《Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for VQA》讀後感想
跟隨attetion工作閱讀該文 摘要 該文給我帶來的思考首先於注意力的使用，它採用的co-attention結構，注意力函數類似於attention is all you need 類似採用多層尺度乘法注意，第二它採用拉伸的特徵向量表示句子或圖像。 模型結構 如圖所示，Q 和V分別是圖像和問句的特徵表示，大小爲d×T， d×N，T 爲特徵圖展平後長度，N爲句子長度。該模型共有L個堆疊的Dense
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相關文章
- 1. Visual Genome ：Connecting Language and Vision Using Crowdsourced Dense Image Annotations.
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- 4. PROGRESSIVE GROWING OF GANs for IMPROVED QUALITY, STABILITY AND VARIATION
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- 8. Distributed Representations of Sentences and Documents閱讀筆記
- 9. [解讀] Deep Clustering for Unsupervised Learning of Visual Features
- 10. A Joint Optimization Approach of LiDAR-Camera Fusion for Accurate Dense 3-D Reconstructions