Don’t Just Assume； Look and Answer：Overcoming Priors for Visual Question Answering閱讀筆記
許多研究發現，目前的視覺問題回答(VQA)模型在很大程度上是由訓練數據的表面相關性驅動的，缺乏足夠的圖像基礎。我們提出了一種新的VQA設置，即針對每種題型，訓練和測試集都有不同的答案先驗分佈。提出了VQA v1和VQA v2數據集的新分割，稱之爲改變先驗下的可視化問題回答(VQA-CP v1和VQA-CP v2)。 1、文章引入 先前的工作所研究的模型當面對一個困難的學習問題時，通常訴諸於鎖定訓練
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相關文章
- 1. Don’t Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
- 2. 閱讀筆記（Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding)
- 3. 《Visual Question Generation as Dual Task of Visual Question Answering》閱讀筆記
- 4. 《Multi-level Attention Networks for Visual Question Answering》閱讀筆記
- 5. 《MUTAN: Multimodal Tucker Fusion for Visual Question Answering》閱讀筆記
- 6. Cycle-Consistency for Robust Visual Question Answering閱讀筆記
- 7. Question-Guided Spatio-Temporal Contextual Attention for Video Question Answering 論文閱讀筆記
- 8. nips 208 visual question answering 導讀
- 9. Multi-level Attention Networks for Visual Question Answering閱讀筆記
- 10. Gated Self-Matching Networks for Reading Comprehension and Question Answering論文閱讀筆記