77 citations · 125 across the 23 of their papers we have counts for
4 papers · 1 filter
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
Mu Cai, Haotian Liu, Dennis Park +4
While existing large vision-language multimodal models focus on whole image understanding, there is a prominent gap in achieving region-specific comprehension. Current approaches t…
A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance
Zeyi Huang, Andy Zhou, Zijian Lin +3
Domain generalization studies the problem of training a model with samples from several domains (or distributions) and then testing the model with samples from a new, unseen domain…
Investigating the Catastrophic Forgetting in Multimodal Large Language Models
Yuexiang Zhai, Shengbang Tong, Xiao Li +4
Following the success of GPT4, there has been a surge in interest in multimodal large language model (MLLM) research. This line of research focuses on developing general-purpose LL…
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding
Mu Cai, Zeyi Huang, Yuheng Li +3
Large language models (LLMs) have made significant advancements in natural language understanding. However, through that enormous semantic representation that the LLM has learnt, i…