{"id":9253,"date":"2024-01-16T14:01:10","date_gmt":"2024-01-16T14:01:10","guid":{"rendered":"https:\/\/dailyai.com\/?p=9253"},"modified":"2024-01-16T14:01:10","modified_gmt":"2024-01-16T14:01:10","slug":"v-multimodal-llm-guided-visual-search-that-beats-gpt-4v","status":"publish","type":"post","link":"https:\/\/dailyai.com\/sv\/2024\/01\/v-multimodal-llm-guided-visual-search-that-beats-gpt-4v\/","title":{"rendered":"V* - Multimodal LLM-styrd visuell s\u00f6kning som sl\u00e5r GPT-4V"},"content":{"rendered":"

Forskare fr\u00e5n UC San Diego och New York University har utvecklat V*, en LLM-styrd s\u00f6kalgoritm som \u00e4r mycket b\u00e4ttre \u00e4n GPT-4V p\u00e5 kontextuell f\u00f6rst\u00e5else och exakt inriktning p\u00e5 specifika visuella element i bilder.<\/strong><\/p>\n

Multimodala stora spr\u00e5kmodeller (MLLM) som OpenAI:s GPT-4V slog oss med h\u00e4pnad f\u00f6rra \u00e5ret med sin f\u00f6rm\u00e5ga att svara p\u00e5 fr\u00e5gor om bilder. Hur imponerande GPT-4V \u00e4n \u00e4r s\u00e5 k\u00e4mpar den ibland n\u00e4r bilderna \u00e4r mycket komplexa och missar ofta sm\u00e5 detaljer.<\/p>\n

V*-algoritmen anv\u00e4nder en Visual Question Answering (VQA) LLM f\u00f6r att identifiera vilket omr\u00e5de i bilden den ska fokusera p\u00e5 f\u00f6r att besvara en visuell fr\u00e5ga. Forskarna kallar denna kombination f\u00f6r Show, sEArch och telL (SEAL).<\/p>\n

Om n\u00e5gon gav dig en h\u00f6guppl\u00f6st bild och st\u00e4llde en fr\u00e5ga om den, skulle din logik leda dig till att zooma in p\u00e5 ett omr\u00e5de d\u00e4r det \u00e4r mest sannolikt att du hittar f\u00f6rem\u00e5let i fr\u00e5ga. SEAL anv\u00e4nder V* f\u00f6r att analysera bilder p\u00e5 ett liknande s\u00e4tt.<\/p>\n

En visuell s\u00f6kmodell skulle helt enkelt kunna dela upp en bild i block, zooma in i varje block och sedan bearbeta den f\u00f6r att hitta objektet i fr\u00e5ga, men det \u00e4r ber\u00e4kningsm\u00e4ssigt mycket ineffektivt.<\/p>\n

N\u00e4r V* f\u00e5r en textuell fr\u00e5ga om en bild f\u00f6rs\u00f6ker den f\u00f6rst lokalisera bildm\u00e5let direkt. Om det inte g\u00e5r ber den MLLM att anv\u00e4nda sunt f\u00f6rnuft f\u00f6r att identifiera vilket omr\u00e5de i bilden som det \u00e4r mest troligt att m\u00e5let befinner sig i.<\/p>\n

Den fokuserar sedan sin s\u00f6kning p\u00e5 just det omr\u00e5det, i st\u00e4llet f\u00f6r att f\u00f6rs\u00f6ka g\u00f6ra en \"inzoomad\" s\u00f6kning av hela bilden.<\/p>\n

\"\"
N\u00e4r LLM uppmanas att s\u00f6ka efter gitarren identifierar den scenen som det logiska omr\u00e5det att fokusera den visuella analysen p\u00e5 f\u00f6r att leta efter den. K\u00e4lla: GitHub GitHub<\/figcaption><\/figure>\n

N\u00e4r GPT-4V uppmanas att svara p\u00e5 fr\u00e5gor om en bild som kr\u00e4ver omfattande visuell bearbetning av h\u00f6guppl\u00f6sta bilder \u00e4r det sv\u00e5rt. SEAL som anv\u00e4nder V* presterar mycket b\u00e4ttre.<\/p>\n

