Language-supervised pretraining on a new multimodal infrared dataset improves infrared small-target detection and cross-domain generalization compared with prior IRST detectors.
The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results
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abstract
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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cs.CV 1years
2026 1verdicts
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Understand Before Detect: Vision--Language Learning for Omni-Domain Infrared Small Target Detection
Language-supervised pretraining on a new multimodal infrared dataset improves infrared small-target detection and cross-domain generalization compared with prior IRST detectors.