{"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\/da\/2024\/01\/v-multimodal-llm-guided-visual-search-that-beats-gpt-4v\/","title":{"rendered":"V* - Multimodal LLM-styret visuel s\u00f8gning, der sl\u00e5r GPT-4V"},"content":{"rendered":"

Forskere fra UC San Diego og New York University har udviklet V*, en LLM-styret s\u00f8gealgoritme, som er meget bedre end GPT-4V til at forst\u00e5 konteksten og til at ramme specifikke visuelle elementer i billeder.<\/strong><\/p>\n

Multimodale store sprogmodeller (MLLM) som OpenAI's GPT-4V slog benene v\u00e6k under os sidste \u00e5r med deres evne til at besvare sp\u00f8rgsm\u00e5l om billeder. Hvor imponerende GPT-4V end er, s\u00e5 k\u00e6mper den nogle gange, n\u00e5r billederne er meget komplekse og ofte overser sm\u00e5 detaljer.<\/p>\n

V*-algoritmen bruger en Visual Question Answering (VQA) LLM til at guide den i at identificere, hvilket omr\u00e5de af billedet den skal fokusere p\u00e5 for at besvare en visuel foresp\u00f8rgsel. Forskerne kalder denne kombination for Show, sEArch og telL (SEAL).<\/p>\n

Hvis nogen gav dig et billede i h\u00f8j opl\u00f8sning og stillede dig et sp\u00f8rgsm\u00e5l om det, ville din logik f\u00e5 dig til at zoome ind p\u00e5 et omr\u00e5de, hvor der er st\u00f8rst sandsynlighed for at finde den p\u00e5g\u00e6ldende genstand. SEAL bruger V* til at analysere billeder p\u00e5 en lignende m\u00e5de.<\/p>\n

En visuel s\u00f8gemodel kunne simpelthen opdele et billede i blokke, zoome ind p\u00e5 hver blok og derefter behandle det for at finde det p\u00e5g\u00e6ldende objekt, men det er beregningsm\u00e6ssigt meget ineffektivt.<\/p>\n

N\u00e5r V* f\u00e5r en tekstforesp\u00f8rgsel om et billede, fors\u00f8ger den f\u00f8rst at finde billedm\u00e5let direkte. Hvis det ikke lykkes, beder den MLLM om at bruge sin sunde fornuft til at identificere det omr\u00e5de i billedet, hvor det er mest sandsynligt, at m\u00e5let befinder sig.<\/p>\n

Derefter fokuserer den sin s\u00f8gning p\u00e5 netop det omr\u00e5de i stedet for at fors\u00f8ge en \"indzoomet\" s\u00f8gning p\u00e5 hele billedet.<\/p>\n

\"\"
N\u00e5r man bliver bedt om at s\u00f8ge efter guitaren, identificerer LLM scenen som det logiske omr\u00e5de at fokusere den visuelle analyse p\u00e5 for at lede efter den. Kilde: GitHub GitHub<\/figcaption><\/figure>\n

N\u00e5r GPT-4V bliver bedt om at besvare sp\u00f8rgsm\u00e5l om et billede, der kr\u00e6ver omfattende visuel behandling af h\u00f8jopl\u00f8selige billeder, har den det sv\u00e6rt. SEAL, der bruger V*, klarer sig meget bedre.<\/p>\n

\"\"
SEAL svarer korrekt p\u00e5 et sp\u00f8rgsm\u00e5l om et billede, mens GPT-4V tager fejl. Kilde: GitHub<\/figcaption><\/figure>\n

P\u00e5 sp\u00f8rgsm\u00e5let \"Hvilken slags drik kan vi k\u00f8be i den automat?\" svarede SEAL \"Coca-Cola\". svarede SEAL \"Coca-Cola\", mens GPT-4V fejlagtigt g\u00e6ttede p\u00e5 \"Pepsi\".<\/p>\n

Forskerne brugte 191 billeder i h\u00f8j opl\u00f8sning fra Meta's Segment Anything (SAM)-datas\u00e6t og skabte et benchmark for at se, hvordan SEALs pr\u00e6station var sammenlignet med andre modeller. V*Bench-benchmarken tester to opgaver: genkendelse af attributter og rumlige relationer.<\/p>\n

Figurerne nedenfor viser den menneskelige ydeevne sammenlignet med open source-modeller, kommercielle modeller som GPT-4V og SEAL. Det l\u00f8ft, som V* giver i SEALs pr\u00e6station, er s\u00e6rligt imponerende, fordi den underliggende MLLM, den bruger, er LLaVa-7b, som er meget mindre end GPT-4V.<\/p>\n

\"\"<\/p>\n

Denne intuitive tilgang til at analysere billeder ser ud til at fungere rigtig godt med en r\u00e6kke imponerende eksempler p\u00e5 papirets resum\u00e9 p\u00e5 GitHub<\/a>.<\/p>\n

Det bliver interessant at se, om andre MLLM'er som dem fra OpenAI eller Google anvender en lignende tilgang.<\/p>\n

P\u00e5 sp\u00f8rgsm\u00e5let om, hvilken drik der blev solgt fra automaten p\u00e5 billedet ovenfor, svarede Googles Bard: \"Der er ingen automat i forgrunden.\" M\u00e5ske vil Gemini Ultra g\u00f8re et bedre stykke arbejde.<\/p>\n

Indtil videre ser det ud til, at SEAL og dens nye V*-algoritme er langt foran nogle af de st\u00f8rste multimodale modeller, n\u00e5r det g\u00e6lder visuel afh\u00f8ring.<\/p>\n

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

Forskere fra UC San Diego og New York University har udviklet V*, en LLM-styret s\u00f8gealgoritme, der er meget bedre end GPT-4V til kontekstuel forst\u00e5else og pr\u00e6cis m\u00e5lretning af specifikke visuelle elementer i billeder. Multimodale store sprogmodeller (MLLM) som OpenAI's GPT-4V slog benene v\u00e6k under os sidste \u00e5r med deres evne til at besvare sp\u00f8rgsm\u00e5l om billeder. Hvor imponerende GPT-4V end er, s\u00e5 k\u00e6mper den nogle gange, n\u00e5r billederne er meget komplekse, og overser ofte sm\u00e5 detaljer. V*-algoritmen bruger en Visual Question Answering (VQA) LLM til at guide den i at identificere, hvilket omr\u00e5de af billedet den skal fokusere p\u00e5 for at besvare et visuelt sp\u00f8rgsm\u00e5l.<\/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\/da\/2024\/01\/v-multimodal-llm-guided-visual-search-that-beats-gpt-4v\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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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