SCNet論文詳解：Improving Convolutional Networks with Self-calibrated Convolutions
《Improving Convolutional Networks with Self-calibrated Convolutions》是2020年CVPR的論文，作者來自於南開大學程明明團隊。最近各種卷積注意力組合的模塊工作層出不窮，性能漲點明顯，包括之前的Res2Net、李沐團隊的ResNeSt，應該是近期的熱點方向。 論文地址:http://mftp.mmcheng.net/Papers/2
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相關文章
- 1. Inverting Convolutional Networks with Convolutional Networks 論文理解
- 2. Improving Convolutional Networks with Self-Calibrated Convolutions 自卷積模塊
- 3. 論文精要解讀：Going Deeper with Convolutions
- 4. [論文] Aspect Based Sentiment Analysis with Gated Convolutional Networks
- 5. Inverting Visual Representations with Convolutional Networks論文理解
- 6. 【論文詳解】DPED：DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
- 7. Weakly supervised object recognition with convolutional neural networks 論文解讀
- 8. 論文ImageNet Classification with Deep Convolutional Neural Networks（Alexnet2012）
- 9. [論文閱讀]Language Modeling with Gated Convolutional Networks
- 10. 論文閱讀：EDVR: Video Restoration with Enhanced Deformable Convolutional Networks