26 citations · 91 across the 39 of their papers we have counts for
6 papers · 1 filter
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…