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
SoccerNet 2023 Tracking Challenge -- 3rd place MOT4MOT Team Technical Report
Gal Shitrit, Ishay Be'ery, Ido Yerhushalmy
Unsupervised Recognition of Unknown Objects for Open-World Object Detection
Ruohuan Fang, Guansong Pang, Lei Zhou, Xiao Bai, Jin Zheng
Njobvu-AI: An open-source tool for collaborative image labeling and implementation of computer vision models
Jonathan S. Koning, Ashwin Subramanian, Mazen Alotaibi, Cara L. Appel, Christopher M. Sullivan, Thon Chao, Lisa Truong, Robyn L. Tanguay, Pankaj Jaiswal, Taal Levi, Damon B. Lesmeister