7 papers · 1 filter
WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild?
An-Lan Wang, Jingqun Tang, Liao Lei +10
The rapid advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced capabilities in Document Understanding. However, prevailing benchmarks like DocVQA an…
Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting
Hao Feng, Shu Wei, Xiang Fei +10
Document image parsing is challenging due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Current approaches either assemble specializ…
Vision as LoRA
Han Wang, Yongjie Ye, Bingru Li +5
We introduce Vision as LoRA (VoRA), a novel paradigm for transforming an LLM into an MLLM. Unlike prevalent MLLM architectures that rely on external vision modules for vision encod…
Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLM
Han Wang, Yuxiang Nie, Yongjie Ye +6
The application of Large Vision-Language Models (LVLMs) for analyzing images and videos is an exciting and rapidly evolving field. In recent years, we've seen significant growth in…
What Makes Good Few-shot Examples for Vision-Language Models?
Zhaojun Guo, Jinghui Lu, Xuejing Liu +3
Despite the notable advancements achieved by leveraging pre-trained vision-language (VL) models through few-shot tuning for downstream tasks, our detailed empirical study highlight…
MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering
Jingqun Tang, Qi Liu, Yongjie Ye +14
Text-Centric Visual Question Answering (TEC-VQA) in its proper format not only facilitates human-machine interaction in text-centric visual environments but also serves as a de fac…