{"id":11850,"date":"2024-04-30T12:48:52","date_gmt":"2024-04-30T12:48:52","guid":{"rendered":"https:\/\/dailyai.com\/?p=11850"},"modified":"2024-04-30T15:58:32","modified_gmt":"2024-04-30T15:58:32","slug":"googles-gecko-benchmark-identifies-best-ai-image-generator","status":"publish","type":"post","link":"https:\/\/dailyai.com\/pt\/2024\/04\/googles-gecko-benchmark-identifies-best-ai-image-generator\/","title":{"rendered":"O teste de refer\u00eancia Gecko da Google identifica o melhor gerador de imagens com IA"},"content":{"rendered":"

O Google DeepMind<\/span><\/span> lan\u00e7ou o Gecko, um novo par\u00e2metro de refer\u00eancia para avaliar exaustivamente os modelos de IA de texto para imagem (T2I).<\/strong><\/p>\n

Nos \u00faltimos dois anos, vimos geradores de imagens com IA como DALL-E<\/span><\/span><\/a> e Meio da viagem<\/a> tornam-se progressivamente melhores a cada vers\u00e3o lan\u00e7ada.<\/p>\n

No entanto, a decis\u00e3o sobre qual dos modelos subjacentes que estas plataformas utilizam \u00e9 o melhor tem sido largamente subjectiva e dif\u00edcil de aferir.<\/p>\n

Afirmar de forma generalizada que um modelo \u00e9 \"melhor\" do que outro n\u00e3o \u00e9 assim t\u00e3o simples. Diferentes modelos destacam-se em v\u00e1rios aspectos da gera\u00e7\u00e3o de imagens. Um pode ser bom na renderiza\u00e7\u00e3o de texto, enquanto outro pode ser melhor na intera\u00e7\u00e3o com objectos.<\/p>\n

Um dos principais desafios que os modelos T2I enfrentam \u00e9 o de seguir cada detalhe do pedido e fazer com que estes se reflictam com precis\u00e3o na imagem gerada.<\/p>\n

Com o Gecko, o DeepMind<\/span><\/span> os investigadores criaram um refer\u00eancia<\/a> que avalia as capacidades dos modelos T2I de forma semelhante \u00e0 dos humanos.<\/p>\n

Conjunto de compet\u00eancias<\/h2>\n

Os investigadores come\u00e7aram por definir um conjunto de dados abrangente de compet\u00eancias relevantes para a gera\u00e7\u00e3o de T2I. Estas incluem a compreens\u00e3o espacial, o reconhecimento de ac\u00e7\u00f5es, a interpreta\u00e7\u00e3o de textos, entre outras. Depois, dividiram-nas em subcompet\u00eancias mais espec\u00edficas.<\/p>\n

Por exemplo, na renderiza\u00e7\u00e3o de texto, as subcompet\u00eancias podem incluir a renderiza\u00e7\u00e3o de diferentes tipos de letra, cores ou tamanhos de texto.<\/p>\n

Em seguida, um LLM foi utilizado para gerar instru\u00e7\u00f5es para testar a capacidade do modelo T2I numa compet\u00eancia ou subcompet\u00eancia espec\u00edfica.<\/p>\n

Isto permite aos criadores de um modelo T2I identificar n\u00e3o s\u00f3 as compet\u00eancias que constituem um desafio, mas tamb\u00e9m o n\u00edvel de complexidade em que uma compet\u00eancia se torna um desafio para o seu modelo.<\/p>\n

\"\"
A estrutura de refer\u00eancia Gecko utiliza um conjunto de dados de compet\u00eancias e subcompet\u00eancias (a), pontua\u00e7\u00e3o humana Likert da precis\u00e3o da imagem (b), consultas geradas por LLM para an\u00e1lise VQA e resultados em m\u00e9tricas abrangentes que se correlacionam com avalia\u00e7\u00f5es humanas. Fonte: arXiv<\/figcaption><\/figure>\n

Avalia\u00e7\u00e3o humana vs. avalia\u00e7\u00e3o autom\u00e1tica<\/h2>\n

O Gecko tamb\u00e9m mede a precis\u00e3o com que um modelo T2I segue todos os pormenores de uma mensagem. Mais uma vez, foi utilizado um LLM para isolar os principais pormenores de cada mensagem de entrada e, em seguida, gerar um conjunto de perguntas relacionadas com esses pormenores.<\/p>\n

