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
STAR: Shape-focused Texture Agnostic Representations for Improved Object Detection and 6D Pose Estimation
Peter Hönig, Stefan Thalhammer, Jean-Baptiste Weibel, Matthias Hirschmanner, Markus Vincze
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection
Fan Wu, Jinling Gao, Lanqing Hong, Xinbing Wang, Chenghu Zhou, Nanyang Ye
Improving Object Detection Quality in Football Through Super-Resolution Techniques
Karolina Seweryn, Gabriel Chęć, Szymon Łukasik, Anna Wróblewska
Real-time Traffic Object Detection for Autonomous Driving
Abdul Hannan Khan, Syed Tahseen Raza Rizvi, Andreas Dengel
Do Object Detection Localization Errors Affect Human Performance and Trust?
Sven de Witte, Ombretta Strafforello, Jan van Gemert