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
African or European Swallow? Benchmarking Large Vision-Language Models for Fine-Grained Object Classification
Gregor Geigle, Radu Timofte, Goran Glavaš
Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines
Xinyi Ying, Chao Xiao, Ruojing Li, Xu He, Boyang Li, Zhaoxu Li, Yingqian Wang, Mingyuan Hu, Qingyu Xu, Zaiping Lin, Miao Li, Shilin Zhou, Wei An, Weidong Sheng, Li Liu
LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection
Lilian Hollard, Lucas Mohimont, Nathalie Gaveau, Luiz-Angelo Steffenel
Enhanced Object Detection: A Study on Vast Vocabulary Object Detection Track for V3Det Challenge 2024
Peixi Wu, Bosong Chai, Xuan Nie, Longquan Yan, Zeyu Wang, Qifan Zhou, Boning Wang, Yansong Peng, Hebei Li
BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in Vision-based Roadside 3D Object Detection
Wenjie Wang, Yehao Lu, Guangcong Zheng, Shuigen Zhan, Xiaoqing Ye, Zichang Tan, Jingdong Wang, Gaoang Wang, Xi Li
Advancing Roadway Sign Detection with YOLO Models and Transfer Learning
Selvia Nafaa, Hafsa Essam, Karim Ashour, Doaa Emad, Rana Mohamed, Mohammed Elhenawy, Huthaifa I. Ashqar, Abdallah A. Hassan, Taqwa I. Alhadidi
Unsupervised Object Detection with Theoretical Guarantees
Marian Longa, João F. Henriques