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
SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection
Zhijie Rao, Jingcai Guo, Luyao Tang, Yue Huang, Xinghao Ding, Song Guo
IAdet: Simplest human-in-the-loop object detection
Franco Marchesoni-Acland, Gabriele Facciolo
Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection
Minh-Quan Dao, Julie Stephany Berrio, Vincent Frémont, Mao Shan, Elwan Héry, Stewart Worrall
MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion
Ce Zhang, Chengjie Zhang, Yiluan Guo, Lingji Chen, Michael Happold
Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detection
Peng Sun, Yongbin Zheng, Wenqi Wu, Wanying Xu, Shengjian Bai
Group channel pruning and spatial attention distilling for object detection
Yun Chu, Pu Li, Yong Bai, Zhuhua Hu, Yongqing Chen, Jiafeng Lu
Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection
Yingjie Wang, Jiajun Deng, Yao Li, Jinshui Hu, Cong Liu, Yu Zhang, Jianmin Ji, Wanli Ouyang, Yanyong Zhang