924 citations · 4.2k across the 19 of their papers we have counts for
5 papers · 1 filter
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models
Jiaxian Guo, Junnan Li, Dongxu Li +4
Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answer…
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
Anthony Meng Huat Tiong, Junnan Li, Boyang Li +2
Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting. We propose Plug-and-Play VQA (PNP-VQA), a modular…
LAVIS: A Library for Language-Vision Intelligence
Dongxu Li, Junnan Li, Hung Le +3
We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent a…
Masked Unsupervised Self-training for Label-free Image Classification
Junnan Li, Silvio Savarese, Steven C. H. Hoi
State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost.…
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Junnan Li, Dongxu Li, Caiming Xiong +1
Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based t…