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.
517papers
Papers - Page 22
March 9, 2023
March 2, 2023
FeatAug-DETR: Enriching One-to-Many Matching for DETRs with Feature Augmentation
Rongyao Fang, Peng Gao, Aojun Zhou, Yingjie Cai, Si Liu, Jifeng Dai, Hongsheng LiDeep-NFA: a Deep a contrario Framework for Small Object Detection
Alina Ciocarlan, Sylvie Le Hegarat-Mascle, Sidonie Lefebvre, Arnaud Woiselle
February 23, 2023
February 21, 2023
Self-improving object detection via disagreement reconciliation
Gianluca Scarpellini, Stefano Rosa, Pietro Morerio, Lorenzo Natale, Alessio Del BueOriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey
Kun Wang, Zi Wang, Zhang Li, Ang Su, Xichao Teng, Erting Pan, Minhao Liu, Qifeng YuAutomotive RADAR sub-sampling via object detection networks: Leveraging prior signal information
Madhumitha Sakthi, Ahmed Tewfik, Marius Arvinte, Haris VikaloAssessing Domain Gap for Continual Domain Adaptation in Object Detection
Anh-Dzung Doan, Bach Long Nguyen, Surabhi Gupta, Ian Reid, Markus Wagner, Tat-Jun Chin
February 19, 2023
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