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
Object-conditioned Bag of Instances for Few-Shot Personalized Instance Recognition
Umberto Michieli, Jijoong Moon, Daehyun Kim, Mete Ozay
Prompt Learning for Oriented Power Transmission Tower Detection in High-Resolution SAR Images
Tianyang Li, Chao Wang, Hong Zhang
Roadside Monocular 3D Detection via 2D Detection Prompting
Yechi Ma, Shuoquan Wei, Churun Zhang, Wei Hua, Yanan Li, Shu Kong
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection
Changshun Wu, Weicheng He, Chih-Hong Cheng, Xiaowei Huang, Saddek Bensalem
DODA: Diffusion for Object-detection Domain Adaptation in Agriculture
Shuai Xiang, Pieter M. Blok, James Burridge, Haozhou Wang, Wei Guo
Road Obstacle Detection based on Unknown Objectness Scores
Chihiro Noguchi, Toshiaki Ohgushi, Masao Yamanaka