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The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results

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arxiv 2408.09615 v1 pith:IE2DLP2L submitted 2024-08-18 cs.CV

Boyang Li , Xinyi Ying , Ruojing Li , Yongxian Liu , Yangsi Shi , Miao Li This is my paper · ORCID
classification cs.CV
keywords detectioninfraredsmalltargetcompetitiontrackfirstmethods
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In this paper, we briefly summarize the first competition on resource-limited infrared small target detection (namely, LimitIRSTD). This competition has two tracks, including weakly-supervised infrared small target detection (Track 1) and lightweight infrared small target detection (Track 2). 46 and 60 teams successfully registered and took part in Tracks 1 and Track 2, respectively. The top-performing methods and their results in each track are described with details. This competition inspires the community to explore the tough problems in the application of infrared small target detection, and ultimately promote the deployment of this technology under limited resource.

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  1. Understand Before Detect: Vision--Language Learning for Omni-Domain Infrared Small Target Detection

    cs.CV 2026-08 conditional novelty 5.0 of 10

    Language-supervised pretraining on a new multimodal infrared dataset improves infrared small-target detection and cross-domain generalization compared with prior IRST detectors.

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