7 papers
IMTBench: A Multi-Scenario Cross-Modal Collaborative Evaluation Benchmark for In-Image Machine Translation
Jiahao Lyu, Pei Fu, Zhenhang Li +7
End-to-end In-Image Machine Translation (IIMT) aims to convert text embedded within an image into a target language while preserving the original visual context, layout, and render…
PositionOCR: Augmenting Positional Awareness in Multi-Modal Models via Hybrid Specialist Integration
Chen Duan, Zhentao Guo, Pei Fu +3
In recent years, Multi-modal Large Language Models (MLLMs) have achieved strong performance in OCR-centric Visual Question Answering (VQA) tasks, illustrating their capability to p…
Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding
Zining Wang, Tongkun Guan, Pei Fu +7
Multi-modal Large Language Models (MLLMs) have introduced a novel dimension to document understanding, i.e., they endow large language models with visual comprehension capabilities…
A Token-level Text Image Foundation Model for Document Understanding
Tongkun Guan, Zining Wang, Pei Fu +9
In recent years, general visual foundation models (VFMs) have witnessed increasing adoption, particularly as image encoders for popular multi-modal large language models (MLLMs). H…
Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review
Pei Fu, Tongkun Guan, Zining Wang +8
The recent emergence of Multi-modal Large Language Models (MLLMs) has introduced a new dimension to the Text-rich Image Understanding (TIU) field, with models demonstrating impress…
InstructOCR: Instruction Boosting Scene Text Spotting
Chen Duan, Qianyi Jiang, Pei Fu +5
In the field of scene text spotting, previous OCR methods primarily relied on image encoders and pre-trained text information, but they often overlooked the advantages of incorpora…