{"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\/de\/2024\/01\/v-multimodal-llm-guided-visual-search-that-beats-gpt-4v\/","title":{"rendered":"V* - Multimodale LLM-gef\u00fchrte visuelle Suche, die GPT-4V \u00fcbertrifft"},"content":{"rendered":"

Forscher der UC San Diego und der New York University haben V* entwickelt, einen LLM-gef\u00fchrten Suchalgorithmus, der wesentlich besser als GPT-4V in der Lage ist, den Kontext zu verstehen und bestimmte visuelle Elemente in Bildern pr\u00e4zise zu finden.<\/strong><\/p>\n

Multimodale Large Language Models (MLLM) wie OpenAIs GPT-4V haben uns letztes Jahr mit ihrer F\u00e4higkeit, Fragen zu Bildern zu beantworten, umgehauen. So beeindruckend GPT-4V auch ist, es hat manchmal Probleme, wenn Bilder sehr komplex sind, und \u00fcbersieht oft kleine Details.<\/p>\n

Der V*-Algorithmus verwendet ein Visual Question Answering (VQA) LLM, um zu ermitteln, auf welchen Bereich des Bildes er sich konzentrieren muss, um eine visuelle Anfrage zu beantworten. Die Forscher nennen diese Kombination Show, sEArch, and telL (SEAL).<\/p>\n

Wenn Ihnen jemand ein hochaufl\u00f6sendes Bild gibt und Ihnen eine Frage dazu stellt, w\u00fcrde Ihre Logik Sie dazu veranlassen, einen Bereich zu vergr\u00f6\u00dfern, in dem Sie das fragliche Objekt am ehesten finden. SEAL verwendet V*, um Bilder auf \u00e4hnliche Weise zu analysieren.<\/p>\n

Ein visuelles Suchmodell k\u00f6nnte ein Bild einfach in Bl\u00f6cke unterteilen, in jeden Block hineinzoomen und ihn dann verarbeiten, um das betreffende Objekt zu finden, aber das ist rechnerisch sehr ineffizient.<\/p>\n

Bei einer Textabfrage zu einem Bild versucht V* zun\u00e4chst, das Bildziel direkt zu lokalisieren. Gelingt dies nicht, bittet es das MLLM, mit gesundem Menschenverstand zu ermitteln, in welchem Bereich des Bildes das Ziel am wahrscheinlichsten zu finden ist.<\/p>\n

Die Suche konzentriert sich dann nur auf diesen Bereich, anstatt das gesamte Bild zu durchsuchen.<\/p>\n

\"\"
Bei der Aufforderung, nach der Gitarre zu suchen, identifiziert das LLM die B\u00fchne als den logischen Bereich, auf den sich die visuelle Analyse bei der Suche nach der Gitarre konzentrieren sollte. Quelle: GitHub<\/figcaption><\/figure>\n

Wenn GPT-4V aufgefordert wird, Fragen zu einem Bild zu beantworten, das eine umfangreiche visuelle Verarbeitung von hochaufl\u00f6senden Bildern erfordert, hat es Schwierigkeiten. SEAL mit V* schneidet viel besser ab.<\/p>\n

\"\"
SEAL beantwortet eine Frage zu einem Bild richtig, w\u00e4hrend GPT-4V die Frage falsch beantwortet. Quelle: GitHub<\/figcaption><\/figure>\n

Auf die Frage \"Welche Art von Getr\u00e4nk k\u00f6nnen wir aus diesem Automaten kaufen?\" SEAL antwortete \"Coca-Cola\", w\u00e4hrend GPT-4V f\u00e4lschlicherweise auf \"Pepsi\" tippte.<\/p>\n

Die Forscher verwendeten 191 hochaufl\u00f6sende Bilder aus dem Segment Anything (SAM)-Datensatz von Meta und erstellten einen Benchmark, um zu sehen, wie die Leistung von SEAL im Vergleich zu anderen Modellen ist. Der V*Bench-Benchmark testet zwei Aufgaben: die Erkennung von Attributen und das Erkennen von r\u00e4umlichen Beziehungen.<\/p>\n

Die folgenden Abbildungen zeigen die menschliche Leistung im Vergleich zu Open-Source-Modellen, kommerziellen Modellen wie GPT-4V und SEAL. Der Anstieg der Leistung von SEAL durch V* ist besonders beeindruckend, weil die zugrunde liegende MLLM LLaVa-7b ist, die viel kleiner als GPT-4V ist.<\/p>\n

\"\"<\/p>\n

Dieser intuitive Ansatz zur Analyse von Bildern scheint wirklich gut zu funktionieren, denn es gibt eine Reihe beeindruckender Beispiele auf der Website Zusammenfassung des Papiers auf GitHub<\/a>.<\/p>\n

Es wird interessant sein zu sehen, ob andere MLLMs wie die von OpenAI oder Google einen \u00e4hnlichen Ansatz verfolgen.<\/p>\n

Auf die Frage, welches Getr\u00e4nk aus dem Automaten im obigen Bild verkauft wurde, antwortete Googles Bard: \"Es gibt keinen Automaten im Vordergrund.\" Vielleicht wird Gemini Ultra es besser machen.<\/p>\n

Im Moment sieht es so aus, als ob SEAL und sein neuartiger V*-Algorithmus einigen der gr\u00f6\u00dften multimodalen Modelle bei der visuellen Befragung um einiges voraus sind.<\/p>\n

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

Forscher der UC San Diego und der New York University haben V* entwickelt, einen LLM-gesteuerten Suchalgorithmus, der wesentlich besser als GPT-4V in der Lage ist, den Kontext zu verstehen und bestimmte visuelle Elemente in Bildern pr\u00e4zise zu erfassen. Multimodale Large Language Models (MLLM) wie OpenAIs GPT-4V haben uns letztes Jahr mit ihrer F\u00e4higkeit, Fragen zu Bildern zu beantworten, umgehauen. So beeindruckend GPT-4V auch ist, es hat manchmal Schwierigkeiten, wenn Bilder sehr komplex sind und \u00fcbersieht oft kleine Details. Der V*-Algorithmus verwendet ein Visual Question Answering (VQA) LLM, um zu erkennen, auf welchen Bereich des Bildes er sich konzentrieren muss, um eine visuelle Frage zu beantworten.<\/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\/de\/2024\/01\/v-multimodal-llm-guided-visual-search-that-beats-gpt-4v\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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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