Estas perguntas podem ser simples e directas sobre elementos vis\u00edveis na imagem (por exemplo, \"H\u00e1 um gato na imagem?\") e perguntas mais complexas que testam a compreens\u00e3o da cena ou as rela\u00e7\u00f5es entre objectos (por exemplo, \"O gato est\u00e1 sentado por cima do livro?\").<\/p>\n

Em seguida, um modelo de resposta a perguntas visuais (VQA) analisa a imagem gerada e responde \u00e0s perguntas para verificar a exatid\u00e3o com que o modelo T2I alinha a sua imagem de sa\u00edda com um pedido de entrada.<\/p>\n

Os investigadores recolheram mais de 100.000 anota\u00e7\u00f5es humanas em que os participantes pontuavam uma imagem gerada com base no grau de alinhamento da imagem com crit\u00e9rios espec\u00edficos.<\/p>\n

Foi pedido aos humanos que considerassem um aspeto espec\u00edfico da mensagem de entrada e classificassem a imagem numa escala de 1 a 5, com base na sua adequa\u00e7\u00e3o \u00e0 mensagem.<\/p>\n

Utilizando as avalia\u00e7\u00f5es anotadas por humanos como padr\u00e3o de ouro, os investigadores conseguiram confirmar que a sua m\u00e9trica de avalia\u00e7\u00e3o autom\u00e1tica \"est\u00e1 mais bem correlacionada com as classifica\u00e7\u00f5es humanas do que as m\u00e9tricas existentes para o nosso novo conjunto de dados\".<\/p>\n

O resultado \u00e9 um sistema de avalia\u00e7\u00e3o comparativa capaz de atribuir n\u00fameros a factores espec\u00edficos que tornam uma imagem gerada boa ou m\u00e1.<\/p>\n

O Gecko classifica essencialmente a imagem de sa\u00edda de uma forma que se aproxima da forma como decidimos intuitivamente se estamos ou n\u00e3o satisfeitos com a imagem gerada.<\/p>\n

Ent\u00e3o, qual \u00e9 o melhor modelo de texto para imagem?<\/p>\n

Em o seu trabalho<\/a>Na sua investiga\u00e7\u00e3o, os investigadores conclu\u00edram que o modelo Muse da Google supera o Stable Diffusion 1.5 e o SDXL no benchmark Gecko. Podem ser tendenciosos, mas os n\u00fameros n\u00e3o mentem.<\/p>","protected":false},"excerpt":{"rendered":"

A DeepMind da Google lan\u00e7ou o Gecko, uma nova refer\u00eancia para avaliar exaustivamente os modelos de texto para imagem (T2I) da IA. Nos \u00faltimos dois anos, vimos geradores de imagens de IA como o DALL-E e o Midjourney tornarem-se progressivamente melhores a cada lan\u00e7amento de vers\u00e3o. No entanto, decidir qual dos modelos subjacentes que estas plataformas utilizam \u00e9 o melhor tem sido largamente subjetivo e dif\u00edcil de avaliar. Afirmar que um modelo \u00e9 \"melhor\" do que outro n\u00e3o \u00e9 assim t\u00e3o simples. Diferentes modelos destacam-se em v\u00e1rios aspectos da gera\u00e7\u00e3o de imagens. Um pode ser bom na renderiza\u00e7\u00e3o de texto, enquanto outro pode ser melhor na intera\u00e7\u00e3o com objectos. Um dos principais desafios dos modelos T2I<\/p>","protected":false},"author":6,"featured_media":11853,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[147,107,102,165],"class_list":["post-11850","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-deepmind","tag-generative-ai","tag-google","tag-image-generation"],"yoast_head":"\nGoogle\u2019s Gecko benchmark identifies best AI image generator | 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\/04\/googles-gecko-benchmark-identifies-best-ai-image-generator\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google\u2019s Gecko benchmark identifies best AI image generator | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Google\u2019s DeepMind released Gecko, a new benchmark for comprehensively evaluating AI text-to-image (T2I) models. 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