26 citations · 91 across the 39 of their papers we have counts for
42 papers
All-in-One Multilingual Scene Text Recognition with Script-aware Mixture-of-Experts
Xingsong Ye, Yongkun Du, Jiaxin Zhang +6
Multilingual scene text recognition (STR) remains challenging due to the scarcity of training data for most languages and the difficulty of serving diverse scripts within a single…
Perceptual Flow Matching for Few-Step Generative Modeling
Chuyang Zhao, Yifei Song, Hongfa Wang +7
We propose Perceptual Flow Matching (PFM), a simple yet effective framework for few-step generation in flow-matching models. Rather than performing velocity regression in the conve…
Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods
Xingsong Ye, Yongkun Du, Jiaxin Zhang +5
WordArt (artistic text) features highly customized fonts, textures, and layouts, making WordArt-oriented scene TExt Recognition (WATER) substantially more challenging than general…
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses
Xiao Li, Xiang Zheng, Yifeng Gao +35
Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, safety-critical environments. As…
ICPR 2026 Competition on Low-Resolution License Plate Recognition
Rayson Laroca, Valfride Nascimento, Donggun Kim +19
Low-Resolution License Plate Recognition (LRLPR) remains a challenging problem in real-world surveillance scenarios, where long capture distances, compression artifacts, and advers…
What Is Wrong with Synthetic Data for Scene Text Recognition? A Strong Synthetic Engine with Diverse Simulations and Self-Evolution
Xingsong Ye, Yongkun Du, JiaXin Zhang +3
Large-scale and categorical-balanced text data is essential for training effective Scene Text Recognition (STR) models, which is hard to achieve when collecting real data. Syntheti…
Complex Mathematical Expression Recognition: Benchmark, Large-Scale Dataset and Strong Baseline
Weikang Bai, Yongkun Du, Yuchen Su +2
Mathematical Expression Recognition (MER) has made significant progress in recognizing simple expressions, but the robust recognition of complex mathematical expressions with many…
UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters
Yongkun Du, Zhineng Chen, Yazhen Xie +6
Text and formulas constitute the core informational components of many documents. Accurately and efficiently recognizing both is crucial for developing robust and generalizable doc…
MDiff4STR: Mask Diffusion Model for Scene Text Recognition
Yongkun Du, Miaomiao Zhao, Songlin Fan +3
Mask Diffusion Models (MDMs) have recently emerged as a promising alternative to auto-regressive models (ARMs) for vision-language tasks, owing to their flexible balance of efficie…
DocPTBench: Benchmarking End-to-End Photographed Document Parsing and Translation
Yongkun Du, Pinxuan Chen, Xuye Ying +1
The advent of Multimodal Large Language Models (MLLMs) has unlocked the potential for end-to-end document parsing and translation. However, prevailing benchmarks such as OmniDocBen…
LRANet++: Low-Rank Approximation Network for Accurate and Efficient Text Spotting
Yuchen Su, Zhineng Chen, Yongkun Du +3
End-to-end text spotting aims to jointly optimize text detection and recognition within a unified framework. Despite significant progress, designing an accurate and efficient end-t…
TDR: Task-Decoupled Retrieval with Fine-Grained LLM Feedback for In-Context Learning
Yifu Chen, Bingchen Huang, Zhiling Wang +4
In-context learning (ICL) has become a classic approach for enabling LLMs to handle various tasks based on a few input-output examples. The effectiveness of ICL heavily relies on t…
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…
Distilling Knowledge from Heterogeneous Architectures for Semantic Segmentation
Yanglin Huang, Kai Hu, Yuan Zhang +2
Current knowledge distillation (KD) methods for semantic segmentation focus on guiding the student to imitate the teacher's knowledge within homogeneous architectures. However, the…
Explicit Relational Reasoning Network for Scene Text Detection
Yuchen Su, Zhineng Chen, Yongkun Du +4
Connected component (CC) is a proper text shape representation that aligns with human reading intuition. However, CC-based text detection methods have recently faced a developmenta…
TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition
Xingsong Ye, Yongkun Du, Yunbo Tao +1
Scene text recognition (STR) suffers from challenges of either less realistic synthetic training data or the difficulty of collecting sufficient high-quality real-world data, limit…
SVTRv2: CTC Beats Encoder-Decoder Models in Scene Text Recognition
Yongkun Du, Zhineng Chen, Hongtao Xie +2
Connectionist temporal classification (CTC)-based scene text recognition (STR) methods, e.g., SVTR, are widely employed in OCR applications, mainly due to their simple architecture…
Improving Text-guided Object Inpainting with Semantic Pre-inpainting
Yifu Chen, Jingwen Chen, Yingwei Pan +4
Recent years have witnessed the success of large text-to-image diffusion models and their remarkable potential to generate high-quality images. The further pursuit of enhancing the…
DreamMesh: Jointly Manipulating and Texturing Triangle Meshes for Text-to-3D Generation
Haibo Yang, Yang Chen, Yingwei Pan +5
Learning radiance fields (NeRF) with powerful 2D diffusion models has garnered popularity for text-to-3D generation. Nevertheless, the implicit 3D representations of NeRF lack expl…
Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models
Haibo Yang, Yang Chen, Yingwei Pan +4
Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, espec…
FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process
Yang Luo, Yiheng Zhang, Zhaofan Qiu +4
The emergence of text-to-image generation models has led to the recognition that image enhancement, performed as post-processing, would significantly improve the visual quality of…
CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer
Feiyang Jia, Zhineng Chen, Ziying Song +2
Super-resolution (SR) aims to enhance the quality of low-resolution images and has been widely applied in medical imaging. We found that the design principles of most existing meth…
Decoder Pre-Training with only Text for Scene Text Recognition
Shuai Zhao, Yongkun Du, Zhineng Chen +1
Scene text recognition (STR) pre-training methods have achieved remarkable progress, primarily relying on synthetic datasets. However, the domain gap between synthetic and real ima…
AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning
Xin Wang, Kai Chen, Xingjun Ma +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks even under a black-box setting where the adversary can only query the model. Particularly, query-based…