arXiv Computer Vision
1d ago

If It Moves, Radar Knows: A Physics-Aware Radar Transformer for Class-Agnostic Moving-Object Detection

The paper introduces the Physics-Aware Radar Transformer (PART), a radar-only detector that predicts moving-object existence, surface points, and ground-plane velocity using Doppler-aware query initialization and physics-guided cross-attention. PART achieves high class-agnostic performance on the nuScenes dataset, excelling in rare categories and adverse conditions such as night, rain, and occlusion. The model is lightweight, with only 1.1 million parameters, and its code and pretrained weights will be released publicly.

By Yinghao Sun, Shuguang Li, Jinliang Shao, Tieshan Li
arXiv Machine Learning
Jul 9

Generalist Vision-Language Models for Fast Radio Burst detection: a zero-shot benchmark against a specialized detector

arXiv:2607. 07382v1 Announce Type: new Abstract: Fast Radio Bursts (FRBs) are millisecond-duration radio transients whose automated detection increasingly relies on highly specialized deep learning models.

By Raiff H. Santos, Amilcar R. Queiroz, Tharcisyo S. S. Duarte, K. E. L. de Farias, Rafael A. Batista