most citedSymmetrical Linguistic Feature Distillation with CLIP for Scene Text Recognition

3 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024

Graph Classification via Reference Distribution Learning: Theory and Practice

Zixiao Wang, Jicong Fan

Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few m…

cs.CV2024

Focus on the Whole Character: Discriminative Character Modeling for Scene Text Recognition

Bangbang Zhou, Yadong Qu, Zixiao Wang +3

Recently, scene text recognition (STR) models have shown significant performance improvements. However, existing models still encounter difficulties in recognizing challenging text…

cs.LG2024

MoreauPruner: Robust Pruning of Large Language Models against Weight Perturbations

Zixiao Wang, Jingwei Zhang, Wenqian Zhao +2

Few-shot gradient methods have been extensively utilized in existing model pruning methods, where the model weights are regarded as static values and the effects of potential weigh…

cs.CL2024

ChatPattern: Layout Pattern Customization via Natural Language

Zixiao Wang, Yunheng Shen, Xufeng Yao +4

Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention. In this paper, we propose ChatPatter…

cs.NI20241 cited

Time Synchronization for 5G and TSN Integrated Networking

Zixiao Wang, Zonghui Li, Xuan Qiao +3

Emerging industrial applications involving robotic collaborative operations and mobile robots require a more reliable and precise wireless network for deterministic data transmissi…

cs.CV20233 cited

Symmetrical Linguistic Feature Distillation with CLIP for Scene Text Recognition

Zixiao Wang, Hongtao Xie, Yuxin Wang +3

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 Fea…