4 citations · 9 across the 13 of their papers we have counts for
14 papers
Moment-Video: Diagnosing Temporal Fidelity of Video MLLMs on Momentary Visual Events
Xiaolin Liu, Yilun Zhu, Xiangyu Zhao +9
Video multimodal large language models (MLLMs) have made rapid progress in general and long-form video understanding, yet their ability to capture and use brief, answer-critical vi…
DenseMLLM: Standard Multimodal LLMs for Dense Prediction
Yi Li, Hongze Shen, Lexiang Tang +6
Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in high-level visual understanding. However, extending these models to fine-grained dense predic…
Youtu-VL: Unleashing Visual Potential via Unified Vision-Language Supervision
Zhixiang Wei, Yi Li, Zhehan Kan +38
Despite the significant advancements represented by Vision-Language Models (VLMs), current architectures often exhibit limitations in retaining fine-grained visual information, lea…
Youtu-Parsing: Perception, Structuring and Recognition via High-Parallelism Decoding
Haoyu Cao, Kun Yin, Yunfei Wu +16
This paper presents Youtu-Parsing, an efficient and versatile document parsing model designed for high-performance content extraction. The architecture employs a native Vision Tran…
DREAM: Document Reconstruction via End-to-end Autoregressive Model
Xin Li, Mingming Gong, Yunfei Wu +7
Document reconstruction constitutes a significant facet of document analysis and recognition, a field that has been progressively accruing interest within the scholarly community.…
TACO: Think-Answer Consistency for Optimized Long-Chain Reasoning and Efficient Data Learning via Reinforcement Learning in LVLMs
Zhehan Kan, Yanlin Liu, Kun Yin +8
DeepSeek R1 has significantly advanced complex reasoning for large language models (LLMs). While recent methods have attempted to replicate R1's reasoning capabilities in multimoda…
HRVDA: High-Resolution Visual Document Assistant
Chaohu Liu, Kun Yin, Haoyu Cao +6
Leveraging vast training data, multimodal large language models (MLLMs) have demonstrated formidable general visual comprehension capabilities and achieved remarkable performance a…
Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models
Xin Li, Yunfei Wu, Xinghua Jiang +6
Recently, the advent of Large Visual-Language Models (LVLMs) has received increasing attention across various domains, particularly in the field of visual document understanding (V…
Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA
Yongxin Zhu, Zhen Liu, Yukang Liang +4
In this paper, we propose a novel multi-modal framework for Scene Text Visual Question Answering (STVQA), which requires models to read scene text in images for question answering.…
Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation
Hao Liu, Xin Li, Mingming Gong +5
Recently, Table Structure Recognition (TSR) task, aiming at identifying table structure into machine readable formats, has received increasing interest in the community. While impr…
Relational Representation Learning in Visually-Rich Documents
Xin Li, Yan Zheng, Yiqing Hu +5
Relational understanding is critical for a number of visually-rich documents (VRDs) understanding tasks. Through multi-modal pre-training, recent studies provide comprehensive cont…
The Devil is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training
Hao Liu, Xinghua Jiang, Xin Li +3
The self-supervised Masked Image Modeling (MIM) schema, following "mask-and-reconstruct" pipeline of recovering contents from masked image, has recently captured the increasing int…
Neural Collaborative Graph Machines for Table Structure Recognition
Hao Liu, Xin Li, Bing Liu +3
Recently, table structure recognition has achieved impressive progress with the help of deep graph models. Most of them exploit single visual cues of tabular elements or simply com…
NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition
Hao Liu, Xinghua Jiang, Xin Li +3
Recently, Vision Transformers (ViT), with the self-attention (SA) as the de facto ingredients, have demonstrated great potential in the computer vision community. For the sake of t…