Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering簡介
本篇文章介紹的VQAv2是人工標註的開放式問答數據集，通過收集互補的圖像來平衡目前的VQA數據集，針對視覺問題回答(VQA)任務的語言偏見，使視覺(VQA中的V)變得重要，相較於VQAv1儘量減少了語言偏見（爲每個問題補充了圖片）。 同時，本文提出一個新的用於識別互補圖像的數據收集模型，該模型除了爲給定的(圖像、問題)對提供答案外，還提供基於反例的解釋。具體來說，它識別出與原始圖像相似的圖像，但它
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相關文章
- 1. nips 208 visual question answering 導讀
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