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.
arXiv:2608.20870v1 Announce Type: new Abstract: Infrared small target detection is still challenging in remote sensing imagery, because the targets are extremely small, exhibit weak local contrast, a...
arXiv:2608. 05771v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) has achieved substantial progress under domain-consistent evaluation, yet detector performance often degrades markedly when generalizing to unseen infrared domains.
arXiv:2607. 17148v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) commonly relies on pixel-level mask supervision.
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.
arXiv:2603.05071v2 Announce Type: replace Abstract: Detecting moving infrared small targets is challenging because tiny, low-contrast targets occupy few pixels and are easily obscured by dynamic back...
arXiv:2607. 04603v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) aims to identify long distance small targets from complex infrared backgrounds, and is a fundamental task in remote sensing.
arXiv:2609.00666v1 Announce Type: new Abstract: InfRared Small Target Detection (IRSTD) is a prominent and challenging task in computer vision. In recent years, text-guided methods have significantly...
AdaptiveCDM is a modular framework for source‑free few‑shot domain adaptation in cell detection, enabling a pretrained model to adapt to new imaging domains using only a handful of labeled target images and no source data. It combines Resolution‑Aware Augmentation (RAug) to balance scarce, class‑imbalanced samples while preserving cellular morphology, and Category‑Aware Representation Learning (CARL) to strengthen class‑consistent proposals for better localization and classification. Experiments on M5 and Raabin‑WBC datasets show that AdaptiveCDM achieves competitive or superior mAP scores compared to state‑of‑the‑art methods under their respective supervision settings.
SPARK‑SAM is a new approach that adapts the Segment‑Anything Model (SAM) for infrared small‑target segmentation by learning target‑domain response knowledge and conditioning the decoder with an image‑conditioned joint self‑prompt state. In experiments on three IRSTD benchmarks, SPARK‑SAM achieves IoU scores of 75.78%, 86.49%, and 68.34% with only 0.726 M additional parameters, outperforming 14 retrained SAM variants. The method combines benchmark‑mask supervision with reliability‑aware response guidance, and ablations show consistent accuracy gains from response guidance and high‑resolution prompt refinement.
arXiv:2608.20754v1 Announce Type: new Abstract: Promptable segmentation models provide a reusable interface, but direct transfer to automatic infrared small-target segmentation (IRSTD) exposes a mism...
arXiv:2606. 23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details.
arXiv:2609.18773v1 Announce Type: new Abstract: Long-range infrared imaging frequently confronts dense target clusters whose diffraction-limited signatures merge into a single indistinguishable blob,...