Yun-Kai Dang

This work proposes a latency-aware metric for multi-object tracking called streaming-HOTA (s-HOTA), and introduces an ultra-High-resolution Airborne Remote-sensing Dataset (HARD) annotated at three levels for object detection, multi-object tracking, and scene-level visual question answering.

VLMs can achieve substantial improvements in reasoning performance using only unlabeled data through a novel self-reflective training framework inspired by human cognition.

Achieve state-of-the-art performance on ultra-high-resolution remote sensing tasks without the quadratic compute cost, thanks to a query-guided token compression strategy.

MLLMs can aggressively prune visual tokens without sacrificing performance by adapting token reduction strategies to specific classes and prompts.