論文筆記: (compact) Bilinear Pooling, Confusion
Learn by attention, learn with confusion. 先引用幾段魏秀參大神的論述「見微知著」——細粒度圖像分析進展綜述 細粒度物體的差異僅體現在細微之處。如何有效地對前景對象進行檢測，並從中發現重要的局部區域信息，成爲了細粒度圖像分類算法要解決的關鍵問題。對細粒度分類模型，可以按照其使用的監督信息的多少，分爲「基於強監督信息的分類模型」和「基於弱監督信息的分類模型」兩
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相關文章
- 1. VQA 之 Multimodal Compact Bilinear Pooling
- 2. 【Bilinear Pooling】《A Novel DR Classfication Scheme based on Compact Bilinear Pooling CNN and GBDT》
- 3. 閱讀筆記（Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding)
- 4. Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition論文筆記
- 5. 多模態融合算法——Multimodal Compact Bilinear Pooling
- 6. Semantic Segmentation with Second-Order Pooling論文筆記
- 7. 《Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering》筆記
- 8. 論文筆記：Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
- 9. FGVC---Bilinear Pooling (雙線性池化)及其發展
- 10. [論文筆記]Deep Domain Confusion: Maximizing for Domain Invariance