7 citations · 17 across the 64 of their papers we have counts for
5 papers · 1 filter
Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs
Yonghui Wang, Wengang Zhou, Hao Feng +2
In the field of document understanding, significant advances have been made in the fine-tuning of Multimodal Large Language Models (MLLMs) with instruction-following data. Neverthe…
DanZero+: Dominating the GuanDan Game through Reinforcement Learning
Youpeng Zhao, Yudong Lu, Jian Zhao +2
The utilization of artificial intelligence (AI) in card games has been a well-explored subject within AI research for an extensive period. Recent advancements have propelled AI pro…
DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding
Hao Feng, Qi Liu, Hao Liu +4
This work presents DocPedia, a novel large multimodal model (LMM) for versatile OCR-free document understanding, capable of parsing images up to 2,5602,560 resolution. Unli…
State Sequences Prediction via Fourier Transform for Representation Learning
Mingxuan Ye, Yufei Kuang, Jie Wang +4
While deep reinforcement learning (RL) has been demonstrated effective in solving complex control tasks, sample efficiency remains a key challenge due to the large amounts of data…
IMD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation
Yunyao Mao, Jiajun Deng, Wengang Zhou +3
Recent progresses on self-supervised 3D human action representation learning are largely attributed to contrastive learning. However, in conventional contrastive frameworks, the ri…