arXiv Computer Vision
RDANet: Relative Degradation Aware Network for Infrared Small Target Detection
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CoLR-Det: Collaborative Latent Restoration for Small Object Detection in Low-Resolution Remote Sensing Images
arXiv:2601. 12507v2 Announce Type: replace-cross Abstract: Low-resolution remote sensing small object detection is limited by both missing visual details and the ambiguity of how details serve detection.
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Boosting Infrared Small Target Detection via Logit-Domain Contrast and Adaptive Shape Refinement
Infrared small target detection (IRSTD) remains challenging due to tiny target size, low signal-to-noise ratio, severe foreground-background imbalance, and blurred boundaries in complex scenes. Existing methods usually rely on post-activation probability-domain supervision for discrimination, where weak targets and strong clutter may produce saturated and close probabilities, limiting weak-target discrimination.
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SANet: Selective Attention Network for Infrared Small Target Detection
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Efficient RGB-T Object Detection via Sparse Cross-Modality Fusion
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MI-DETR: A Strong Baseline for Moving Infrared Small Target Detection with Motion Integration
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ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection
ADGNet introduces an Asymmetric Dual-text Guided Network for infrared small target detection, addressing challenges of pixel-level methods and multimodal approaches that lack regional guidance. It employs an Asymmetric Dual-text Prompt (ADP) with an abstract target prompt and a detailed background prompt, and an Asymmetric Dual-Branch Interaction (ADBI) module to guide visual features separately, followed by an Adaptive Feature Aggregation (AFA) module for dynamic fusion. The authors also create an Asymmetric Image-Text Infrared (AITIR) dataset with asymmetric text annotations for three public datasets, and show that ADGNet outperforms 21 state‑of‑the‑art methods.