Guotian Zeng — accepted AI-conference papers

2026

Interactive Tracking: A Human-in-the-Loop Paradigm with Memory-Augmented Adaptation

Yuqing Huang, Guotian Zeng, Zhenqiao Yuan, Zhenyu He, Xin Li, Yaowei Wang +1

CVPR 2026

Existing visual trackers mainly operate in a non-interactive, fire-and-forget manner, making them impractical for real-world scenarios that require human-in-the-loop adaptation. To overcome this limitation, we introduce Interactive Tracking, a new paradigm that allows users to guide the tracker at a

Cited by 0 Source ↗code ↗Scholar ↗

2024

LiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking

Qingmao Wei, Bi Zeng, Jianqi Liu, Li He, Guotian Zeng

ICRA 2024poster

The recent advancements in transformer-based visual trackers have led to significant progress, attributed to their strong modeling capabilities. However, as performance improves, running latency correspondingly increases, presenting a challenge for real-time robotics applications, especially on edge…

Cited by 20 Source ↗code ↗Scholar ↗