《MUTAN: Multimodal Tucker Fusion for Visual Question Answering》閱讀筆記
《MUTAN: Multimodal Tucker Fusion for Visual Question Answering》閱讀筆記 一、研究背景 Bilinear models在視覺問答（VQA）任務中進行信息融合提供了一個吸引人的框架。 它們有助於學習question meaning 和 visual concepts in the image之間的高層次關係，但它們存在高維度問題。 論文引
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相關文章
- 1. 《Visual Question Generation as Dual Task of Visual Question Answering》閱讀筆記
- 2. MUREL: Multimodal Relational Reasoning for Visual Question Answering筆記
- 3. 閱讀筆記（Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding)
- 4. Multimodal Encoder-Decoder Attention Networks for Visual Question Answering論文筆記
- 5. Cycle-Consistency for Robust Visual Question Answering閱讀筆記
- 6. 閱讀筆記Dynamic Fusion with Intra- and Inter-modality Attention Flow for Visual Question Answering
- 7. 論文-《MUREL: Multimodal Relational Reasoning for Visual Question Answering Remi》重點翻譯+擴展
- 8. Multi-level Attention Networks for Visual Question Answering閱讀筆記
- 9. Don’t Just Assume； Look and Answer：Overcoming Priors for Visual Question Answering閱讀筆記
- 10. 《Multi-level Attention Networks for Visual Question Answering》閱讀筆記