32 citations · 61 across the 5 of their papers we have counts for
5 papers
Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly
Yexin Liu, Zhengyang Liang, Yueze Wang +8
Multimodal Large Language Models (MLLMs) have displayed remarkable performance in multi-modal tasks, particularly in visual comprehension. However, we reveal that MLLMs often gener…
Efficient Multimodal Large Language Models: A Survey
Yizhang Jin, Jian Li, Yexin Liu +10
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning.…
Efficient Multimodal Learning from Data-centric Perspective
Muyang He, Yexin Liu, Boya Wu +4
Multimodal Large Language Models (MLLMs) have demonstrated notable capabilities in general visual understanding and reasoning tasks. However, their deployment is hindered by substa…
SVIT: Scaling up Visual Instruction Tuning
Bo Zhao, Boya Wu, Muyang He +1
Thanks to the emerging of foundation models, the large language and vision models are integrated to acquire the multimodal ability of visual captioning, question answering, etc. Al…
Large-scale Dataset Pruning with Dynamic Uncertainty
Muyang He, Shuo Yang, Tiejun Huang +1
The state of the art of many learning tasks, e.g., image classification, is advanced by collecting larger datasets and then training larger models on them. As the outcome, the incr…