hhc1997/l2rm

PyTorch implementation for "Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval (CVPR 2024)"

Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval

Requirements

• Python 3.8
• torch 1.12
• numpy
• scikit-learn
• pomegranate Install. Note that pomegranate requires Cython=0.29, NumPy, SciPy, NetworkX, and joblib. Then you can run python setup.py build and python setup.py install to install it.)
• Punkt Sentence Tokenizer:

import nltk
nltk.download()
> d punkt

Datasets

We follow NCR to obtain image features and vocabularies.

Download Dataset

Noise (Mismatching) Index

We use the same noise index settings as DECL and RCL, which could be found in noise_index. The mismatching ratio (noise ratio) is set as 0.2, 0.4, 0.6, and 0.8.

Training and Evaluation

Training new models

Modify some necessary parameters and run it.

For Flickr30K:

sh train_f30k.sh

For MS-COCO:

sh train_coco.sh

For CC152K:

sh train_cc152k.sh

Evaluation

Modify some necessary parameters and run it.

python main_testing.py

Pre-trained L2RM models

The pre-trained models are available here.

License

Apache License 2.0

Acknowledgements

The code is based on SCAN, SGRAF, NCR, DECL, and KPG-RL licensed under Apache 2.0.