CVPR2016 有哪些值得关注的文章,求推荐!

我把Action Recognition相关的论文列在这里,最近在刷paper,有空再补充
0. Learning Action Maps of Large Environments via First-Person Vision.
1. Progressively Parsing Interactional Objects for Fine Grained Action Detection. 
2. Temporal Action Localization in Untrimmed Videos via Multi-Stage CNNs. 
3. What If We Do Not Have Multiple Videos of the Same Action? — Video Action Localization Using Web Images. 
4. 3D Action Recognition From Novel Viewpoints.
5. Convolutional Two-Stream Network Fusion for Video Action Recognition.
6. VLAD3: Encoding Dynamics of Deep Features for Action Recognition. 
7. A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection.
    使用multi-stream CNN和双向LSTM来提取动作表示,并且利用跟踪器来定位人,抑制背景噪声。
8. A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets. 
9. A Key Volume Mining Deep Framework for Action Recognition. 
10. First Person Action Recognition Using Deep Learned Descriptors. 
11. Mining 3D Key-Pose-Motifs for Action Recognition. 
12. Predicting the Where and What of Actors and Actions Through Online Action Localization.
13. End-To-End Learning of Action Detection From Frame Glimpses in Videos. 
14. Action Recognition in Video Using Sparse Coding and Relative Features. 
15. Improving Human Action Recognition by Non-Action Classification.
16. Real-Time Action Recognition With Enhanced Motion Vector CNNs. 
17. Dynamic Image Networks for Action Recognition.
18. Regularizing Long Short Term Memory With 3D Human-Skeleton Sequences for Action Recognition. 
19. Temporal Action Localization With Pyramid of Score Distribution Features. 
20. Temporal Action Detection Using a Statistical Language Model. 
21. Deep Region and Multi-Label Learning for Facial Action Unit Detection. 
22. Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection.
23. DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns. 
24. Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity. 

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