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
MuRAL: Multi-Scale Region-based Active Learning for Object Detection
Yi-Syuan Liou, Tsung-Han Wu, Jia-Fong Yeh, Wen-Chin Chen, Winston H. Hsu
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation
Hieu H. Pham, Khiem H. Le, Tuan V. Tran, Ha Q. Nguyen
Detecting Everything in the Open World: Towards Universal Object Detection
Zhenyu Wang, Yali Li, Xi Chen, Ser-Nam Lim, Antonio Torralba, Hengshuang Zhao, Shengjin Wang
Detecting the open-world objects with the help of the Brain
Shuailei Ma, Yuefeng Wang, Ying Wei, Peihao Chen, Zhixiang Ye, Jiaqi Fan, Enming Zhang, Thomas H. Li
Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR
Feng Li, Ailing Zeng, Shilong Liu, Hao Zhang, Hongyang Li, Lei Zhang, Lionel M. Ni
Identifying Label Errors in Object Detection Datasets by Loss Inspection
Marius Schubert, Tobias Riedlinger, Karsten Kahl, Daniel Kröll, Sebastian Schoenen, Siniša Šegvić, Matthias Rottmann
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images
Nanqing Liu, Xun Xu, Turgay Celik, Zongxin Gan, Heng-Chao Li