2 citations · 2 across the 5 of their papers we have counts for
5 papers
Correlation-Embedded Transformer Tracking: A Single-Branch Framework
Fei Xie, Wankou Yang, Chunyu Wang +4
Developing robust and discriminative appearance models has been a long-standing research challenge in visual object tracking. In the prevalent Siamese-based paradigm, the features…
Collaborative Attention Memory Network for Video Object Segmentation
Zhixing Huang, Junli Zha, Fei Xie +3
Semi-supervised video object segmentation is a fundamental yet Challenging task in computer vision. Embedding matching based CFBI series networks have achieved promising results by…
Correlation-Aware Deep Tracking
Fei Xie, Chunyu Wang, Guangting Wang +3
Robustness and discrimination power are two fundamental requirements in visual object tracking. In most tracking paradigms, we find that the features extracted by the popular Siame…
Learning Tracking Representations via Dual-Branch Fully Transformer Networks
Fei Xie, Chunyu Wang, Guangting Wang +2
We present a Siamese-like Dual-branch network based on solely Transformers for tracking. Given a template and a search image, we divide them into non-overlapping patches and extrac…
Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking
Fei Xie, Wankou Yang, Bo Liu +3
Existing visual object tracking usually learns a bounding-box based template to match the targets across frames, which cannot accurately learn a pixel-wise representation, thereby…