no code implementations • 9 May 2024 • Zuan Gao, Yuxin Wang, Yadong Qu, Boqiang Zhang, Zixiao Wang, Jianjun Xu, Hongtao Xie
At the pixel level, we reconstruct the original and inverted images to capture character shapes and texture-level linguistic context.
no code implementations • 15 Mar 2024 • Zixiao Wang, Yunheng Shen, Xufeng Yao, Wenqian Zhao, Yang Bai, Farzan Farnia, Bei Yu
Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention.
no code implementations • 25 Jan 2024 • Zixiao Wang, Dong Qiao, Jicong Fan
Discrete distribution clustering (D2C) was often solved by Wasserstein barycenter methods.
no code implementations • 15 Nov 2023 • Zixiao Wang, AmirEmad Ghassami, Ilya Shpitser
We consider the task of identifying and estimating a parameter of interest in settings where data is missing not at random (MNAR).
no code implementations • 18 Oct 2023 • Zixiao Wang, Farzan Farnia, Zhenghao Lin, Yunheng Shen, Bei Yu
First, we focus on the Fr\'echet inception distance (FID) and consider the following FID-based aggregate scores over the clients: 1) FID-avg as the mean of clients' individual FID scores, 2) FID-all as the FID distance of the trained model to the collective dataset containing all clients' data.
1 code implementation • 8 Oct 2023 • Zixiao Wang, Hongtao Xie, Yuxin Wang, Jianjun Xu, Boqiang Zhang, Yongdong Zhang
In this paper, we explore the potential of the Contrastive Language-Image Pretraining (CLIP) model in scene text recognition (STR), and establish a novel Symmetrical Linguistic Feature Distillation framework (named CLIP-OCR) to leverage both visual and linguistic knowledge in CLIP.
no code implementations • 23 Mar 2023 • Zixiao Wang, Yunheng Shen, Wenqian Zhao, Yang Bai, Guojin Chen, Farzan Farnia, Bei Yu
Deep generative models dominate the existing literature in layout pattern generation.
no code implementations • 27 Dec 2022 • Zixiao Wang, Junwu Weng, Chun Yuan, Jue Wang
Thanks to Noise Contrastive Learning, the average classification accuracy improvement on Mini-Kinetics and Sth-Sth-V1 is over 1. 6\%.
no code implementations • 4 Oct 2022 • Zixiao Wang, Yuluo Guo, Jin Zhao, Yu Zhang, Hui Yu, Xiaofei Liao, Biao Wang, Ting Yu
In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model.
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