Rethinking Representations for Cross-Domain Infrared Small Target Detection: A Generalizable Perspective from the Frequency Domain

2026-04-02

AI Summary

This work addresses the challenge of infrared small target detection under cross-domain scenarios, where environmental variations induce distribution shifts that severely degrade the generalization of existing methods. The study is the first to identify spectral phase inconsistency as a key signature of domain discrepancy and proposes a Spatial–Spectral Collaborative Perception Network (S²CPNet) to tackle this issue. S²CPNet incorporates a Phase Rectification Module (PRM) to enable generalized and accurate target perception, while integrating an Orthogonal Attention Mechanism (OAM) and Selective Style Recombination (SSR) to effectively suppress domain-specific overfitting. Evaluated across multiple cross-domain settings on three standard datasets, the proposed method consistently outperforms state-of-the-art approaches, achieving the best reported performance to date.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

The accurate target-background separation in infrared small target detection (IRSTD) highly depends on the discriminability of extracted representations. However, most existing methods are confined to domain-consistent settings, while overlooking whether such discriminability can generalize to unseen domains. In practice, distribution shifts between training and testing data are inevitable due to variations in observational conditions and environmental factors. Meanwhile, the intrinsic indistinctiveness of infrared small targets aggravates overfitting to domain-specific patterns. Consequently, the detection performance of models trained on source domains can be severely degraded when deployed in unseen domains. To address this challenge, we propose a spatial-spectral collaborative perception network (S$^2$CPNet) for cross-domain IRSTD. Moving beyond conventional spatial learning pipelines, we rethink IRSTD representations from a frequency perspective and reveal inconsistencies in spectral phase as the primary manifestation of domain discrepancies. Based on this insight, we develop a phase rectification module (PRM) to derive generalizable target awareness. Then, we employ an orthogonal attention mechanism (OAM) in skip connections to preserve positional information while refining informative representations. Moreover, the bias toward domain-specific patterns is further mitigated through selective style recomposition (SSR). Extensive experiments have been conducted on three IRSTD datasets, and the proposed method consistently achieves state-of-the-art performance under diverse cross-domain settings.

Problem

• infrared small target detection
• cross-domain
• domain generalization
• distribution shift
• representation discriminability

Innovation

• frequency domain
• phase rectification
• cross-domain generalization
• orthogonal attention
• infrared small target detection