12 citations · 22 across the 6 of their papers we have counts for
6 papers
MonkeyOCRv2: A Visual-Text Foundation Model for Document AI
Yuliang Liu, Zhang Li, Ziyang Zhang +11
Mainstream visual encoders are pretrained on natural images and cannot be effectively applied to document images without document-oriented adaptation, as dense text and fine-graine…
LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance
Zhang Li, Biao Yang, Qiang Liu +7
While large multi-modal models (LMMs) demonstrate promising capabilities in segmentation and comprehension, they still struggle with two limitations: inaccurate segmentation and ha…
MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm
Zhang Li, Yuliang Liu, Qiang Liu +8
We introduce MonkeyOCR, a document parsing model that advances the state of the art by leveraging a Structure-Recognition-Relation (SRR) triplet paradigm. This design simplifies wh…
Exploring the Capabilities of Large Multimodal Models on Dense Text
Shuo Zhang, Biao Yang, Zhang Li +3
While large multi-modal models (LMM) have shown notable progress in multi-modal tasks, their capabilities in tasks involving dense textual content remains to be fully explored. Den…
TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document
Yuliang Liu, Biao Yang, Qiang Liu +4
We present TextMonkey, a large multimodal model (LMM) tailored for text-centric tasks. Our approach introduces enhancement across several dimensions: By adopting Shifted Window Att…
Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models
Zhang Li, Biao Yang, Qiang Liu +6
Large Multimodal Models (LMMs) have shown promise in vision-language tasks but struggle with high-resolution input and detailed scene understanding. Addressing these challenges, we…