VLLaVO: Mitigating Visual Gap through LLMs

This is the offical code of VLLaVO.

Firstly, we extract the descriptions of an images by VLMs (CLIP and BLIP), then finetuning LLM (LLaMA) with the descriptions. The finetuned LLM can be used to do classification.

Prepare model

All model we used in the following list:

• CLIP https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K
• BLIP https://huggingface.co/Salesforce/blip-image-captioning-large
• LLaMA2-7B-chat-hf https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
• FLAN-T5 https://huggingface.co/t5-base

Prepare dataset

Directly download existing dataset

We offer the dataset used in our paper on the following link: dataset link

Description Extract

If you want to extract the descriptions to construct the dataset by yourself, the following codes can be used.

CUDA_VISIBLE_DEVICES=1 python descriptions_extractor.py -s dataset/office_home/image_list/Product.txt --save_path ../datasets/Office_home --base_path dataset/office_home/

Finetune

See DG_llama.sh in ./script/bash_command for LLM model llama2.

Evaluate

See classification_llama.sh for LLM model llama2.