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
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
KOLOMVERSE: Korea open large-scale image dataset for object detection in the maritime universe
Abhilasha Nanda, Sung Won Cho, Hyeopwoo Lee, Jin Hyoung Park
DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection
Yunhao Ge, Jiashu Xu, Brian Nlong Zhao, Neel Joshi, Laurent Itti, Vibhav Vineet