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
MCUBench: A Benchmark of Tiny Object Detectors on MCUs
Sudhakar Sah, Darshan C. Ganji, Matteo Grimaldi, Ravish Kumar, Alexander Hoffman, Honnesh Rohmetra, Ehsan Saboori
A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation
Jer Pelhan, Alan Lukežič, Vitjan Zavrtanik, Matej Kristan
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