\"\"
SEAL svarar r\u00e4tt p\u00e5 en fr\u00e5ga om en bild medan GPT-4V svarar fel. K\u00e4lla: GitHub GitHub<\/figcaption><\/figure>\n

P\u00e5 fr\u00e5gan \"Vilken typ av dryck kan vi k\u00f6pa fr\u00e5n den d\u00e4r automaten?\" svarade SEAL \"Coca-Cola\" medan GPT-4V felaktigt gissade p\u00e5 \"Pepsi\".<\/p>\n

Forskarna anv\u00e4nde 191 h\u00f6guppl\u00f6sta bilder fr\u00e5n Meta's Segment Anything (SAM) dataset och skapade ett riktm\u00e4rke f\u00f6r att se hur SEALs prestanda j\u00e4mf\u00f6rdes med andra modeller. V*Bench-riktm\u00e4rket testar tv\u00e5 uppgifter: attributigenk\u00e4nning och resonemang om rumsliga relationer.<\/p>\n

Figurerna nedan visar m\u00e4nsklig prestanda j\u00e4mf\u00f6rt med modeller med \u00f6ppen k\u00e4llkod, kommersiella modeller som GPT-4V och SEAL. Den \u00f6kning som V* ger i SEAL:s prestanda \u00e4r s\u00e4rskilt imponerande eftersom den underliggande MLLM som anv\u00e4nds \u00e4r LLaVa-7b, som \u00e4r mycket mindre \u00e4n GPT-4V.<\/p>\n

\"\"<\/p>\n

Denna intuitiva metod f\u00f6r att analysera bilder verkar fungera riktigt bra med ett antal imponerande exempel p\u00e5 sammanfattning p\u00e5 GitHub<\/a>.<\/p>\n

Det ska bli intressant att se om andra MLLM:er, som de fr\u00e5n OpenAI eller Google, anv\u00e4nder sig av ett liknande tillv\u00e4gag\u00e5ngss\u00e4tt.<\/p>\n

P\u00e5 fr\u00e5gan om vilken dryck som s\u00e5ldes fr\u00e5n automaten p\u00e5 bilden ovan svarade Googles Bard: \"Det finns ingen automat i f\u00f6rgrunden.\" Kanske Gemini Ultra kommer att g\u00f6ra ett b\u00e4ttre jobb.<\/p>\n

F\u00f6r n\u00e4rvarande ser det ut som om SEAL och dess nya V*-algoritm ligger en bra bit f\u00f6re n\u00e5gra av de st\u00f6rsta multimodala modellerna n\u00e4r det g\u00e4ller visuella fr\u00e5gor.<\/p>\n

 <\/p>","protected":false},"excerpt":{"rendered":"

Forskare fr\u00e5n UC San Diego och New York University har utvecklat V*, en LLM-styrd s\u00f6kalgoritm som \u00e4r mycket b\u00e4ttre \u00e4n GPT-4V p\u00e5 kontextuell f\u00f6rst\u00e5else och exakt inriktning p\u00e5 specifika visuella element i bilder. Multimodala stora spr\u00e5kmodeller (MLLM) som OpenAI:s GPT-4V slog oss med h\u00e4pnad f\u00f6rra \u00e5ret med sin f\u00f6rm\u00e5ga att svara p\u00e5 fr\u00e5gor om bilder. Hur imponerande GPT-4V \u00e4n \u00e4r s\u00e5 har den ibland problem n\u00e4r bilderna \u00e4r mycket komplexa och missar ofta sm\u00e5 detaljer. V*-algoritmen anv\u00e4nder en Visual Question Answering (VQA) LLM f\u00f6r att v\u00e4gleda den i att identifiera vilket omr\u00e5de i bilden den ska fokusera p\u00e5 f\u00f6r att svara p\u00e5 en visuell<\/p>","protected":false},"author":6,"featured_media":9260,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[166,118],"class_list":["post-9253","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-computer-vision","tag-llms"],"yoast_head":"\nV* - Multimodal LLM guided visual search that beats GPT-4V | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/sv\/2024\/01\/v-multimodal-llm-guided-visual-search-that-beats-gpt-4v\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"V* - Multimodal LLM guided visual search that beats GPT-4V | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from UC San Diego and New York University developed V*, an LLM-guided search algorithm that is a lot better than GPT-4V at contextual understanding, and precise targeting of specific visual elements in images. 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