123 citations · 202 across the 7 of their papers we have counts for
7 papers
Convolutional Temporal Attention Model for Video-based Person Re-identification
Tanzila Rahman, Mrigank Rochan, Yang Wang
The goal of video-based person re-identification is to match two input videos, so that the distance of the two videos is small if two videos contain the same person. A common appro…
Cross-Modal Self-Attention Network for Referring Image Segmentation
Linwei Ye, Mrigank Rochan, Zhi Liu +1
We consider the problem of referring image segmentation. Given an input image and a natural language expression, the goal is to segment the object referred by the language expressi…
Cross-Entropy Adversarial View Adaptation for Person Re-identification
Lin Wu, Richang Hong, Yang Wang +1
Person re-identification (re-ID) is a task of matching pedestrians under disjoint camera views. To recognise paired snapshots, it has to cope with large cross-view variations cause…
3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification
Lin Wu, Yang Wang, Ling Shao +1
In this paper, we introduce a global video representation to video-based person re-identification (re-ID) that aggregates local 3D features across the entire video extent. Most of…
Video-based Person Re-identification Using Spatial-Temporal Attention Networks
Shivansh Rao, Tanzila Rahman, Mrigank Rochan +1
We consider the problem of video-based person re-identification. The goal is to identify a person from videos captured under different cameras. In this paper, we propose an efficie…
LEGO: Learning Edge with Geometry all at Once by Watching Videos
Zhenheng Yang, Peng Wang, Yang Wang +2
Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network is attracting significant attention. In this paper, we introduce a "3…
Personalized Video Recommendation Using Rich Contents from Videos
Xingzhong Du, Hongzhi Yin, Ling Chen +3
Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommend…