27 citations · 48 across the 4 of their papers we have counts for
12 papers
Video Annotation for Visual Tracking via Selection and Refinement
Kenan Dai, Jie Zhao, Lijun Wang +5
Deep learning based visual trackers entail offline pre-training on large volumes of video datasets with accurate bounding box annotations that are labor-expensive to achieve. We pr…
A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification
Xuehu Liu, Pingping Zhang, Chenyang Yu +3
Video-based person re-identification (Re-ID) aims to retrieve video sequences of the same person under non-overlapping cameras. Previous methods usually focus on limited views, suc…
Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification
Xuehu Liu, Pingping Zhang, Chenyang Yu +2
Video-based person re-identification (Re-ID) aims to automatically retrieve video sequences of the same person under non-overlapping cameras. To achieve this goal, it is the key to…
Transformer Tracking
Xin Chen, Bin Yan, Jiawen Zhu +3
Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the…
Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation
Bin Yan, Xinyu Zhang, Dong Wang +2
Visual object tracking aims to precisely estimate the bounding box for the given target, which is a challenging problem due to factors such as deformation and occlusion. Many recen…
Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking
Pengyu Zhang, Jie Zhao, Dong Wang +2
In this study, we propose a novel RGB-T tracking framework by jointly modeling both appearance and motion cues. First, to obtain a robust appearance model, we develop a novel late…