6 citations · 8 across the 9 of their papers we have counts for
9 papers · 1 filter
Seeing is Improving: Visual Feedback for Iterative Text Layout Refinement
Junrong Guo, Shancheng Fang, Yadong Qu +1
Recent advances in Multimodal Large Language Models (MLLMs) have enabled automated generation of structured layouts from natural language descriptions. Existing methods typically f…
IGD: Instructional Graphic Design with Multimodal Layer Generation
Yadong Qu, Shancheng Fang, Yuxin Wang +4
Graphic design visually conveys information and data by creating and combining text, images and graphics. Two-stage methods that rely primarily on layout generation lack creativity…
Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing
Yadong Qu, Yuxin Wang, Bangbang Zhou +3
Existing scene text recognition (STR) methods struggle to recognize challenging texts, especially for artistic and severely distorted characters. The limitation lies in the insuffi…
Leveraging Text Localization for Scene Text Removal via Text-aware Masked Image Modeling
Zixiao Wang, Hongtao Xie, YuXin Wang +3
Existing scene text removal (STR) task suffers from insufficient training data due to the expensive pixel-level labeling. In this paper, we aim to address this issue by introducing…
How Control Information Influences Multilingual Text Image Generation and Editing?
Boqiang Zhang, Zuan Gao, Yadong Qu +1
Visual text generation has significantly advanced through diffusion models aimed at producing images with readable and realistic text. Recent works primarily use a ControlNet-based…
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…
Self-Supervised Pre-training with Symmetric Superimposition Modeling for Scene Text Recognition
Zuan Gao, Yuxin Wang, Yadong Qu +4
In text recognition, self-supervised pre-training emerges as a good solution to reduce dependence on expansive annotated real data. Previous studies primarily focus on local visual…
Exploring Stroke-Level Modifications for Scene Text Editing
Yadong Qu, Qingfeng Tan, Hongtao Xie +3
Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. However, due to the complicated background textures…
PERT: A Progressively Region-based Network for Scene Text Removal
Yuxin Wang, Hongtao Xie, Shancheng Fang +2
Scene text removal (STR) contains two processes: text localization and background reconstruction. Through integrating both processes into a single network, previous methods provide…