\u03ad\u03b3\u03be\u03b5\u03c4\u03b5 \u03c4\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b3\u03b5\u03bd\u03b9\u03ba\u03b5\u03cd\u03b5\u03b9<\/p>\n<p>Go \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 leakage audit, baseline \u03bc\u03b5 frozen features, LoRA \u03ba\u03b1\u03b9 Full-FT, \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03cc test set \u03b3\u03b9\u03b1 \u03bd\u03ad\u03bf \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd, interventions \u03c3\u03c4\u03b1 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ac shortcuts \u03ba\u03b1\u03b9 metrics \u03b4\u03b5\u03bc\u03ad\u03bd\u03b1 \u03bc\u03b5 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bb\u03ac\u03b8\u03bf\u03c5\u03c2. No-go \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03c4\u03c5\u03c7\u03b1\u03af\u03bf image split \u03bc\u03b5 near-duplicates, \u03ad\u03bd\u03b1 \u03bc\u03cc\u03bd\u03bf seed, aggregate accuracy \u03c7\u03c9\u03c1\u03af\u03c2 class breakdown \u03ae deployment \u03c7\u03c9\u03c1\u03af\u03c2 monitoring \u03b3\u03b9\u03b1 domain shift.<\/p>\n<\/div>\n<h2 id=\"praktiko-plaisio\">\u0388\u03bd\u03b1 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae<\/h2>\n<p>\u039e\u03b5\u03ba\u03b9\u03bd\u03ae\u03c3\u03c4\u03b5 \u03bc\u03b5 \u03be\u03b5\u03ba\u03ac\u03b8\u03b1\u03c1\u03b7 operational definition \u03c4\u03bf\u03c5 task \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03c0\u03bf\u03b9\u03b5\u03c2 \u03bf\u03bc\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 train, validation \u03ba\u03b1\u03b9 test. \u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf accuracy \u03b1\u03bb\u03bb\u03ac \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03c0\u03cc\u03c3\u03c4\u03b1\u03c3\u03b7\u03c2 \u03ae \u03c3\u03bf\u03b2\u03b1\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03bb\u03ac\u03b8\u03bf\u03c5\u03c2 \u03c0\u03bf\u03c5 \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c3\u03c4\u03b7 \u03c7\u03c1\u03ae\u03c3\u03b7. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 centroid error, \u03ce\u03c3\u03c4\u03b5 \u03b4\u03cd\u03bf \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03b9\u03c2 \u03bd\u03b1 \u03bc\u03b7\u03bd \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03b9\u03c3\u03bf\u03b4\u03cd\u03bd\u03b1\u03bc\u03b5\u03c2 \u03b3\u03b5\u03c9\u03b3\u03c1\u03b1\u03c6\u03b9\u03ba\u03ac.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03bf vision adaptation<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">Step 1<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03b7\u03bd \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03c0\u03b1\u03af\u03c1\u03bd\u03b5\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03ba\u03bb\u03ac\u03c3\u03b5\u03b9\u03c2, \u03b5\u03c0\u03b9\u03c4\u03c1\u03b5\u03c0\u03c4\u03ac inputs, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bb\u03ac\u03b8\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b9\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bf\u03b4\u03b7\u03b3\u03bf\u03cd\u03bd \u03c3\u03b5 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 2<\/span><strong>\u039a\u03ac\u03bd\u03c4\u03b5 leakage \u03ba\u03b1\u03b9 near-duplicate audit<\/strong>\n<p>\u039f\u03bc\u03b1\u03b4\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 coordinates, \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03b1, sessions \u03ae templates \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf split \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c1\u03b7\u03c4\u03ac \u03c0\u03cc\u03c3\u03bf context \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 train \u03ba\u03b1\u03b9 test.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 3<\/span><strong>\u039a\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 \u03ba\u03bf\u03b9\u03bd\u03cc baseline \u03b3\u03b9\u03b1 \u03cc\u03bb\u03b1 \u03c4\u03b1 adaptation regimes<\/strong>\n<p>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 zero-shot, frozen probe, LoRA \u03ba\u03b1\u03b9 Full-FT \u03bc\u03b5 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf preprocessing, \u03c4\u03b9\u03c2 \u03af\u03b4\u03b9\u03b5\u03c2 \u03ba\u03bb\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf test protocol.