861 citations · 1k across the 18 of their papers we have counts for
20 papers
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…
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…
DenoDet V2: Phase-Amplitude Cross Denoising for SAR Object Detection
Kang Ni, Minrui Zou, Yuxuan Li +4
One of the primary challenges in Synthetic Aperture Radar (SAR) object detection lies in the pervasive influence of coherent noise. As a common practice, most existing methods, whe…
RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentation
Fengyi Wu, Yimian Dai, Tianfang Zhang +4
Robust principal component analysis (RPCA) decomposes an observation matrix into low-rank background and sparse object components. This capability has enabled its application in ta…
SeqCSIST: Sequential Closely-Spaced Infrared Small Target Unmixing
Ximeng Zhai, Bohan Xu, Yaohong Chen +3
Due to the limitation of the optical lens focal length and the resolution of the infrared detector, distant Closely-Spaced Infrared Small Target (CSIST) groups typically appear as…
A Simple Detector with Frame Dynamics is a Strong Tracker
Chenxu Peng, Chenxu Wang, Minrui Zou +5
Infrared object tracking plays a crucial role in Anti-Unmanned Aerial Vehicle (Anti-UAV) applications. Existing trackers often depend on cropped template regions and have limited m…
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…
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…
HazyDet: Open-Source Benchmark for Drone-View Object Detection with Depth-Cues in Hazy Scenes
Changfeng Feng, Zhenyuan Chen, Xiang Li +5
Object detection from aerial platforms under adverse atmospheric conditions, particularly haze, is paramount for robust drone autonomy. Yet, this domain remains largely underexplor…
Pick of the Bunch: Detecting Infrared Small Targets Beyond Hit-Miss Trade-Offs via Selective Rank-Aware Attention
Yimian Dai, Peiwen Pan, Yulei Qian +4
Infrared small target detection faces the inherent challenge of precisely localizing dim targets amidst complex background clutter. Traditional approaches struggle to balance detec…
Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection
Fei Zhou, Maixia Fu, Yulei Qian +2
Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with…
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…
DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images
Yimian Dai, Minrui Zou, Yuxuan Li +3
Synthetic Aperture Radar (SAR) target detection has long been impeded by inherent speckle noise and the prevalence of diminutive, ambiguous targets. While deep neural networks have…
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…
One-Stage Cascade Refinement Networks for Infrared Small Target Detection
Yimian Dai, Xiang Li, Fei Zhou +3
Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-wo…
Attentional Local Contrast Networks for Infrared Small Target Detection
Yimian Dai, Yiquan Wu, Fei Zhou +1
To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this paper, we propose a novel model-driven deep network for infrared small target detection, w…
Asymmetric Contextual Modulation for Infrared Small Target Detection
Yimian Dai, Yiquan Wu, Fei Zhou +1
Single-frame infrared small target detection remains a challenge not only due to the scarcity of intrinsic target characteristics but also because of lacking a public dataset. In t…
Attentional Feature Fusion
Yimian Dai, Fabian Gieseke, Stefan Oehmcke +2
Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures. It is often implemented via simple operations…
Attention as Activation
Yimian Dai, Stefan Oehmcke, Fabian Gieseke +2
Activation functions and attention mechanisms are typically treated as having different purposes and have evolved differently. However, both concepts can be formulated as a non-lin…
Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection
Yimian Dai, Yiquan Wu
Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However,…