no code implementations • 6 Apr 2024 • Ming Zhou, Weize Quan, Ziqi Zhou, Kai Wang, Tong Wang, Dong-Ming Yan
Motivated by these insights, we introduce a Text-oriented Cross-Attention Network (TCAN), emphasizing the predominant role of the text modality in MSA.
no code implementations • 7 Jan 2024 • Weize Quan, Jiaxi Chen, Yanli Liu, Dong-Ming Yan, Peter Wonka
The goal of this paper is to comprehensively review the deep learning-based methods for image and video inpainting.
1 code implementation • 14 Dec 2023 • Yingrui Wu, Mingyang Zhao, Keqiang Li, Weize Quan, Tianqi Yu, Jianfeng Yang, Xiaohong Jia, Dong-Ming Yan
This work presents an accurate and robust method for estimating normals from point clouds.
1 code implementation • CVPR 2023 • Youxin Pang, Yong Zhang, Weize Quan, Yanbo Fan, Xiaodong Cun, Ying Shan, Dong-Ming Yan
In this paper, we introduce a novel self-supervised disentanglement framework to decouple pose and expression without 3DMMs and paired data, which consists of a motion editing module, a pose generator, and an expression generator.
1 code implementation • PRCV 2021 • Shiyu Hou, Chaoqun Wang, Weize Quan, Jingen Jiang, Dong-Ming Yan
The core goal is to improve the accuracy of text detection and recognition by removing the highlight from text images.
1 code implementation • 19 Nov 2020 • Xuewei Bian, Chaoqun Wang, Weize Quan, Juntao Ye, Xiaopeng Zhang, Dong-Ming Yan
Specifically, we decouple the text removal problem into text stroke detection and stroke removal.
no code implementations • 4 Nov 2020 • Ruisong Zhang, Weize Quan, Baoyuan Wu, Zhifeng Li, Dong-Ming Yan
Recent GAN-based image inpainting approaches adopt an average strategy to discriminate the generated image and output a scalar, which inevitably lose the position information of visual artifacts.
no code implementations • 17 Feb 2019 • Weize Quan, Dong-Ming Yan, Kai Wang, Xiaopeng Zhang, Denis Pellerin
First, we design and implement a base network, which can attain better performance in terms of classification accuracy and generalization (in most cases) compared with state-of-the-art methods.
no code implementations • ECCV 2018 • Hanyu Wang, Jianwei Guo, Dong-Ming Yan, Weize Quan, Xiaopeng Zhang
In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes.