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
Video object tracking based on YOLOv7 and DeepSORT
Feng Yang, Xingle Zhang, Bo Liu
W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection
Zitong Huang, Yiping Bao, Bowen Dong, Erjin Zhou, Wangmeng Zuo
Domain Adaptive Person Search
Junjie Li, Yichao Yan, Guanshuo Wang, Fufu Yu, Qiong Jia, Shouhong Ding
Real Time Object Detection System with YOLO and CNN Models: A Review
Viswanatha V, Chandana R K, Ramachandra A. C.
Satellite Detection in Unresolved Space Imagery for Space Domain Awareness Using Neural Networks
Jarred Jordan, Daniel Posada, David Zuehlke, Angelica Radulovic, Aryslan Malik, Troy Henderson
Dual Stream Computer-Generated Image Detection Network Based On Channel Joint And Softpool
Ziyi Xi, Hao Lin, Weiqi Luo
Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks
Daniel Niederlöhner, Michael Ulrich, Sascha Braun, Daniel Köhler, Florian Faion, Claudius Gläser, André Treptow, Holger Blume