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20212026
most citedHandwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer

8 citations · 9 across the 7 of their papers we have counts for

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cs.CV2026

Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context

Zhaowei Wang, Lishu Luo, Haodong Duan +9

Long-context modeling is becoming a core capability of modern large vision-language models (LVLMs), enabling sustained context management across long-document understanding, video…

cs.CV2025

Clair Obscur: an Illumination-Aware Method for Real-World Image Vectorization

Xingyue Lin, Shuai Peng, Xiangyu Xie +3

Image vectorization aims to convert raster images into editable, scalable vector representations while preserving visual fidelity. Existing vectorization methods struggle to repres…

cs.CV2025

Uni-MuMER: Unified Multi-Task Fine-Tuning of Vision-Language Model for Handwritten Mathematical Expression Recognition

Yu Li, Jin Jiang, Jianhua Zhu +4

Handwritten Mathematical Expression Recognition (HMER) remains a persistent challenge in Optical Character Recognition (OCR) due to the inherent freedom of symbol layouts and varia…

cs.CV2024

Vote&Mix: Plug-and-Play Token Reduction for Efficient Vision Transformer

Shuai Peng, Di Fu, Baole Wei +3

Despite the remarkable success of Vision Transformers (ViTs) in various visual tasks, they are often hindered by substantial computational cost. In this work, we introduce Vote\&Mi…

cs.CV2024

SketchRef: a Multi-Task Evaluation Benchmark for Sketch Synthesis

Xingyue Lin, Xingjian Hu, Shuai Peng +2

Sketching is a powerful artistic technique for capturing essential visual information about real-world objects and has increasingly attracted attention in image synthesis research.…

cs.CV2021★ 8 cited

Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer

Wenqi Zhao, Liangcai Gao, Zuoyu Yan +3

Encoder-decoder models have made great progress on handwritten mathematical expression recognition recently. However, it is still a challenge for existing methods to assign attenti…