发布日期: 2025-07-05

更新日期: 2025-07-08

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2025-07-05 更新

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

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.

不同于一般的物体检测，移动红外小目标检测由于目标尺寸小和背景对比度弱而面临巨大挑战。目前，大多数现有方法都是全监督的，严重依赖于大量手动目标注释。然而，对视频序列进行手动注释通常成本高昂且耗时，尤其是针对低质量的红外帧图像。受一般物体检测的启发，非全监督策略（例如弱监督）被认为在减少注释要求方面具有潜力。为了突破传统的全监督框架，作为首次探索工作，本文提出了一种新的弱监督对比学习（WeCoL）方案，该方案仅在模型训练期间需要简单的目标数量提示。具体来说，在我们的方案中，基于预训练的分割任何模型（SAM），设计了一种潜在目标挖掘策略，以整合目标激活图和多帧能量积累。此外，通过计算特征子空间中正负样本的相似性，采用对比学习来进一步提高伪标签的可靠性。此外，我们提出了一种长短时运动感知学习方案，以同时建模小目标的局部运动模式和全局运动轨迹。在两个公共数据集（DAUB和ITSDT-15K）上的广泛实验证明，我们的弱监督方案通常可以超越早期的全监督方法。甚至，其性能可以达到最新（SOTA）全监督方法的90%以上。

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Summary

本文提出了一种新的弱监督对比学习（WeCoL）方案，用于红外移动小目标检测。该方案借鉴了一般目标检测的思想，采用非全监督策略（如弱监督），以减少对大量手动目标注释的需求。通过结合目标激活图和跨帧能量累积的潜在目标挖掘策略，利用预训练的分割任何模型（SAM）。同时采用对比学习来提高伪标签的可靠性，并通过计算特征子空间中的正负样本相似性来实现。此外，还提出了一种长短时运动感知学习方案，以同时建模小目标的局部运动模式和全局运动轨迹。在公共数据集上的实验表明，该弱监督方案的性能经常优于早期的全监督方法，甚至其性能超过先进的全监督方法的90%。

Key Takeaways