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 4<\/span><strong>\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 interventions \u03c3\u03c4\u03b1 \u03c0\u03b9\u03b8\u03b1\u03bd\u03ac shortcuts<\/strong>\n<p>\u0391\u03c6\u03b1\u03b9\u03c1\u03ad\u03c3\u03c4\u03b5 background, brand cues \u03ae \u03b5\u03c0\u03bf\u03c7\u03b9\u03ba\u03ac \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03bc\u03b5 matched controls \u03ba\u03b1\u03b9 \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 accuracy, retention \u03ba\u03b1\u03b9 switch rate \u03bc\u03b1\u03b6\u03af.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 5<\/span><strong>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03bd\u03ad\u03bf \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bd\u03ad\u03bf \u03b1\u03c1\u03c7\u03b5\u03af\u03bf<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 geographically \u03ae operationally separated test set: \u03ac\u03bb\u03bb\u03bf \u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b7\u03bc\u03b1, studio, \u03c7\u03ce\u03c1\u03b1, \u03c0\u03b5\u03c1\u03af\u03bf\u03b4\u03bf \u03ae \u03ba\u03b1\u03bd\u03ac\u03bb\u03b9.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 6<\/span><strong>\u0395\u03c0\u03b1\u03bd\u03b1\u03bb\u03ac\u03b2\u03b5\u03c4\u03b5 \u03bc\u03b5 seeds \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2<\/strong>\n<p>\u039c\u03b7\u03bd \u03ba\u03ac\u03bd\u03b5\u03c4\u03b5 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae\u03c2 \u03b1\u03c0\u03cc \u03bc\u03af\u03b1 \u03bc\u03b9\u03ba\u03c1\u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03c7\u03c9\u03c1\u03af\u03c2 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ad\u03c2 \u03b5\u03ba\u03c4\u03b5\u03bb\u03ad\u03c3\u03b5\u03b9\u03c2, class-level \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03ba\u03b1\u03b9 confidence intervals.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 7<\/span><strong>\u03a0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 drift \u03ba\u03b1\u03b9 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 rollback<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 input shift, confidence, human overrides \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf launch, \u03bc\u03b5 versioned dataset \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03bf\u03c6\u03ae \u03c3\u03c4\u03bf \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u03a4\u03b1 paired interventions \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 matched random masks, \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03b5\u03bc\u03c6\u03ac\u03bd\u03b9\u03c3\u03b7\u03c2, \u03b4\u03b9\u03b1\u03c4\u03ac\u03c1\u03b1\u03be\u03b7 layout \u03ba\u03b1\u03b9 controls \u03b5\u03ba\u03c4\u03cc\u03c2 \u03c4\u03bf\u03c5 \u03b2\u03b1\u03c3\u03b9\u03ba\u03bf\u03cd domain. \u0388\u03bd\u03b1 \u03bc\u03cc\u03bd\u03bf metric \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03c1\u03ad\u03c8\u03b5\u03b9 \u03c4\u03bf \u03bd\u03cc\u03b7\u03bc\u03b1, \u03cc\u03c0\u03c9\u03c2 \u03c6\u03ac\u03bd\u03b7\u03ba\u03b5 \u03c3\u03c4\u03b1 edges \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf blur. \u03a0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ac seeds, confidence intervals \u03ba\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b1 test sets \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03b1 \u03cc\u03c4\u03b1\u03bd \u03bc\u03b9\u03ba\u03c1\u03ad\u03c2 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 \u03c0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03bf\u03b4\u03b7\u03b3\u03ae\u03c3\u03bf\u03c5\u03bd \u03c3\u03b5 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae\u03c2.