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
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
Chengqi Lyu, Wenwei Zhang, Haian Huang, Yue Zhou, Yudong Wang, Yanyi Liu, Shilong Zhang, Kai Chen
Event-based YOLO Object Detection: Proof of Concept for Forward Perception System
Waseem Shariff, Muhammad Ali Farooq, Joe Lemley, Peter Corcoran
Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras
Daniel Gehrig, Davide Scaramuzza
Improving Crowded Object Detection via Copy-Paste
Jiangfan Deng, Dewen Fan, Xiaosong Qiu, Feng Zhou
Explaining YOLO: Leveraging Grad-CAM to Explain Object Detections
Armin Kirchknopf, Djordje Slijepcevic, Ilkay Wunderlich, Michael Breiter, Johannes Traxler, Matthias Zeppelzauer
NeRF-RPN: A general framework for object detection in NeRFs
Benran Hu, Junkai Huang, Yichen Liu, Yu-Wing Tai, Chi-Keung Tang
Plug and Play Active Learning for Object Detection
Chenhongyi Yang, Lichao Huang, Elliot J. Crowley
Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning
Zhongxiang Zhou, Yifei Yang, Yue Wang, Rong Xiong
Simultaneous Multiple Object Detection and Pose Estimation using 3D Model Infusion with Monocular Vision
Congliang Li, Shijie Sun, Xiangyu Song, Huansheng Song, Naveed Akhtar, Ajmal Saeed Mian