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
CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar
Jen-Hao Cheng, Sheng-Yao Kuan, Hugo Latapie, Gaowen Liu, Jenq-Neng Hwang
Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO
Julian Moosmann, Pietro Bonazzi, Yawei Li, Sizhen Bian, Philipp Mayer, Luca Benini, Michele Magno
Fully Quantized Always-on Face Detector Considering Mobile Image Sensors
Haechang Lee, Wongi Jeong, Dongil Ryu, Hyunwoo Je, Albert No, Kijeong Kim, Se Young Chun
Detecting Generated Images by Real Images Only
Xiuli Bi, Bo Liu, Fan Yang, Bin Xiao, Weisheng Li, Gao Huang, Pamela C. Cosman