<\/p>\n<h2 id=\"symperasma\">\u03a4\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf CLIP \u00ab\u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9\u00bb \u03c4\u03bf Los Angeles<\/h2>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c4\u03bf\u03c5 visual encoder \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c8\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03bc\u03ad\u03c4\u03c1\u03b9\u03b1 zero-shot \u03b2\u03ac\u03c3\u03b7 \u03c3\u03b5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc classifier \u03b3\u03b9\u03b1 \u03bf\u03ba\u03c4\u03ce \u03ba\u03bf\u03bd\u03c4\u03b9\u03bd\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ad\u03c2, \u03bc\u03ad\u03c3\u03b1 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03c7\u03c9\u03c1\u03b9\u03ba\u03ac \u03b5\u03c0\u03b9\u03ba\u03b1\u03bb\u03c5\u03c0\u03c4\u03cc\u03bc\u03b5\u03bd\u03bf dataset. \u03a0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03b1, \u03c4\u03b1 adapted models \u03ad\u03b3\u03b9\u03bd\u03b1\u03bd \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b5\u03c5\u03b1\u03af\u03c3\u03b8\u03b7\u03c4\u03b1 \u03c3\u03c4\u03b7\u03bd \u03ac\u03b8\u03b9\u03ba\u03c4\u03b7 \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7 \u03c4\u03b7\u03c2 \u03c3\u03ba\u03b7\u03bd\u03ae\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b9\u03c3\u03b1\u03bd \u03bd\u03b1 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc appearance cues \u03cc\u03c0\u03c9\u03c2 \u03b2\u03bb\u03ac\u03c3\u03c4\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bf\u03c5\u03c1\u03b1\u03bd\u03cc\u03c2.<\/p>\n<p>\u0397 \u03c0\u03b9\u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae\u03c2 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03cd\u03bd\u03b8\u03b5\u03c4\u03b7: \u03b7 \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03af\u03b1 \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc configuration \u03ba\u03b1\u03b9 \u03b5\u03bc\u03c6\u03ac\u03bd\u03b9\u03c3\u03b7\u03c2. \u0397 \u03b4\u03bf\u03bc\u03ae \u03ad\u03c7\u03b5\u03b9 \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03bc\u03cc\u03bd\u03b7 \u03c4\u03b7\u03c2. \u039a\u03b1\u03b9 \u03c4\u03bf \u03c5\u03c8\u03b7\u03bb\u03cc accuracy \u03b1\u03c6\u03bf\u03c1\u03ac variation \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b3\u03cd\u03c1\u03c9 \u03b1\u03c0\u03cc \u03b3\u03bd\u03c9\u03c3\u03c4\u03ad\u03c2 \u03c4\u03bf\u03c0\u03bf\u03b8\u03b5\u03c3\u03af\u03b5\u03c2, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03bf\u03c5 \u03b3\u03b5\u03c9\u03b3\u03c1\u03b1\u03c6\u03b9\u03ba\u03bf\u03cd generalization.<\/p>\n<p>\u0393\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03bf\u03c5 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03b9 foundation models, \u03b1\u03c5\u03c4\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03ce\u03c1\u03b9\u03bc\u03b7 \u03c5\u03c0\u03b5\u03bd\u03b8\u03cd\u03bc\u03b9\u03c3\u03b7: \u03bc\u03b7\u03bd \u03c1\u03c9\u03c4\u03ac\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd \u03b7 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5. \u03a1\u03c9\u03c4\u03ae\u03c3\u03c4\u03b5 \u03c0\u03bf\u03b9\u03b1 evidence \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c0\u03bb\u03ad\u03bf\u03bd \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03c0\u03ce\u03c2 \u03b1\u03bd\u03c4\u03b9\u03b4\u03c1\u03ac \u03cc\u03c4\u03b1\u03bd \u03b1\u03c5\u03c4\u03ae \u03b4\u03b9\u03b1\u03c4\u03b1\u03c1\u03ac\u03c3\u03c3\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03b1\u03bd \u03c4\u03bf test set \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03cc\u03c3\u03bf \u03ac\u03b3\u03bd\u03c9\u03c3\u03c4\u03bf \u03bd\u03bf\u03bc\u03af\u03b6\u03b5\u03c4\u03b5.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-kicker\">\u0391\u03c0\u03cc \u03c4\u03bf benchmark \u03c3\u03b5 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 AI \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd<\/p>\n<p class=\"td-service-cta-title\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 computer-vision \u03c1\u03bf\u03ad\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03bf\u03c5\u03bd \u03c4\u03b7 \u03b3\u03b5\u03bd\u03af\u03ba\u03b5\u03c5\u03c3\u03ae \u03c4\u03bf\u03c5\u03c2<\/p>\n<p>\u0397 TWO DOTS \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 data splits, leakage audits, model evaluation, monitoring \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b1 approval gates \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf vision model \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03ae\u03c2 \u03c3\u03b1\u03c2.