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...
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: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...
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:2610.18291v2 Announce Type: replace Abstract: Since the rise of supervised deep learning models, researchers have attempted to solve infrared small target detection using various models like co...
arXiv:2511.18031v1 Announce Type: cross Abstract: Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that i...
arXiv:2601.15829v3 Announce Type: replace Abstract: Recent years have witnessed the remarkable success of deep learning in remote sensing image interpretation, driven by the availability of large-sca...
arXiv:2610.18276v2 Announce Type: replace Abstract: Infrared small target detection is often used in different civilian and military applications to detect static or moving targets in scenes like in...
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,...
arXiv:2610.01291v1 Announce Type: new Abstract: Recent advances in deep learning for shadow removal have significantly enhanced image quality and realism. However, most approaches rely on real-world...
MOSAIC is an object‑centric framework that adapts pretrained diffusion Transformers for image restoration by learning compact structural conditions. It organizes degraded images into structural slots capturing object‑level and layout‑level evidence, integrates these across resolutions, and retrieves recurring priors from a shared prototype memory. Injecting these representations into selected layers of a frozen diffusion Transformer via lightweight residual modulation preserves the pretrained generative prior, yielding improved remote sensing image super‑resolution performance on UCMerced, AID, and RSSCN7 datasets.
arXiv:2606. 30215v1 Announce Type: cross Abstract: RGB-T detectors leverage the complementary strengths of visible and thermal infrared modalities, achieving robust performance under challenging conditions.