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

TL;DR

This paper introduces a weakly-supervised contrastive learning approach for moving infrared small target detection, reducing annotation effort while achieving near state-of-the-art performance through innovative target mining and motion modeling techniques.

Contribution

It pioneers a weakly-supervised framework using quantity prompts and contrastive learning for infrared small target detection, reducing reliance on manual annotations.

Findings

Outperforms early fully-supervised methods on public datasets.

Achieves over 90% of state-of-the-art fully-supervised performance.

Demonstrates effectiveness of weak supervision in infrared small target detection.

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.