MI-DETR: A Strong Baseline for Moving Infrared Small Target Detection with Motion Integration
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；ShanghaiTech University（上海科技大学）；Beijing Institute of Basic Medical Sciences（北京基础医学研究所）
Nian Liu, Jin Gao, Zhen Liang, Shubo Lin, Sikui Zhang, Fudong Ge, Liang Li, Weiming Hu
Detecting moving infrared small targets is challenging because tiny, low-contrast targets occupy few pixels and are easily obscured by dynamic backgrounds. Existing multi-frame methods aggregate temporal information across frames to capture motion. However, dynamic background changes can generate similar motion cues, making it difficult to distinguish between target motion and background interference. Furthermore, even when motion cues are extracted, combining them with current-frame appearance features remains difficult. To address these issues, we propose Motion Integration DETR (MI-DETR), a three-stage framework that explicitly models motion and fuses it with appearance features. First, to suppress background clutter while preserving target-related motion cues, Recurrent Interpretable Motion Cue Aggregation (RIMCA) maintains a recurrent temporal state that accumulates motion across consecutive frames, producing a causal and spatially aligned motion representation. Second, to integrate spatial and temporal information, Pathway Mutual Interaction (PMI) preserves separate appearance and motion pathways while enabling bidirectional feature exchange between them. Finally, an RT-DETR-based detector uses these refined features for end-to-end target localization. Experiments on DAUB-R, ITSDT-15K, and IRDST-H show that explicit motion modeling and pathway interaction effectively improve moving infrared small target detection.
Gaze-DETR: Top-Down Guidance Through Priority Maps for Infrared Weak-Small UAV Detection with DETR
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；ShanghaiTech University（上海科技大学）；Beijing Institute of Basic Medical Sciences（北京基础医学研究所）
WS-DETR: Robust Water Surface Object Detection through Vision-Radar Fusion with Detection Transformer
College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Graz University of Technology（格拉茨工业大学）；Virtual Vehicle Research（虚拟车辆研究院）
Bidirectional Temporal Information Propagation for Moving Infrared Small Target Detection
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
FeedbackSTS-Det: Sparse Frames-Based Spatio-Temporal Semantic Feedback Network for Moving Infrared Small Target Detection
School of Information and Communication Engineering and the Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院及成像探测与智能感知实验室）；School of Television and Audiovisual Arts, Communication University of Zhejiang（浙江传媒学院电视与视听艺术学院）
Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
HyperTea: A Hypergraph-based Temporal Enhancement and Alignment Network for Moving Infrared Small Target Detection
Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences（空间系统电子信息技术重点实验室，国家空间科学中心，中国科学院）；School of Advanced Interdisciplinary Studies, University of Chinese Academy of Sciences（中国科学院大学交叉学科学院）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）
运动目标检测（MOD）是自动驾驶流水线的关键任务。MOD通常通过2流卷积架构来处理，该架构包含外观和运动提示，而不考虑空间或运动特征之间的相互关系。在本文中，我们解决这个问题，通过多头注意力机制，在空间和运动流。我们提出MODETR;移动目标检测变换器网络，由用于空间和运动模态的多流Transformer编码器和使用集合预测产生移动对象边界框的对象Transformer解码器组成。整个架构使用二分损失进行端到端训练。将运动线索与Transformer模型的几种方法进行了探讨，包括双流RGB和光流（OF）的方法，和多流架构，利用序列信息。为了结合时间信息，我们提出了一种新的时间位置编码（TPE）的方法来扩展空间位置编码（SPE）在DETR。为此，我们探索了两种架构选择，在速度和时间之间进行平衡。为了评估我们的网络，我们在KITTI MOD [6]数据集上执行MOD任务。结果显示，MOD的Transformer网络的mAP显著高于现有技术方法的5%。此外，所提出的TPE编码提供了超过SPE基线的10%的mAP改进。
DAMSDet: Dynamic Adaptive Multispectral Detection Transformer with Competitive Query Selection and Adaptive Feature Fusion
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学通信与信息工程学院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）
Edge-Aware and Content-Adaptive Infrared Gas Leak Detection for Industrial Safety Monitoring
School of Mechatronic Engineering, Xi’an Technological University（机械电子工程学院，西安理工大学）；School of Electronic Information Engineering, Xi’an Technological University（电子信息工程学院，西安理工大学）；Shaanxi Shanhua Coal Chemical Co., Ltd.（陕西神华化工有限公司）
