{"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\/it\/2024\/04\/googles-gecko-benchmark-identifies-best-ai-image-generator\/","title":{"rendered":"Il benchmark Gecko di Google individua il miglior generatore di immagini AI"},"content":{"rendered":"

Google DeepMind<\/span><\/span> ha rilasciato Gecko, un nuovo benchmark per la valutazione completa dei modelli AI text-to-image (T2I).<\/strong><\/p>\n

Negli ultimi due anni, abbiamo visto generatori di immagini AI come DALL-E<\/span><\/span><\/a> e Viaggio intermedio<\/a> migliorano progressivamente con ogni versione rilasciata.<\/p>\n

Tuttavia, decidere quale dei modelli sottostanti utilizzati da queste piattaforme sia il migliore \u00e8 stato in gran parte soggettivo e difficile da valutare.<\/p>\n

Affermare che un modello \u00e8 \"migliore\" di un altro non \u00e8 cos\u00ec semplice. Modelli diversi eccellono in vari aspetti della generazione di immagini. Uno pu\u00f2 essere bravo nel rendering del testo, mentre un altro pu\u00f2 essere migliore nell'interazione con gli oggetti.<\/p>\n

Una sfida fondamentale che i modelli T2I devono affrontare \u00e8 quella di seguire ogni dettaglio nel prompt e di rifletterlo accuratamente nell'immagine generata.<\/p>\n

Con Gecko, il DeepMind<\/span><\/span> ricercatori hanno creato un parametro di riferimento<\/a> che valuta le capacit\u00e0 dei modelli T2I in modo simile a come fanno gli esseri umani.<\/p>\n

Set di competenze<\/h2>\n

I ricercatori hanno innanzitutto definito un insieme completo di competenze rilevanti per la generazione di T2I. Queste includono la comprensione dello spazio, il riconoscimento delle azioni, la resa del testo e altre. Hanno poi suddiviso queste abilit\u00e0 in sotto-abilit\u00e0 pi\u00f9 specifiche.<\/p>\n

Ad esempio, alla voce rendering del testo, le sottocompetenze possono includere il rendering di diversi font, colori o dimensioni del testo.<\/p>\n

Un LLM \u00e8 stato poi utilizzato per generare prompt per testare la capacit\u00e0 del modello T2I su una specifica abilit\u00e0 o sub-abilit\u00e0.<\/p>\n

Ci\u00f2 consente ai creatori di un modello T2I di individuare non solo quali competenze sono impegnative, ma anche a quale livello di complessit\u00e0 una competenza diventa impegnativa per il loro modello.<\/p>\n

\"\"
Il framework di benchmark Gecko utilizza un set di dati di competenze e sottocompetenze (a), un punteggio Likert umano per l'accuratezza delle immagini (b), query generate da LLM per l'analisi VQA e risultati in metriche complete che correlano con le valutazioni umane. Fonte: arXiv<\/figcaption><\/figure>\n

Valutazione umana e automatica<\/h2>\n

Gecko misura anche la precisione con cui un modello T2I segue tutti i dettagli di una richiesta. Anche in questo caso, \u00e8 stato utilizzato un LLM per isolare i dettagli chiave di ogni richiesta di input e quindi generare una serie di domande relative a tali dettagli.<\/p>\n

Queste domande possono essere sia semplici e dirette sugli elementi visibili nell'immagine (ad esempio, \"C'\u00e8 un gatto nell'immagine?\"), sia pi\u00f9 complesse che verificano la comprensione della scena o le relazioni tra gli oggetti (ad esempio, \"Il gatto \u00e8 seduto sopra il libro?\").<\/p>\n

Un modello di Visual Question Answering (VQA) analizza quindi l'immagine generata e risponde alle domande per verificare l'accuratezza con cui il modello T2I allinea l'immagine di output a una richiesta di input.<\/p>\n

I ricercatori hanno raccolto oltre 100.000 annotazioni umane in cui i partecipanti assegnavano un punteggio a un'immagine generata in base all'allineamento dell'immagine a criteri specifici.<\/p>\n

Agli esseri umani \u00e8 stato chiesto di considerare un aspetto specifico della richiesta di input e di assegnare un punteggio all'immagine su una scala da 1 a 5, in base a quanto fosse in linea con la richiesta.<\/p>\n

Utilizzando le valutazioni annotate dall'uomo come gold standard, i ricercatori hanno potuto confermare che la loro metrica di autovalutazione \"\u00e8 meglio correlata alle valutazioni umane rispetto alle metriche esistenti per il nostro nuovo set di dati\".<\/p>\n

Il risultato \u00e8 un sistema di benchmarking in grado di attribuire numeri a fattori specifici che rendono buona o meno un'immagine generata.<\/p>\n

Gecko assegna essenzialmente un punteggio all'immagine in uscita in modo molto simile a come noi decidiamo intuitivamente se siamo soddisfatti o meno dell'immagine generata.<\/p>\n

Qual \u00e8 quindi il miglior modello text-to-image?<\/p>\n

In la loro carta<\/a>I ricercatori hanno concluso che il modello Muse di Google batte Stable Diffusion 1.5 e SDXL nel benchmark Gecko. Forse sono di parte, ma i numeri non mentono.<\/p>","protected":false},"excerpt":{"rendered":"

DeepMind di Google ha rilasciato Gecko, un nuovo benchmark per la valutazione completa dei modelli AI text-to-image (T2I). Negli ultimi due anni, abbiamo visto generatori di immagini AI come DALL-E e Midjourney migliorare progressivamente con ogni versione rilasciata. Tuttavia, decidere quale dei modelli sottostanti utilizzati da queste piattaforme sia il migliore \u00e8 stato in gran parte soggettivo e difficile da valutare. Affermare che un modello \u00e8 \"migliore\" di un altro non \u00e8 cos\u00ec semplice. Modelli diversi eccellono in vari aspetti della generazione di immagini. Uno pu\u00f2 essere bravo nel rendering del testo, mentre un altro pu\u00f2 essere migliore nell'interazione con gli oggetti. Una sfida fondamentale per i modelli 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\/it\/2024\/04\/googles-gecko-benchmark-identifies-best-ai-image-generator\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\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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