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.
517papers
Papers - Page 4
December 13, 2024
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FD2-Net: Frequency-Driven Feature Decomposition Network for Infrared-Visible Object Detection
UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework
SEGT: A General Spatial Expansion Group Transformer for nuScenes Lidar-based Object Detection Task
ContextHOI: Spatial Context Learning for Human-Object Interaction Detection
Analysis of Object Detection Models for Tiny Object in Satellite Imagery: A Dataset-Centric Approach
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