{"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\/fr\/2024\/04\/googles-gecko-benchmark-identifies-best-ai-image-generator\/","title":{"rendered":"Le test Gecko de Google identifie le meilleur g\u00e9n\u00e9rateur d'images par IA"},"content":{"rendered":"

Google's DeepMind<\/span><\/span> a publi\u00e9 Gecko, une nouvelle r\u00e9f\u00e9rence pour l'\u00e9valuation compl\u00e8te des mod\u00e8les de conversion texte-image (T2I) de l'IA.<\/strong><\/p>\n

Au cours des deux derni\u00e8res ann\u00e9es, nous avons vu des g\u00e9n\u00e9rateurs d'images d'IA tels que DALL-E<\/span><\/span><\/a> et Voyage \u00e0 mi-parcours<\/a> s'am\u00e9liorent progressivement au fil des versions.<\/p>\n

Toutefois, le choix du meilleur mod\u00e8le sous-jacent utilis\u00e9 par ces plateformes est largement subjectif et difficile \u00e0 \u00e9valuer.<\/p>\n

Il n'est pas si simple d'affirmer qu'un mod\u00e8le est \"meilleur\" qu'un autre. Diff\u00e9rents mod\u00e8les excellent dans divers aspects de la g\u00e9n\u00e9ration d'images. L'un d'entre eux peut \u00eatre bon pour le rendu de texte, tandis qu'un autre peut \u00eatre meilleur pour l'interaction avec les objets.<\/p>\n

L'un des principaux d\u00e9fis auxquels sont confront\u00e9s les mod\u00e8les T2I est de suivre chaque d\u00e9tail de l'invite et de le refl\u00e9ter avec pr\u00e9cision dans l'image g\u00e9n\u00e9r\u00e9e.<\/p>\n

Avec Gecko, le DeepMind<\/span><\/span> Les chercheurs ont cr\u00e9\u00e9 un rep\u00e8re<\/a> qui \u00e9value les capacit\u00e9s des mod\u00e8les T2I comme le font les humains.<\/p>\n

Comp\u00e9tences<\/h2>\n

Les chercheurs ont d'abord d\u00e9fini un ensemble complet de comp\u00e9tences pertinentes pour la g\u00e9n\u00e9ration de T2I. Il s'agit notamment de la compr\u00e9hension spatiale, de la reconnaissance d'actions et de la restitution de textes. Ils ont ensuite d\u00e9compos\u00e9 ces comp\u00e9tences en sous-comp\u00e9tences plus sp\u00e9cifiques.<\/p>\n

Par exemple, dans le cadre du rendu de texte, les sous-comp\u00e9tences peuvent inclure le rendu de diff\u00e9rentes polices, couleurs ou tailles de texte.<\/p>\n

Un LLM a ensuite \u00e9t\u00e9 utilis\u00e9 pour g\u00e9n\u00e9rer des invites afin de tester la capacit\u00e9 du mod\u00e8le T2I sur une comp\u00e9tence ou une sous-comp\u00e9tence sp\u00e9cifique.<\/p>\n

Cela permet aux cr\u00e9ateurs d'un mod\u00e8le T2I d'identifier non seulement les comp\u00e9tences qui posent probl\u00e8me, mais aussi le niveau de complexit\u00e9 auquel une comp\u00e9tence devient probl\u00e9matique pour leur mod\u00e8le.<\/p>\n

\"\"
Le cadre de r\u00e9f\u00e9rence Gecko utilise un ensemble de donn\u00e9es de comp\u00e9tences et de sous-comp\u00e9tences (a), une notation Likert humaine de la pr\u00e9cision des images (b), des requ\u00eates g\u00e9n\u00e9r\u00e9es par LLM pour l'analyse VQA, et des r\u00e9sultats dans des m\u00e9triques compl\u00e8tes qui sont en corr\u00e9lation avec les \u00e9valuations humaines. Source : arXiv<\/figcaption><\/figure>\n

\u00c9valuer l'humain par rapport \u00e0 l'auto<\/h2>\n

Gecko mesure \u00e9galement la pr\u00e9cision avec laquelle un mod\u00e8le T2I suit tous les d\u00e9tails d'une invite. L\u00e0 encore, un LLM a \u00e9t\u00e9 utilis\u00e9 pour isoler les d\u00e9tails cl\u00e9s de chaque message d'entr\u00e9e, puis pour g\u00e9n\u00e9rer un ensemble de questions li\u00e9es \u00e0 ces d\u00e9tails.<\/p>\n

