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
Test-time Adaptation with Slot-Centric Models
Mihir Prabhudesai, Anirudh Goyal, Sujoy Paul, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gaurav Aggarwal, Thomas Kipf, Deepak Pathak, Katerina Fragkiadaki
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds
Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, Yulan Guo
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving
Kaican Li, Kai Chen, Haoyu Wang, Lanqing Hong, Chaoqiang Ye, Jianhua Han, Yukuai Chen, Wei Zhang, Chunjing Xu, Dit-Yan Yeung, Xiaodan Liang, Zhenguo Li, Hang Xu
What's in the Black Box? The False Negative Mechanisms Inside Object Detectors
Dimity Miller, Peyman Moghadam, Mark Cox, Matt Wildie, Raja Jurdak
Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection
Fatih Cagatay Akyon, Sinan Onur Altinuc, Alptekin Temizel
An experimental study of the vision-bottleneck in VQA
Pierre Marza, Corentin Kervadec, Grigory Antipov, Moez Baccouche, Christian Wolf
Single-stage Rotate Object Detector via Two Points with Solar Corona Heatmap
Beihang Song, Jing Li, Shan Xue, Jun Chang, Jia Wu, Jun Wan, Tianpeng Liu