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Improved Probabilistic Image-Text Representations (PCME++) (ICLR 2024)

Official Python implementation of PCME++ | Paper | Project page

This codebase is built upon the following repositories

Updates

08 Apr, 2024: HuggingFace model for ImageNet zero-shot is released. See hf_example.py for more details
07 Aug, 2023: Code is released!

Installation

Please check the library version before you run the code:

lightning==2.0.1
torch==2.0
torchtext==0.15.1
torchvision==0.15.1
transformers

Or, simply run pip install (I strongly recommend making a new virtual environment before you run this):

pip3 install -r requirements.txt

Dataset preparation

Step 1. Download COCO 2014 images from the official website: https://cocodataset.org/#download I may assume that your dataset file directory looks like

/path/to/dataset
└── images
    ├── train2014 # approximately 82k images are here
    └── val2014   # approximately 40k images are here

Step 2. Download annotation files from this link and untar the annotations to the dataset path. It will make your dataset file directory will be

/path/to/dataset
└── images
    └── ...
├── id_mapping.json # mapping file for image and captions
├── cxc_annots      # annotations for CxC evaluation of VSE infty codebase
└── precomp         # caption annotations are here
    ├── train_caps.txt
    ├── train_ids.txt
    ├── dev_caps.txt
    ├── dev_ids.txt
    ├── test_caps.txt
    ├── test_ids.txt
    ├── testall_caps.txt
    └── testall_ids.txt

Quick start

Most of the experiments are reproducible with a single V100. If you want to use multiple GPUs (e.g., larger batch size, or larger model), you should specify --train__dist_train option.
If you would like to run multiple experiments using this repository, it would be better to specify your expname using train__expname. The default expname is results, and all logs and weights will be dumped to results, if expname is not specified.

You can reproduce the main results by the following commands:

# PCME++ ViT-B/32 backbone
CUDA_VISIBLE_DEVICES=0 python3 train.py ./configs/pcmepp.yaml --dataloader__data_path /path/to/dataset

# PCME++ ViT-B/16 backbone
CUDA_VISIBLE_DEVICES=0 python3 train.py ./configs/pcmepp.yaml --dataloader__data_path /path/to/dataset --model__backbone_source clip_ViT-B/16

# PCME++ ViT-L/14 backbone
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 train.py ./configs/pcmepp.yaml --dataloader__data_path /path/to/dataset --model__backbone_source clip_ViT-L/14 --model__img_dim 1024 --dataloader__batch_size 16 --train__dist_train

This repository also provides noise ratio option as follows:

# PCME++ ViT-B/32 backbone with noise ratio 20%
CUDA_VISIBLE_DEVICES=0 python3 train.py ./configs/pcmepp.yaml --dataloader__data_path /path/to/dataset --dataloader__noise_ratio 0.2

# PCME++ ViT-B/32 backbone with noise ratio 50%
CUDA_VISIBLE_DEVICES=0 python3 train.py ./configs/pcmepp.yaml --dataloader__data_path /path/to/dataset --dataloader__noise_ratio 0.5

You can train the baselines methods using the following commands:

# ViT-B/32 backbones. Changing backbone is the same as the PCME++ backbone changes
CUDA_VISIBLE_DEVICES=0 python3 train.py ./configs/others/vse_infty.yaml --dataloader__data_path /path/to/dataset
CUDA_VISIBLE_DEVICES=0 python3 train.py ./configs/others/info_nce.yaml --dataloader__data_path /path/to/dataset
CUDA_VISIBLE_DEVICES=0 python3 train.py ./configs/others/pcmepp_mu_only.yaml --dataloader__data_path /path/to/dataset
CUDA_VISIBLE_DEVICES=0 python3 train.py ./configs/others/pcme.yaml --dataloader__data_path /path/to/dataset

# only exception is InfoNCE + multiple GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 train.py ./configs/others/info_nce.yaml --dataloader__data_path /path/to/dataset --model__backbone_source clip_ViT-L/14 --model__img_dim 1024 --dataloader__batch_size 16 --train__dist_train --train__all_gather_infonce

Official weights

We will provide the official weights for each model in the paper.

How to cite

@inproceedings{chun2024pcmepp,
    title={Improved Probabilistic Image-Text Representations},
    author={Chun, Sanghyuk},
    year={2024},
    booktitle={International Conference on Learning Representations (ICLR)},
}

I would like to suggest citing PCME and ECCV Caption, too.

@inproceedings{chun2021pcme,
    title={Probabilistic Embeddings for Cross-Modal Retrieval},
    author={Chun, Sanghyuk and Oh, Seong Joon and De Rezende, Rafael Sampaio and Kalantidis, Yannis and Larlus, Diane},
    year={2021},
    booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
}

@inproceedings{chun2022eccv_caption,
    title={ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCO}, 
    author={Chun, Sanghyuk and Kim, Wonjae and Park, Song and Chang, Minsuk Chang and Oh, Seong Joon},
    year={2022},
    booktitle={European Conference on Computer Vision (ECCV)},
}

License

MIT License

Copyright (c) 2023-present NAVER Cloud Corp.

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The above copyright notice and this permission notice shall be included in
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
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