論文閱讀筆記 SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-grained Recognition
這篇論文來自美國羅格斯大學的 Han Zhang， CVPR2016 1. 簡介 相比於一般的目標識別，細粒度識別具有更大的挑戰性。其原因是由於姿態與視角的不同，不同圖像之間微小的差異很容易被掩蓋。一個直觀的思路就是，利用目標的 part 去區分不同的子類。當前，一些 state-of-the-art 的方法都是利用 CNN 去檢測 目標的 part regions。 但這些方
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相關文章
- 1. 【論文閱讀筆記】Rich feature hierarchies for accurate object detection and semantic segmentation
- 2. （R-CNN）Rich feature hierarchies for accurate object detection and semantic segmentation論文閱讀筆記2014
- 3. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks論文閱讀筆記
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- 5. 論文筆記--Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
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- 8. 論文閱讀筆記（四）：Rich feature hierarchies for accurate object detection and semantic segmentation ( R-CNN )
- 9. 【論文閱讀】Rich feature hierarchies for accurate object detection and semantic segmentation
- 10. 《Learning Features and Parts for Fine-Grained Recognition》論文閱讀筆記