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CVPR2017: Learning Deep Context-aware Features over Body and Latent Parts for
時間 2020-12-30
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論文查看 作者是中科院的Dangwei Li等。這篇工作是multi-class person identification tasks,主要創新有三點:(1)用空洞卷積(dilated conv)進行多尺度特徵提取,減少傳統CNN提取特徵的信息損失;(2)利用Spatial Transformer Networks (STN,其中作者設置了三個參數限制) 提取可變的body-part,相比較於r
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相關文章
1.
Learning Deep Features for Discriminative Localization
2.
《Learning Features and Parts for Fine-Grained Recognition》論文閱讀筆記
3.
CVPR2017 tutorial: 3D Deep Learning
4.
Comparison of SIFT Encoded and Deep Learning Features for the Classification and Detection of Esca D
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