Memory-Augmented Query Intent Understanding for Efficient Chat-based Image Retrieval

Datasets

Download the extracted features from Google drive and place the data in the file images.

- VisDial
  ├── images
      ├── corpus_blip.pth
      └── visdial_img_train.pth
      └── visdial_img_val.pth

Environments

• Ubuntu 20.04
• CUDA 12.6
• Python 3.10

Use the following instructions to create the corresponding conda environment.
Please make sure to download the required pretrained models (e.g., BLIP) from their official sources before running the training or evaluation scripts.

conda create --name maqiu python=3.10 -y
conda activate maqiu
pip install -r requirements.txt

Training and Evaluation

Run the following script for multi-GPU finetuning on VisDial:

./train.sh $run_id

Argument meaning

• $run_id — folder name for saving checkpoints and logs

Example

./train.sh run_0

Run the following script for evaluation:

./eval.sh $run_id

Example

./eval.sh run_0