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
VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation
Chengjie Huang, Vahdat Abdelzad, Sean Sedwards, Krzysztof Czarnecki
YCB-LUMA: YCB Object Dataset with Luminance Keying for Object Localization
Thomas Pöllabauer
Bounding-box Watermarking: Defense against Model Extraction Attacks on Object Detectors
Satoru Koda, Ikuya Morikawa
Synthetica: Large Scale Synthetic Data for Robot Perception
Ritvik Singh, Jingzhou Liu, Karl Van Wyk, Yu-Wei Chao, Jean-Francois Lafleche, Florian Shkurti, Nathan Ratliff, Ankur Handa
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications
Alain Andres, Aitor Martinez-Seras, Ibai Laña, Javier Del Ser