1 code implementation • NeurIPS 2022 • Xi Jiang, Ying Chen, Qiang Nie, Yong liu, Jianlin Liu, Bin-Bin Gao, Jun Liu, Chengjie Wang, Feng Zheng
Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction.
no code implementations • 21 Mar 2024 • Xi Jiang, Ying Chen, Qiang Nie, Jianlin Liu, Yong liu, Chengjie Wang, Feng Zheng
To address this issue, we introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD), which leverages the fine-grained category information in the training stage.
no code implementations • 10 May 2023 • Zhuofei Huang, Jianlin Liu, Shang Xu, Ying Chen, Yong liu
Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces.
1 code implementation • 17 Apr 2023 • Jianlin Liu, Qiang Nie, Yong liu, Chengjie Wang
We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer.
no code implementations • 4 Apr 2023 • Lixia Wu, Jianlin Liu, Junhong Lou, Haoyuan Hu, Jianbin Zheng, Haomin Wen, Chao Song, Shu He
How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistics system.
no code implementations • 20 Sep 2022 • Dihe Huang, Ying Chen, Yikang Ding, Jinli Liao, Jianlin Liu, Kai Wu, Qiang Nie, Yong liu, Chengjie Wang, Zhiheng Li
In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically focus on the valuable parts of the object during voxel-to-BEV feature transformation.
no code implementations • 31 Aug 2022 • Jianlin Liu, Zhuofei Huang, Dihe Huang, Shang Xu, Ying Chen, Yong liu
3D object detection from monocular image(s) is a challenging and long-standing problem of computer vision.
1 code implementation • 18 Feb 2022 • Ying Chen, Dihe Huang, Shang Xu, Jianlin Liu, Yong liu
Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important.