S Yao, R Guan, X Huang, Z Li, X Sha… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret …
This paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object …
Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in …
We aim at advancing open-vocabulary object detection, which detects objects described by arbitrary text inputs. The fundamental challenge is the availability of training data. It is costly …
H Bangalath, M Maaz, MU Khattak… - Advances in …, 2022 - proceedings.neurips.cc
Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by leveraging different forms of weak supervision. This helps generalize to novel objects at …
X Du, Z Wang, M Cai, Y Li - arXiv preprint arXiv:2202.01197, 2022 - arxiv.org
Out-of-distribution (OOD) detection has received much attention lately due to its importance in the safe deployment of neural networks. One of the key challenges is that models lack …
N Karim, MN Rizve, N Rahnavard… - Proceedings of the …, 2022 - openaccess.thecvf.com
Supervised deep learning methods require a large repository of annotated data; hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization …
Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown …
D Bogdoll, M Nitsche… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set …