Update CVPR2021.md

CVPR2021.md

@@ -1193,6 +1193,9 @@
 
 ### 行为识别/动作识别/检测/分割/定位(Action/Activity Recognition)
 
+[22] Home Action Genome: Cooperative Compositional Action Understanding(家庭行动基因组：合作组成行动的理解)<br>
+[paper](https://arxiv.org/abs/2105.05226)<br><br>
+
 [21] Weakly Supervised Action Selection Learning in Video(视频中的弱监督动作选择学习)<br>
 [paper](https://arxiv.org/abs/2105.02439) | [code](https://github.com/layer6ai-labs/ASL)<br><br>
 
@@ -1959,6 +1962,9 @@
 
 ### 表征学习(Representation Learning)
 
+[15] Representation Learning via Global Temporal Alignment and Cycle-Consistency(通过全局时间对齐和周期一致性进行表示学习)<br>
+[paper](https://arxiv.org/abs/2105.05217)<br><br>
+
 [14] Multi-Perspective LSTM for Joint Visual Representation Learning(用于联合视觉表示学习的多视角LSTM)<br>
 [paper](https://arxiv.org/abs/2105.02802) | [code](https://github.com/arsm/MPLSTM)<br><br>
 
@@ -2414,6 +2420,9 @@
 
 ## 视觉推理/视觉问答(Visual Reasoning/VQA)
 
+[9] Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules(找到了我的理由？ 使用胶囊进行弱监督的地面视觉问答)<br>
+[paper](https://arxiv.org/abs/2105.04836)<br><br>
+
 [8] Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answering(通往答案的桥梁：用于视频问答的结构感知图交互网络)<br>
 [paper](https://arxiv.org/abs/2104.14085)<br><br>
 
@@ -3218,6 +3227,15 @@ Densely Coded Labels (DCL)是 Circular Smooth Label (CSL)的优化版本。DCL
 
 # 4. CVPR2021 Workshop
 
+[81] EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration(ESPN：用于模糊图像恢复的增强型深金字塔网络)<br>
+[paper](https://arxiv.org/abs/2105.04872)<br><br>
+
+[80] ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research(ChaLearn LAP大规模签名人独立的隔离手语识别挑战：设计，结果和未来研究)<br>
+[paper](https://arxiv.org/abs/2105.05066)<br><br>
+
+[79] Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment(重新考虑雷达的作用：通过坐标对齐的摄像机-雷达数据集和系统注释器)<br>
+[paper](https://arxiv.org/abs/2105.05207)<br><br>
+
 [78] Good Practices and A Strong Baseline for Traffic Anomaly Detection(【AI CITY第一名】良好做法和强大的交通异常检测基准)<br>
 [paper](https://arxiv.org/abs/2105.03827) | [code](https://github.com/Endeavour10020/AICity2021-Anomaly-Detection)<br>