CMU、Meta聯手放大招！ VQAScore一個問題搞定文生圖模型評測，準確性遠超傳統方法！
生成式AI發展迅猛，但如何全面評估其性能一直是個難題。各種模型層出不窮，效果也是越來越驚豔。但是，問題來了，怎麼評價這些文生圖模型的效果呢?傳統的評價方法，要麼是靠人眼看，主觀性太強;要麼是用一些簡單的指標，比如CLIPScore，但這些指標往往無法捕捉到複雜文本提示中的細節，比如對象之間的關係、邏輯推理等等。這就導致很多文生圖模型的評測結果不準確，甚至會出現一些搞笑的情況，明明生成的圖片驢脣不對馬嘴，得分卻還挺高。爲了解決這個問題，卡耐基梅隆大學
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