{"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\/pt\/2024\/01\/v-multimodal-llm-guided-visual-search-that-beats-gpt-4v\/","title":{"rendered":"V* - Pesquisa visual guiada por LLM multimodal que supera a GPT-4V"},"content":{"rendered":"

Investigadores da Universidade da Calif\u00f3rnia em San Diego e da Universidade de Nova Iorque desenvolveram o V*, um algoritmo de pesquisa guiada por LLM que \u00e9 muito melhor do que o GPT-4V na compreens\u00e3o do contexto e na sele\u00e7\u00e3o precisa de elementos visuais espec\u00edficos nas imagens.<\/strong><\/p>\n

Os modelos multimodais de linguagem ampla (MLLM), como o GPT-4V da OpenAI, surpreenderam-nos no ano passado com a sua capacidade de responder a perguntas sobre imagens. Por muito impressionante que o GPT-4V seja, por vezes tem dificuldades quando as imagens s\u00e3o muito complexas e, muitas vezes, deixa escapar pequenos pormenores.<\/p>\n

O algoritmo V* utiliza um LLM de resposta a perguntas visuais (VQA) para o orientar na identifica\u00e7\u00e3o da \u00e1rea da imagem em que se deve concentrar para responder a uma pergunta visual. Os investigadores chamam a esta combina\u00e7\u00e3o Show, sEArch, and telL (SEAL).<\/p>\n

Se algu\u00e9m lhe desse uma imagem de alta resolu\u00e7\u00e3o e lhe fizesse uma pergunta sobre ela, a sua l\u00f3gica gui\u00e1-lo-ia para fazer zoom numa \u00e1rea onde fosse mais prov\u00e1vel encontrar o item em quest\u00e3o. O SEAL utiliza o V* para analisar imagens de uma forma semelhante.<\/p>\n

Um modelo de pesquisa visual poderia simplesmente dividir uma imagem em blocos, fazer zoom em cada bloco e depois process\u00e1-la para encontrar o objeto em quest\u00e3o, mas isso \u00e9 computacionalmente muito ineficiente.<\/p>\n

Quando lhe \u00e9 solicitada uma consulta textual sobre uma imagem, V* come\u00e7a por tentar localizar diretamente o alvo da imagem. Se n\u00e3o o conseguir fazer, pede ao MLLM que utilize uma abordagem de senso comum para identificar a \u00e1rea da imagem onde \u00e9 mais prov\u00e1vel que o alvo se encontre.<\/p>\n

Em seguida, concentra a sua pesquisa apenas nessa \u00e1rea, em vez de tentar uma pesquisa \"ampliada\" de toda a imagem.<\/p>\n

\"\"
Quando lhe \u00e9 pedido que procure a guitarra, o LLM identifica o palco como a \u00e1rea l\u00f3gica onde concentrar a an\u00e1lise visual para a procurar. Fonte: GitHub<\/figcaption><\/figure>\n

Quando o GPT-4V \u00e9 solicitado a responder a perguntas sobre uma imagem que requer um processamento visual extensivo de imagens de alta resolu\u00e7\u00e3o, ele tem dificuldades. O SEAL com V* tem um desempenho muito melhor.<\/p>\n

\"\"
O SEAL responde corretamente a uma pergunta sobre uma imagem, enquanto o GPT-4V se engana. Fonte: GitHub<\/figcaption><\/figure>\n

Quando lhe foi perguntado \"Que tipo de bebida podemos comprar naquela m\u00e1quina de venda autom\u00e1tica?\" o SEAL respondeu \"Coca-Cola\", enquanto o GPT-4V adivinhou incorretamente \"Pepsi\".<\/p>\n

Os investigadores utilizaram 191 imagens de alta resolu\u00e7\u00e3o do conjunto de dados Meta's Segment Anything (SAM) e criaram uma refer\u00eancia para ver como o desempenho do SEAL se comparava com o de outros modelos. O benchmark V*Bench testa duas tarefas: reconhecimento de atributos e racioc\u00ednio de rela\u00e7\u00f5es espaciais.<\/p>\n

As figuras abaixo mostram o desempenho humano em compara\u00e7\u00e3o com modelos de c\u00f3digo aberto, modelos comerciais como o GPT-4V e o SEAL. O aumento que o V* d\u00e1 no desempenho do SEAL \u00e9 particularmente impressionante porque o MLLM subjacente que utiliza \u00e9 o LLaVa-7b, que \u00e9 muito mais pequeno do que o GPT-4V.<\/p>\n

\"\"<\/p>\n

Esta abordagem intuitiva \u00e0 an\u00e1lise de imagens parece funcionar muito bem com uma s\u00e9rie de exemplos impressionantes no resumo do documento no GitHub<\/a>.<\/p>\n

Ser\u00e1 interessante ver se outros MLLMs, como os da OpenAI ou da Google, adoptam uma abordagem semelhante.<\/p>\n

Quando lhe perguntaram que bebida era vendida na m\u00e1quina de venda autom\u00e1tica da imagem acima, o Bard da Google respondeu: \"N\u00e3o h\u00e1 nenhuma m\u00e1quina de venda autom\u00e1tica em primeiro plano\". Talvez o Gemini Ultra fa\u00e7a um trabalho melhor.<\/p>\n

Para j\u00e1, parece que o SEAL e o seu novo algoritmo V* est\u00e3o muito \u00e0 frente de alguns dos maiores modelos multimodais no que diz respeito ao questionamento visual.<\/p>\n

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

Investigadores da Universidade da Calif\u00f3rnia em San Diego e da Universidade de Nova Iorque desenvolveram o V*, um algoritmo de pesquisa guiado por LLM que \u00e9 muito melhor do que o GPT-4V na compreens\u00e3o do contexto e na sele\u00e7\u00e3o precisa de elementos visuais espec\u00edficos nas imagens. Os modelos multimodais de linguagem ampla (MLLM), como o GPT-4V da OpenAI, surpreenderam-nos no ano passado com a sua capacidade de responder a perguntas sobre imagens. Por muito impressionante que o GPT-4V seja, por vezes tem dificuldades quando as imagens s\u00e3o muito complexas e muitas vezes deixa escapar pequenos pormenores. O algoritmo V* utiliza um LLM de resposta a perguntas visuais (VQA) para o orientar na identifica\u00e7\u00e3o da \u00e1rea da imagem em que se deve concentrar para responder a uma pergunta visual<\/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\/pt\/2024\/01\/v-multimodal-llm-guided-visual-search-that-beats-gpt-4v\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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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