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

Different from general object detection, moving infrared small target detection faces huge challenges due to tiny target size and weak backgroundthis http URL, 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 modelthis http URL, 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 energythis http URL, contrastive learning is adopted to further improve the reliability of pseudo-labels, by calculating the similarity between positive and negative samples in featurethis http URL, we propose a long-short term motion-aware learning scheme to simultaneously model the local motion patterns and global motion trajectory of smallthis http URLextensive 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.

@article{duan2025_2507.02454,
  title={ Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection },
  author={ Weiwei Duan and Luping Ji and Shengjia Chen and Sicheng Zhu and Jianghong Huang and Mao Ye },
  journal={arXiv preprint arXiv:2507.02454},
  year={ 2025 }
}