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
On Calibration of Object Detectors: Pitfalls, Evaluation and Baselines
Selim Kuzucu, Kemal Oksuz, Jonathan Sadeghi, Puneet K. Dokania
Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodology
Frank A. Ruis, Alma M. Liezenga, Friso G. Heslinga, Luca Ballan, Thijs A. Eker, Richard J. M. den Hollander, Martin C. van Leeuwen, Judith Dijk, Wyke Huizinga