Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection

• URL: http://arxiv.org/abs/2507.02454v1
• Date: Thu, 03 Jul 2025 09:11:31 GMT
• Title: Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection
• Authors: Weiwei Duan, Luping Ji, Shengjia Chen, Sicheng Zhu, Jianghong Huang, Mao Ye,
• Abstract summary: Moving infrared small target detection faces huge challenges due to tiny target size and weak background contrast.<n>Currently, most existing methods are fully-supervised, heavily relying on a large number of manual target-wise annotations.<n>This paper proposes a new weakly-supervised contrastive learning (WeCoL) scheme, only requires simple target quantity prompts during model training.
• Score: 11.930404803127358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Different from general object detection, moving infrared small target detection faces huge challenges due to tiny target size and weak background contrast.Currently, most existing methods are fully-supervised, heavily relying on a large number of manual target-wise annotations. However, manually annotating video sequences is often expensive and time-consuming, especially for low-quality infrared frame images. Inspired by general object detection, non-fully supervised strategies ($e.g.$, weakly supervised) are believed to be potential in reducing annotation requirements. To break through traditional fully-supervised frameworks, as the first exploration work, this paper proposes a new weakly-supervised contrastive learning (WeCoL) scheme, only requires simple target quantity prompts during model training.Specifically, in our scheme, based on the pretrained segment anything model (SAM), a potential target mining strategy is designed to integrate target activation maps and multi-frame energy accumulation.Besides, contrastive learning is adopted to further improve the reliability of pseudo-labels, by calculating the similarity between positive and negative samples in feature subspace.Moreover, we propose a long-short term motion-aware learning scheme to simultaneously model the local motion patterns and global motion trajectory of small targets.The extensive experiments on two public datasets (DAUB and ITSDT-15K) verify that our weakly-supervised scheme could often outperform early fully-supervised methods. Even, its performance could reach over 90\% of state-of-the-art (SOTA) fully-supervised ones.

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