6 citations · 19 across the 14 of their papers we have counts for
5 papers · 2 filters
Making Large Language Models Better Planners with Reasoning-Decision Alignment
Zhijian Huang, Tao Tang, Shaoxiang Chen +5
Data-driven approaches for autonomous driving (AD) have been widely adopted in the past decade but are confronted with dataset bias and uninterpretability. Inspired by the knowledg…
MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis
Lei Chen, Feng Yan, Yujie Zhong +3
Multimodal Large Language Models (MLLM) have made significant progress in the field of document analysis. Despite this, existing benchmarks typically focus only on extracting text…
Fewer Tokens and Fewer Videos: Extending Video Understanding Abilities in Large Vision-Language Models
Shimin Chen, Yitian Yuan, Shaoxiang Chen +2
Amidst the advancements in image-based Large Vision-Language Models (image-LVLM), the transition to video-based models (video-LVLM) is hindered by the limited availability of quali…
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
Yang Jiao, Shaoxiang Chen, Zequn Jie +3
Large Multimodal Model (LMM) is a hot research topic in the computer vision area and has also demonstrated remarkable potential across multiple disciplinary fields. A recent trend…
LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs
Shaoxiang Chen, Zequn Jie, Lin Ma
Instruction finetuning on a variety of image-text instruction data is the key to obtaining a versatile Multimodal Large Language Model (MLLM), and different configurations of the i…