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
Humans disagree with the IoU for measuring object detector localization error
Ombretta Strafforello, Vanathi Rajasekart, Osman S. Kayhan, Oana Inel, Jan van Gemert
Why Accuracy Is Not Enough: The Need for Consistency in Object Detection
Caleb Tung, Abhinav Goel, Fischer Bordwell, Nick Eliopoulos, Xiao Hu, George K. Thiruvathukal, Yung-Hsiang Lu
Active Learning Strategies for Weakly-supervised Object Detection
Huy V. Vo, Oriane Siméoni, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Jean Ponce
Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations
Wenjie Pei, Shuang Wu, Dianwen Mei, Fanglin Chen, Jiandong Tian, Guangming Lu
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
Weihan Cao, Yifan Zhang, Jianfei Gao, Anda Cheng, Ke Cheng, Jian Cheng
Object-Level Targeted Selection via Deep Template Matching
Suraj Kothawade, Donna Roy, Michele Fenzi, Elmar Haussmann, Jose M. Alvarez, Christoph Angerer