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
Replay Consolidation with Label Propagation for Continual Object Detection
Riccardo De Monte, Davide Dalle Pezze, Marina Ceccon, Francesco Pasti, Francesco Paissan, Elisabetta Farella, Gian Antonio Susto, Nicola Bellotto
Proto-OOD: Enhancing OOD Object Detection with Prototype Feature Similarity
Junkun Chen, Jilin Mei, Liang Chen, Fangzhou Zhao, Yu Hu
Hybrid Classification-Regression Adaptive Loss for Dense Object Detection
Yanquan Huang, Liu Wei Zhen, Yun Hao, Mengyuan Zhang, Qingyao Wu, Zikun Deng, Xueming Liu, Hong Deng
UTrack: Multi-Object Tracking with Uncertain Detections
Edgardo Solano-Carrillo, Felix Sattler, Antje Alex, Alexander Klein, Bruno Pereira Costa, Angel Bueno Rodriguez, Jannis Stoppe