Il peut s'agir de questions simples et directes sur les \u00e9l\u00e9ments visibles de l'image (par exemple, \"Y a-t-il un chat sur l'image ?\") ou de questions plus complexes qui testent la compr\u00e9hension de la sc\u00e8ne ou les relations entre les objets (par exemple, \"Le chat est-il assis au-dessus du livre ?\").<\/p>\n

Un mod\u00e8le de r\u00e9ponse aux questions visuelles (VQA) analyse ensuite l'image g\u00e9n\u00e9r\u00e9e et r\u00e9pond aux questions pour voir avec quelle pr\u00e9cision le mod\u00e8le T2I aligne son image de sortie sur une invite d'entr\u00e9e.<\/p>\n

Les chercheurs ont recueilli plus de 100 000 annotations humaines dans lesquelles les participants notaient une image g\u00e9n\u00e9r\u00e9e en fonction du degr\u00e9 d'alignement de l'image sur des crit\u00e8res sp\u00e9cifiques.<\/p>\n

Les humains ont \u00e9t\u00e9 invit\u00e9s \u00e0 prendre en compte un aspect sp\u00e9cifique de l'invite et \u00e0 noter l'image sur une \u00e9chelle de 1 \u00e0 5 en fonction de son ad\u00e9quation avec l'invite.<\/p>\n

En utilisant les \u00e9valuations annot\u00e9es par des humains comme \u00e9talon-or, les chercheurs ont pu confirmer que leur m\u00e9trique d'\u00e9valuation automatique \"est mieux corr\u00e9l\u00e9e avec les \u00e9valuations humaines que les m\u00e9triques existantes pour notre nouvel ensemble de donn\u00e9es\".<\/p>\n

Le r\u00e9sultat est un syst\u00e8me d'\u00e9talonnage capable de chiffrer les facteurs sp\u00e9cifiques qui font qu'une image g\u00e9n\u00e9r\u00e9e est bonne ou non.<\/p>\n

Gecko \u00e9value essentiellement l'image de sortie d'une mani\u00e8re qui correspond \u00e9troitement \u00e0 la fa\u00e7on dont nous d\u00e9cidons intuitivement si nous sommes satisfaits ou non de l'image g\u00e9n\u00e9r\u00e9e.<\/p>\n

Quel est donc le meilleur mod\u00e8le de conversion du texte en image ?<\/p>\n

En leur document<\/a>Les chercheurs ont conclu que le mod\u00e8le Muse de Google surpasse Stable Diffusion 1.5 et SDXL sur le benchmark Gecko. Ils sont peut-\u00eatre partiaux, mais les chiffres ne mentent pas.<\/p>","protected":false},"excerpt":{"rendered":"

DeepMind de Google a publi\u00e9 Gecko, une nouvelle r\u00e9f\u00e9rence pour \u00e9valuer de mani\u00e8re exhaustive les mod\u00e8les de conversion texte-image (T2I) de l'IA. Au cours des deux derni\u00e8res ann\u00e9es, nous avons vu des g\u00e9n\u00e9rateurs d'images d'IA comme DALL-E et Midjourney s'am\u00e9liorer progressivement \u00e0 chaque version. Cependant, la d\u00e9termination des meilleurs mod\u00e8les sous-jacents utilis\u00e9s par ces plateformes est largement subjective et difficile \u00e0 \u00e9valuer. Il n'est pas si simple d'affirmer qu'un mod\u00e8le est \"meilleur\" qu'un autre. Diff\u00e9rents mod\u00e8les excellent dans divers aspects de la g\u00e9n\u00e9ration d'images. L'un d'entre eux peut \u00eatre bon pour le rendu de texte, tandis qu'un autre peut \u00eatre meilleur pour l'interaction avec les objets. L'un des principaux d\u00e9fis que doivent relever les mod\u00e8les 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\/fr\/2024\/04\/googles-gecko-benchmark-identifies-best-ai-image-generator\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\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. Over the last two years, we\u2019ve seen AI image generators like DALL-E and Midjourney become progressively better with each version release. However, deciding which of the underlying models these platforms use is best has been largely subjective and difficult to benchmark. To make a broad claim that one model is \u201cbetter\u201d than another isn\u2019t so simple. Different models excel in various aspects of image generation. One may be good at text rendering while another may be better at object interaction. 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