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u03a3\u03c5\u03b6\u03b7\u03c4\u03ae\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf AI pilot<\/a><\/div>\n<\/div>\n<\/section>\n<section id=\"sychnes-erotiseis\" class=\"td-faq-section\">\n<div class=\"td-faq\">\n<p class=\"td-faq-heading\">Frequently Asked Questions (FAQs)<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf regional geolocalization;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03b7 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03bc\u03b9\u03b1\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1\u03c2 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03ba\u03bf\u03bd\u03c4\u03b9\u03bd\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ad\u03c2 \u03c4\u03b7\u03c2 \u03af\u03b4\u03b9\u03b1\u03c2 \u03bc\u03b7\u03c4\u03c1\u03bf\u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b6\u03ce\u03bd\u03b7\u03c2, \u03cc\u03c0\u03bf\u03c5 \u03c4\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7\u03c2 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b1\u03c2 \u03b3\u03b5\u03c9\u03b3\u03c1\u03b1\u03c6\u03b9\u03ba\u03ac cues \u03c3\u03c5\u03c7\u03bd\u03ac \u03bc\u03bf\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b1 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 \u03b5\u03af\u03c7\u03b5 \u03c4\u03b7\u03bd \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 fine-tuning \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 82,10% \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf test set, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 78,36% \u03b3\u03b9\u03b1 LoRA \u03ba\u03b1\u03b9 75,94% \u03b3\u03b9\u03b1 Partial Update.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03bf linear probing \u03b1\u03c0\u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03cc\u03c4\u03b9 \u03c4\u03bf frozen CLIP \u03b4\u03b5\u03bd \u03ad\u03c7\u03b5\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0388\u03b4\u03b5\u03b9\u03be\u03b5 \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 frozen readouts \u03b4\u03b5\u03bd \u03b1\u03bd\u03ad\u03ba\u03c4\u03b7\u03c3\u03b1\u03bd \u03c4\u03b7 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c4\u03c9\u03bd adapted encoders. \u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03af\u03b5\u03b9 \u03ac\u03bb\u03bb\u03b7, \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7\u03c2 \u03c4\u03c9\u03bd frozen features.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd edges \u03ba\u03b1\u03b9 blur;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0393\u03b9\u03b1 \u03bd\u03b1 \u03bc\u03b5\u03b9\u03c9\u03b8\u03b5\u03af \u03b7 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03b5\u03c1\u03ae\u03c2 \u03b5\u03bc\u03c6\u03ac\u03bd\u03b9\u03c3\u03b7 \u03b5\u03bd\u03ce \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af\u03c4\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03c7\u03bf\u03bd\u03b4\u03c1\u03bf\u03b5\u03b9\u03b4\u03bf\u03cd\u03c2 \u03bf\u03c1\u03b3\u03ac\u03bd\u03c9\u03c3\u03b7\u03c2. \u0395\u03af\u03bd\u03b1\u03b9 \u03b1\u03c4\u03b5\u03bb\u03ae proxies \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03ce\u03bd\u03bf\u03c5\u03bd \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 \u03c4\u03b7 \u03b4\u03bf\u03bc\u03ae.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03c4\u03bf patch scrambling;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03b1 adapted models \u03ac\u03bb\u03bb\u03b1\u03b6\u03b1\u03bd \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03c0\u03bf\u03bb\u03cd \u03c3\u03c5\u03c7\u03bd\u03cc\u03c4\u03b5\u03c1\u03b1 \u03cc\u03c4\u03b1\u03bd \u03b4\u03b9\u03b1\u03c4\u03b1\u03c1\u03b1\u03c3\u03c3\u03cc\u03c4\u03b1\u03bd \u03b7 \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7, \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03bf \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03cc \u03bc\u03b5 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf scene configuration.