2 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
OmniSparse: Training-Aware Fine-Grained Sparse Attention for Long-Video MLLMs
Feng Chen, Yefei He, Shaoxuan He +9
Existing sparse attention methods primarily target inference-time acceleration by selecting critical tokens under predefined sparsity patterns. However, they often fail to bridge t…
Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding
Yun Li, Zhe Liu, Yajing Kong +6
Applying Multimodal Large Language Models (MLLMs) to video understanding presents significant challenges due to the need to model temporal relations across frames. Existing approac…
Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens
Feng Chen, Chenhui Gou, Jing Liu +6
As multimodal large language models (MLLMs) advance rapidly, rigorous evaluation has become essential, providing further guidance for their development. In this work, we focus on a…
Effectively Enhancing Vision Language Large Models by Prompt Augmentation and Caption Utilization
Minyi Zhao, Jie Wang, Zhaoyang Li +3
Recent studies have shown that Vision Language Large Models (VLLMs) may output content not relevant to the input images. This problem, called the hallucination phenomenon, undoubte…
CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning
Hongyu Hu, Jiyuan Zhang, Minyi Zhao +1
Nowadays, the research on Large Vision-Language Models (LVLMs) has been significantly promoted thanks to the success of Large Language Models (LLM). Nevertheless, these Vision-Lang…