43 citations · 83 across the 36 of their papers we have counts for
4 papers · 1 filter
Ferret: Refer and Ground Anything Anywhere at Any Granularity
Haoxuan You, Haotian Zhang, Zhe Gan +6
We introduce Ferret, a new Multimodal Large Language Model (MLLM) capable of understanding spatial referring of any shape or granularity within an image and accurately grounding op…
VeCLIP: Improving CLIP Training via Visual-enriched Captions
Zhengfeng Lai, Haotian Zhang, Bowen Zhang +9
Large-scale web-crawled datasets are fundamental for the success of pre-training vision-language models, such as CLIP. However, the inherent noise and potential irrelevance of web-…
Compressing LLMs: The Truth is Rarely Pure and Never Simple
Ajay Jaiswal, Zhe Gan, Xianzhi Du +3
Despite their remarkable achievements, modern Large Language Models (LLMs) face exorbitant computational and memory footprints. Recently, several works have shown significant succe…
Guiding Instruction-based Image Editing via Multimodal Large Language Models
Tsu-Jui Fu, Wenze Hu, Xianzhi Du +3
Instruction-based image editing improves the controllability and flexibility of image manipulation via natural commands without elaborate descriptions or regional masks. However, h…