Yuqi Jiang
Papers
1
Total Citations
15
H-Index
1
About
Yuqi Jiang is a rising researcher in computer vision, whose work centers on advancing multi-object tracking (MOT) and object detection for dynamic, real-world environments. Their most-cited paper, "Siamese-DETR for Generic Multi-Object Tracking" (2024, 15 citations), tackles a critical limitation of traditional MOT: the inability to track objects beyond pre-defined closed-set categories. By integrating Siamese networks with the DETR transformer architecture, Jiang proposes a novel framework that enables generic, open-vocabulary tracking—detecting and following any dynamic object, from vehicles to novel entities, in scenes like autonomous driving and robot navigation. This contribution bridges the gap between detection and tracking, offering a more flexible and scalable solution for unconstrained environments. Though early in their career, Jiang's work has already garnered attention for its potential to reshape how machines perceive and interact with the world, marking them as a promising innovator in the field of visual perception.
Research Focus
Key Achievements
Top Papers
- 1Siamese-DETR for Generic Multi-Object Tracking15 citations · 2024