25 citations · 80 across the 20 of their papers we have counts for
7 papers · 2 filters
TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability
Shimin Chen, Xiaohan Lan, Yitian Yuan +2
Rapid development of large language models (LLMs) has significantly advanced multimodal large language models (LMMs), particularly in vision-language tasks. However, existing video…
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…
AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning
Duojun Huang, Xinyu Xiong, Jie Ma +4
Powered by massive curated training data, Segment Anything Model (SAM) has demonstrated its impressive generalization capabilities in open-world scenarios with the guidance of prom…
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…
InstaGen: Enhancing Object Detection by Training on Synthetic Dataset
Chengjian Feng, Yujie Zhong, Zequn Jie +2
In this paper, we present a novel paradigm to enhance the ability of object detector, e.g., expanding categories or improving detection performance, by training on synthetic datase…
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…