Huiwei Shi
Papers
1
Total Citations
4
H-Index
1
About
Huiwei Shi is a researcher advancing the frontiers of visual object tracking, with a particular focus on RGBT (RGB-Thermal) fusion and temporal modeling. Their most-cited work, "IAMTrack: interframe appearance and modality tokens propagation with temporal modeling for RGBT tracking" (2025), introduces a novel framework that propagates appearance and modality tokens across frames, enabling robust tracking under challenging conditions like low light or occlusion. This contribution addresses a critical gap in multi-modal tracking by leveraging temporal coherence, achieving state-of-the-art performance on benchmark datasets. With 4 citations already in its early publication year, IAMTrack signals growing influence in the computer vision community. Shi’s research bridges deep learning and sensor fusion, offering practical solutions for autonomous systems and surveillance. Their work exemplifies how integrating temporal dynamics with multi-modal data can push the boundaries of real-time tracking, making them a rising voice in this specialized field.
Research Focus
Key Achievements
Top Papers
- 1