385 citations · 1.1k across the 14 of their papers we have counts for
14 papers
Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos
Junhao Zhang, Yali Wang, Zhipeng Zhou +3
Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos. However, it is often built on the fixed human-joint affinity, according to human…
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation
Zhe Wang, Hao Chen, Xinyu Li +4
Temporal action segmentation is a task to classify each frame in the video with an action label. However, it is quite expensive to annotate every frame in a large corpus of videos…
PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos
Tianyu Luan, Yali Wang, Junhao Zhang +3
The end-to-end Human Mesh Recovery (HMR) approach has been successfully used for 3D body reconstruction. However, most HMR-based frameworks reconstruct human body by directly learn…
Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation
Zhe Wang, Daeyun Shin, Charless C. Fowlkes
Monocular estimation of 3d human pose has attracted increased attention with the availability of large ground-truth motion capture datasets. However, the diversity of training data…
Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering
Zhe Wang, Xiaoyi Liu, Liangjian Chen +4
Visual question answering (VQA) is of significant interest due to its potential to be a strong test of image understanding systems and to probe the connection between language and…
Temporal Segment Networks for Action Recognition in Videos
Limin Wang, Yuanjun Xiong, Zhe Wang +4
Deep convolutional networks have achieved great success for image recognition. However, for action recognition in videos, their advantage over traditional methods is not so evident…
Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images
Limin Wang, Zhe Wang, Yu Qiao +1
Event recognition in still images is an intriguing problem and has potential for real applications. This paper addresses the problem of event recognition by proposing a convolution…
Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition
Zhe Wang, Limin Wang, Yali Wang +2
Traditional feature encoding scheme (e.g., Fisher vector) with local descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are two classes of successful methods…
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Limin Wang, Yuanjun Xiong, Zhe Wang +4
Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is…
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016
Yuanjun Xiong, Limin Wang, Zhe Wang +7
This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016. We follow the basic pipeline of temporal segm…
Real-time Action Recognition with Enhanced Motion Vector CNNs
Bowen Zhang, Limin Wang, Zhe Wang +2
The deep two-stream architecture exhibited excellent performance on video based action recognition. The most computationally expensive step in this approach comes from the calculat…
Better Exploiting OS-CNNs for Better Event Recognition in Images
Limin Wang, Zhe Wang, Sheng Guo +1
Event recognition from still images is one of the most important problems for image understanding. However, compared with object recognition and scene recognition, event recognitio…
Towards Good Practices for Very Deep Two-Stream ConvNets
Limin Wang, Yuanjun Xiong, Zhe Wang +1
Deep convolutional networks have achieved great success for object recognition in still images. However, for action recognition in videos, the improvement of deep convolutional net…
Object-Scene Convolutional Neural Networks for Event Recognition in Images
Limin Wang, Zhe Wang, Wenbin Du +1
Event recognition from still images is of great importance for image understanding. However, compared with event recognition in videos, there are much fewer research works on event…