InstructBLIP: Towards General-purpose Vision-Language Models with
  Instruction Tuning
Authors: name: Wenliang Dai, name: Junnan Li, name: Dongxu Li, name: Anthony Meng Huat Tiong, name: Junqi Zhao, name: Weisheng Wang, name: Boyang Li, name: Pascale Fung, name: Steven Hoi

General-purpose language models that can solve various language-domain tasks
have emerged driven by the pre-training and instruction-tuning pipeline.
However, building general-purpose vision-language models is challenging due to
the increased task discrepancy introduced by the additional visual input.
Although vision-language pre-training has been widely studied, vision-language
instruction tuning remains relatively less explored. In this paper, we conduct
a systematic and comprehensive study on vision-language instruction tuning
based on the pre-trained BLIP-2 models. We gather a wide variety of 26 publicly
available datasets, transform them into instruction tuning format and
categorize them into two clusters for held-in instruction tuning and held-out
zero-shot evaluation. Additionally, we introduce instruction-aware visual
feature extraction, a crucial method that enables the model to extract
informative features tailored to the given instruction. The resulting
InstructBLIP models achieve state-of-the-art zero-shot performance across all
13 held-out datasets, substantially outperforming BLIP-2 and the larger
Flamingo. Our models also lead to state-of-the-art performance when finetuned
on individual downstream tasks (e.g., 90.7% accuracy on ScienceQA IMG).
Furthermore, we qualitatively demonstrate the advantages of InstructBLIP over
concurrent multimodal models. All InstructBLIP models have been open-sourced at
https://github.com/salesforce/LAVIS/tree/main/projects/instructblip.