Official Pytorch implementation of PCME | Paper
Sanghyuk Chun1 Seong Joon Oh1 Rafael Sampaio de Rezende2 Yannis Kalantidis2 Diane Larlus2
1NAVER AI LAB
2NAVER LABS Europe
- Jan 2024: PCME++, the improved version of PCME, is accepted at ICLR 2024. Please use naver-ai/pcmepp for the improved version!
- 16 Jul, 2022: Add PCME CutMix-pretrained weight (used for ECCV Caption paper)
- 23 Jun, 2021: Initial upload.
Install dependencies using the following command.
pip install cython && pip install -r requirements.txt
python -c 'import nltk; nltk.download("punkt", download_dir="/opt/conda/nltk_data")'
git clone https://github.com/NVIDIA/apex && cd apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
You can use my docker image as well
docker pull sanghyukchun/pcme:torch1.2-apex-dali
Please Add --model__cache_dir /vector_cache when you run the code
All experiments are based on configuration files (see config/coco and config/cub). If you want to change only a few options, instead of re-writing a new configuration file, you can override the configuration as the follows:
python <train | eval>.py --dataloader__batch_size 32 --dataloader__eval_batch_size 8 --model__eval_method matching_prob
See config/parser.py for details
We followed the same split provided by VSE++. Dataset splits can be found in datasets/annotations.
Note that we also need instances_<train | val>2014.json for computing PMRP score.
Download images (CUB-200-2011) from this link, and download caption from reedscot/cvpr2016. You can use the image path and the caption path separately in the code.
NOTE: the current implementation of plausible match R-Precision (PMRP) is not efficient:
It first dumps all ranked items for each item to a local file, and compute R-precision.
We are planning to re-implement efficient PMRP as soon as possible.
# Compute recall metrics
python evaluate_recall_coco.py ./config/coco/pcme_coco.yaml \
--dataset_root <your_dataset_path> \
--model_path model_last.pth \
# --model__cache_dir /vector_cache # if you use my docker image
# Compute plausible match R-Precision (PMRP) metric
python extract_rankings_coco.py ./config/coco/pcme_coco.yaml \
--dataset_root <your_dataset_path> \
--model_path model_last.pth \
--dump_to <dumped_ranking_file> \
# --model__cache_dir /vector_cache # if you use my docker image
python evaluate_pmrp_coco.py --ranking_file <dumped_ranking_file>
| Method | I2T 1K PMRP | I2T 1K R@1 | I2T ECCV mAP@R | T2I 1K PMRP | T2I 1K R@1 | T2I ECCV mAP@R | Model file |
|---|---|---|---|---|---|---|---|
| PCME | 45.0 | 68.8 | 26.2 | 46.0 | 54.6 | 48.0 | link |
| PCME (CutMix-pretrained) | 46.2 | 68.3 | 28.6 | 47.1 | 56.7 | 54.9 | link |
| PVSE K=1 | 40.3 | 66.7 | 23.4 | 41.8 | 53.5 | 44.6 | - |
| PVSE K=2 | 42.8 | 69.2 | 26.7 | 43.6 | 55.2 | 53.8 | - |
| VSRN | 41.2 | 76.2 | 30.8 | 42.4 | 62.8 | 53.8 | - |
| VSRN + AOQ | 44.7 | 77.5 | 30.7 | 45.6 | 63.5 | 51.2 | - |
Check ECCV Caption dataset for more details of "ECCV mAP@R".
- Paper: ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCO
- GitHub: naver-ai/eccv-caption
python evaluate_cub.py ./config/cub/pcme_cub.yaml \
--dataset_root <your_dataset_path> \
--caption_root <your_caption_path> \
--model_path model_last.pth \
# --model__cache_dir /vector_cache # if you use my docker image
NOTE: If you just download file from reedscot/cvpr2016, then caption_root will be cvpr2016_cub/text_c10
If you want to test other probabilistic distances, such as Wasserstein distance or KL-divergence, try the following command:
python evaluate_cub.py ./config/cub/pcme_cub.yaml \
--dataset_root <your_dataset_path> \
--caption_root <your_caption_path> \
--model_path model_last.pth \
--model__eval_method <distance_method> \
# --model__cache_dir /vector_cache # if you use my docker image
You can choose distance_method in ['elk', 'l2', 'min', 'max', 'wasserstein', 'kl', 'reverse_kl', 'js', 'bhattacharyya', 'matmul', 'matching_prob']
NOTE: we train each model with mixed-precision training (O2) on a single V100.
Since, the current code does not support multi-gpu training, if you use different hardware, the batchsize should be reduced.
Please note that, hence, the results couldn't be reproduced if you use smaller hardware than V100.
python train_coco.py ./config/coco/pcme_coco.yaml --dataset_root <your_dataset_path> \
# --model__cache_dir /vector_cache # if you use my docker image
It takes about 46 hours in a single V100 with mixed precision training.
We use CUB Caption dataset (Reed, et al. 2016) as a new cross-modal retrieval benchmark. Here, instead of matching the sparse paired image-caption pairs, we treat all image-caption pairs in the same class as positive. Since our split is based on the zero-shot learning benchmark (Xian, et al. 2017), we leave out 50 classes from 200 bird classes for the evaluation.
- Reed, Scott, et al. "Learning deep representations of fine-grained visual descriptions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Xian, Yongqin, Bernt Schiele, and Zeynep Akata. "Zero-shot learning-the good, the bad and the ugly." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.
We additionally use cross-validation splits by (Xian, et el. 2017), namely using 100 classes for training and 50 classes for validation.
python train_cub.py ./config/cub/pcme_cub.yaml \
--dataset_root <your_dataset_path> \
--caption_root <your_caption_path> \
--dataset_name cub_trainval1 \
# --model__cache_dir /vector_cache # if you use my docker image
Similarly, you can use cub_trainval2 and cub_trainval3 as well.
python train_cub.py ./config/cub/pcme_cub.yaml \
--dataset_root <your_dataset_path> \
--caption_root <your_caption_path> \
# --model__cache_dir /vector_cache # if you use my docker image
It takes about 4 hours in a single V100 with mixed precision training.
@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)},
}
I would like to suggest citing ECCV Caption and PCME++, too.
@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)},
}
@inproceedings{chun2024pcmepp,
title={Improved Probabilistic Image-Text Representations},
author={Chun, Sanghyuk},
year={2024},
booktitle={International Conference on Learning Representations (ICLR)},
}
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