198 citations · 342 across the 12 of their papers we have counts for
13 papers
GeoFocus: Blending Efficient Global-to-Local Perception for Multimodal Geometry Problem-Solving
Linger Deng, Yuliang Liu, Wenwen Yu +4
Geometry problem-solving remains a significant challenge for Large Multimodal Models (LMMs), requiring not only global shape recognition but also attention to intricate local relat…
DocThinker: Explainable Multimodal Large Language Models with Rule-based Reinforcement Learning for Document Understanding
Wenwen Yu, Zhibo Yang, Yuliang Liu +1
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in document understanding. However, their reasoning processes remain largely black-box, making it…
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models
Wenwen Yu, Zhibo Yang, Jianqiang Wan +5
Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understanding and the emergence of large language…
OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition
Jianqiang Wan, Sibo Song, Wenwen Yu +6
Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Gene…
Turning a CLIP Model into a Scene Text Spotter
Wenwen Yu, Yuliang Liu, Xingkui Zhu +3
We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP) model to enhance scene text detection and spotting tasks, transforming it into a robust ba…
Looking and Listening: Audio Guided Text Recognition
Wenwen Yu, Mingyu Liu, Biao Yang +5
Text recognition in the wild is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest vision and language processing are effective…
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
Wenwen Yu, Chengquan Zhang, Haoyu Cao +24
Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, an…
OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models
Yuliang Liu, Zhang Li, Mingxin Huang +7
Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks…
ICDAR 2023 Competition on Reading the Seal Title
Wenwen Yu, Mingyu Liu, Mingrui Chen +5
Reading seal title text is a challenging task due to the variable shapes of seals, curved text, background noise, and overlapped text. However, this important element is commonly f…
Turning a CLIP Model into a Scene Text Detector
Wenwen Yu, Yuliang Liu, Wei Hua +3
The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown great potential in various downstream tasks via leveraging the pretrained vision and language k…
Unsupervised Domain Adaptation Network with Category-Centric Prototype Aligner for Biomedical Image Segmentation
Ping Gong, Wenwen Yu, Qiuwen Sun +2
With the widespread success of deep learning in biomedical image segmentation, domain shift becomes a critical and challenging problem, as the gap between two domains can severely…
PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks
Wenwen Yu, Ning Lu, Xianbiao Qi +2
Computer vision with state-of-the-art deep learning models has achieved huge success in the field of Optical Character Recognition (OCR) including text detection and recognition ta…
MASTER: Multi-Aspect Non-local Network for Scene Text Recognition
Ning Lu, Wenwen Yu, Xianbiao Qi +4
Attention-based scene text recognizers have gained huge success, which leverages a more compact intermediate representation to learn 1d- or 2d- attention by a RNN-based encoder-dec…