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
Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Network
Yunhui Zhou, Dongqi Han, Yuguo Yu
Advanced Efficient Strategy for Detection of Dark Objects Based on Spiking Network with Multi-Box Detection
Munawar Ali, Baoqun Yin, Hazrat Bilal, Aakash Kumar, Ali Muhammad, Avinash Rohra
SimPLR: A Simple and Plain Transformer for Scaling-Efficient Object Detection and Segmentation
Duy-Kien Nguyen, Martin R. Oswald, Cees G. M. Snoek
Joint object detection and re-identification for 3D obstacle multi-camera systems
Irene Cortés, Jorge Beltrán, Arturo de la Escalera, Fernando García
Anchor-Intermediate Detector: Decoupling and Coupling Bounding Boxes for Accurate Object Detection
Yilong Lv, Min Li, Yujie He, Shaopeng Li, Zhuzhen He, Aitao Yang
Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images
Nanqing Liu, Xun Xu, Yingjie Gao, Heng-Chao Li
DynamicBEV: Leveraging Dynamic Queries and Temporal Context for 3D Object Detection
Jiawei Yao, Yingxin Lai
HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged Information
Heitor Rapela Medeiros, Fidel A. Guerrero Pena, Masih Aminbeidokhti, Thomas Dubail, Eric Granger, Marco Pedersoli