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0397 \u03b5\u03c5\u03b1\u03b9\u03c3\u03b8\u03b7\u03c3\u03af\u03b1 \u03c3\u03c4\u03bf scrambling \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03b9\u03b4\u03b9\u03ba\u03ae \u03c3\u03c4\u03bf\u03bd \u03b3\u03b5\u03c9\u03b5\u03bd\u03c4\u03bf\u03c0\u03b9\u03c3\u03bc\u03cc;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u03a4\u03bf Caltech101 control \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03c0\u03c4\u03ce\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03b1\u03bd\u03b1\u03b3\u03bd\u03ce\u03c1\u03b9\u03c3\u03b7 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03c9\u03bd, \u03ac\u03c1\u03b1 \u03c4\u03bf corruption \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03b1 \u03c4\u03b7\u03bd \u03cc\u03c1\u03b1\u03c3\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf dataset split \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03bf 55,39% \u03c4\u03c9\u03bd test \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd \u03bc\u03bf\u03b9\u03c1\u03b1\u03b6\u03cc\u03c4\u03b1\u03bd coordinate \u03bc\u03b5 \u03c4\u03bf training \u03ba\u03b1\u03b9 \u03c4\u03bf 79,68% \u03b2\u03c1\u03b9\u03c3\u03ba\u03cc\u03c4\u03b1\u03bd \u03b5\u03bd\u03c4\u03cc\u03c2 50 \u03bc\u03ad\u03c4\u03c1\u03c9\u03bd, \u03bf\u03c0\u03cc\u03c4\u03b5 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 viewpoint variation \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03b5 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ac \u03c3\u03b7\u03bc\u03b5\u03af\u03b1.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ae \u03c3\u03c5\u03bc\u03b2\u03bf\u03c5\u03bb\u03ae \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1 \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc AI pilot;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039d\u03b1 \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 aggregate metrics \u03bc\u03b5 intervention tests, \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03bf test set, \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ac seeds \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03c0\u03bf\u03c4\u03c5\u03c0\u03ce\u03bd\u03bf\u03c5\u03bd \u03c4\u03bf \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03bb\u03ac\u03b8\u03bf\u03c5\u03c2.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">Sources<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2608.21761\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 What Does CLIP Learn for Regional Geolocalization?<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/mlfoundations\/open_clip\" target=\"_blank\" rel=\"noopener\">ML Foundations \u2014 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03bf repository \u03c4\u03bf\u03c5 OpenCLIP<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2103.00020\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 Learning Transferable Visual Models From Natural Language Supervision<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2106.09685\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 LoRA: Low-Rank Adaptation of Large Language Models<\/a><\/li>\n<li><a href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\" target=\"_blank\" rel=\"noopener\">NIST \u2014 AI Risk Management Framework<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u0397 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c4\u03bf\u03c5 CLIP \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf\u03bd \u03c0\u03b5\u03c1\u03b9\u03c6\u03b5\u03c1\u03b5\u03b9\u03b1\u03ba\u03cc \u03b3\u03b5\u03c9\u03b5\u03bd\u03c4\u03bf\u03c0\u03b9\u03c3\u03bc\u03cc, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf scene configuration \u03ba\u03b1\u03b9 \u03c4\u03bf train\u2013test overlap \u03ba\u03b1\u03b8\u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1.<\/p>","protected":false},"author":1,"featured_media":98380,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[199],"tags":[20408,20591,19230,19275,20592],"class_list":["post-97697","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-ai-models","tag-clip","tag-computer-vision","tag-lora","tag-visual-geolocation"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/97697","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/comments?post=97697"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/97697\/revisions"}],"predecessor-version":[{"id":98381,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/97697\/revisions\/98381"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media\/98380"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media?parent=97697"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/categories?post=97697"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/tags?post=97697"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}