11 papers
Visual Instruction Pretraining for Domain-Specific Foundation Models
Yuxuan Li, Yicheng Zhang, Wenhao Tang +4
Modern computer vision is converging on a closed loop in which perception, reasoning and generation mutually reinforce each other. However, this loop remains incomplete: the top-do…
DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing
Shengdong Han, Shangdong Yang, Xin Zhang +5
Resolving closely-spaced small targets in dense clusters presents a significant challenge in infrared imaging, as the overlapping signals hinder precise determination of their quan…
Multi-Scale Direction-Aware Network for Infrared Small Target Detection
Jinmiao Zhao, Zelin Shi, Chuang Yu +3
Infrared small target detection faces the problem that it is difficult to effectively separate the background and the target. Existing deep learning-based methods focus on edge and…
Rethinking Infrared Small Target Detection: A Foundation-Driven Efficient Paradigm
Chuang Yu, Jinmiao Zhao, Yunpeng Liu +6
While large-scale visual foundation models (VFMs) exhibit strong generalization across diverse visual domains, their potential for single-frame infrared small target (SIRST) detect…
SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection
Yuxuan Li, Xiang Li, Yunheng Li +5
With the rapid advancement of remote sensing technology, high-resolution multi-modal imagery is now more widely accessible. Conventional Object detection models are trained on a si…
Background Semantics Matter: Cross-Task Feature Exchange Network for Clustered Infrared Small Target Detection
Mengxuan Xiao, Yinfei Zhu, Yiming Zhu +5
Infrared small target detection presents significant challenges due to the limited intrinsic features of the target and the overwhelming presence of visually similar background dis…