1 citations · 1 across the 2 of their papers we have counts for
5 papers
Understand Before Detect: Vision--Language Learning for Omni-Domain Infrared Small Target Detection
Haoyang Yuan, Boyang Li, Yingqian Wang +7
Omni-domain infrared small target (IRST) detection is crucial for infrared surveillance, yet remains challenging due to heterogeneous imaging domains and inconsistent target charac…
Event-based Tiny Object Detection: A Benchmark Dataset and Baseline
Nuo Chen, Chao Xiao, Yimian Dai +3
Small object detection (SOD) in anti-UAV task is a challenging problem due to the small size of UAVs and complex backgrounds. Traditional frame-based cameras struggle to detect sma…
Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better
Ruojing Li, Wei An, Yingqian Wang +6
Infrared small target (IRST) detection is challenging in simultaneously achieving precise, robust, and efficient performance due to extremely dim targets and strong interference. C…
From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervision
Chuang Yu, Jinmiao Zhao, Yunpeng Liu +3
Recently, single-frame infrared small target (SIRST) detection with single point supervision has drawn wide-spread attention. However, the latest label evolution with single point…
Towards Robust Infrared Small Target Detection: A Feature-Enhanced and Sensitivity-Tunable Framework
Jinmiao Zhao, Zelin Shi, Chuang Yu +2
Recently, single-frame infrared small target (SIRST) detection technology has attracted widespread attention. Different from most existing deep learning-based methods that focus on…