Object Detector
Object detection, aiming to identify and locate objects within images or videos, is a core computer vision task with applications ranging from autonomous driving to medical image analysis. Current research emphasizes improving accuracy, particularly in addressing false positives and handling challenging conditions like occlusions, varying viewpoints, and noisy data, often employing transformer-based architectures and leveraging techniques like knowledge distillation and semi-supervised learning. These advancements are crucial for enhancing the reliability and robustness of object detectors in real-world applications, impacting fields requiring accurate and efficient scene understanding.
Papers
Zero-Shot Scene Understanding for Automatic Target Recognition Using Large Vision-Language Models
Yasiru Ranasinghe, Vibashan VS, James Uplinger, Celso De Melo, Vishal M. Patel
Toward Realistic Camouflaged Object Detection: Benchmarks and Method
Zhimeng Xin, Tianxu Wu, Shiming Chen, Shuo Ye, Zijing Xie, Yixiong Zou, Xinge You, Yufei Guo
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector
Tianheng Qiu, Ka Lung Law, Guanghua Pan, Jufei Wang, Xin Gao, Xuan Huang, Hu Wei
PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection
Xiaoran Xu, Jiangang Yang, Wenhui Shi, Siyuan Ding, Luqing Luo, Jian Liu