Object Detection
Object detection, a core computer vision task, aims to identify and locate objects within images or videos. Current research emphasizes improving accuracy and efficiency across diverse scenarios, focusing on architectures like YOLO and DETR, and exploring techniques such as multimodal fusion, attention mechanisms, and loss function refinements to handle challenges like small object detection, adverse weather conditions, and limited labeled data. These advancements have significant implications for applications ranging from autonomous driving and robotics to medical image analysis and remote sensing, driving progress in both theoretical understanding and practical deployment of object detection systems.
Papers
UAV-Based Human Body Detector Selection and Fusion for Geolocated Saliency Map Generation
Piotr Rudol, Patrick Doherty, Mariusz Wzorek, Chattrakul Sombattheera
FA-YOLO: Research On Efficient Feature Selection YOLO Improved Algorithm Based On FMDS and AGMF Modules
Yukang Huo, Mingyuan Yao, Qingbin Tian, Tonghao Wang, Ruifeng Wang, Haihua Wang
Anno-incomplete Multi-dataset Detection
Yiran Xu, Haoxiang Zhong, Kai Wu, Jialin Li, Yong Liu, Chengjie Wang, Shu-Tao Xia, Hongen Liao
WARM-3D: A Weakly-Supervised Sim2Real Domain Adaptation Framework for Roadside Monocular 3D Object Detection
Xingcheng Zhou, Deyu Fu, Walter Zimmer, Mingyu Liu, Venkatnarayanan Lakshminarasimhan, Leah Strand, Alois C. Knoll
Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection
Xinhao Luo, Man Yao, Yuhong Chou, Bo Xu, Guoqi Li