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
Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization
Zhao Wang, Aoxue Li, Fengwei Zhou, Zhenguo Li, Qi Dou
Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection
Martin Aubard, László Antal, Ana Madureira, Erika Ábrahám
MOTPose: Multi-object 6D Pose Estimation for Dynamic Video Sequences using Attention-based Temporal Fusion
Arul Selvam Periyasamy, Sven Behnke