22 citations · 68 across the 16 of their papers we have counts for
16 papers
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…
Test-Time Scaling with Reflective Generative Model
Zixiao Wang, Yuxin Wang, Xiaorui Wang +8
We introduce our first reflective generative model MetaStone-S1, which obtains OpenAI o3-mini's performance via the new Reflective Generative Form. The new form focuses on high-qua…
GRIP: A Graph-Based Reasoning Instruction Producer
Jiankang Wang, Jianjun Xu, Xiaorui Wang +4
Large-scale, high-quality data is essential for advancing the reasoning capabilities of large language models (LLMs). As publicly available Internet data becomes increasingly scarc…
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…
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…
Choose What You Need: Disentangled Representation Learning for Scene Text Recognition, Removal and Editing
Boqiang Zhang, Hongtao Xie, Zuan Gao +1
Scene text images contain not only style information (font, background) but also content information (character, texture). Different scene text tasks need different information, bu…
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…
Learning Unified Decompositional and Compositional NeRF for Editable Novel View Synthesis
Yuxin Wang, Wayne Wu, Dan Xu
Implicit neural representations have shown powerful capacity in modeling real-world 3D scenes, offering superior performance in novel view synthesis. In this paper, we target a mor…
Linguistic More: Taking a Further Step toward Efficient and Accurate Scene Text Recognition
Boqiang Zhang, Hongtao Xie, Yuxin Wang +2
Vision model have gained increasing attention due to their simplicity and efficiency in Scene Text Recognition (STR) task. However, due to lacking the perception of linguistic know…
ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting
Shancheng Fang, Zhendong Mao, Hongtao Xie +3
Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for ch…
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…
From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network
Yuxin Wang, Hongtao Xie, Shancheng Fang +3
In this paper, we abandon the dominant complex language model and rethink the linguistic learning process in the scene text recognition. Different from previous methods considering…
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…
Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition
Shancheng Fang, Hongtao Xie, Yuxin Wang +2
Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively model linguistic rules in end-to-end deep networks remains a research challenge. In…
ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection
Yuxin Wang, Hongtao Xie, Zhengjun Zha +3
Scene text detection has witnessed rapid development in recent years. However, there still exists two main challenges: 1) many methods suffer from false positives in their text rep…