Shuimu Zeng — accepted AI-conference papers

2026

Boosting Self-Supervised Tracking with Contextual Prompts and Noise Learning

Yaozong Zheng, Qihua Liang, Bineng Zhong, Shuimu Zeng, Yuanliang Xue, Ning Li +1

CVPR 2026

Learning robust contextual knowledge from unlabeled videos is essential for advancing self-supervised tracking. However, conventional self-supervised trackers lack effective context modeling, while existing context association methods based on non-semantic queries struggle to adapt to unlabeled trac

Cited by 0 Source ↗Scholar ↗

2026

Learning to Track Instance from Single Nature Language Description

Yaozong Zheng, Bineng Zhong, Qihua Liang, Shuimu Zeng, Haiying Xia, Shuxiang Song

CVPR 2026

How to achieve vision-language (VL) tracking using natural language descriptions from a video sequence without relying on any bounding-box ground truth? In this work, we achieve this goal by tackling self-supervised VL tracking, which aims to evaluate tracking capabilities guided by natural language

Cited by 0 Source ↗Scholar ↗