From Within to Between: Knowledge Distillation for Cross Modality Retrieval

Dependencies

The code assumes PyTorch 1.4 and Python 3.7 (other versions may work, but have not been tested).

Dependencies can be installed via:

pip install -r requirements/pip-requirements.txt

Model Zoo

Please note that the numbers are slightly different in the paper due to compression artifacts correction.

MSRVTT

Model	Distillation	Task	R@1	R@5	R@10	R@50	MdR	Links
CE+	Caption	t2v	14.5	37.7	50.6	78.5	10.0	config, model
TeachText - CE+	Caption	t2v	14.8	38.1	51.1	79.1	10.0	config, model

MSVD

Model	Distillation	Task	R@1	R@5	R@10	R@50	MdR	Links
CE+	Caption	t2v	25.9	58.3	72.7	93.3	4.0	config, model
TeachText - CE+	Caption	t2v	25.6	57.1	71.4	92.9	4.0	config, model

DiDeMo

Model	Distillation	Task	R@1	R@5	R@10	R@50	MdR	Links
CE+	Caption	t2v	19.5	44.6	59.0	83.0	7.0	config, model
TeachText - CE+	Caption	t2v	22.2	49.0	61.4	86.4	6.0	config, model

Activity-Net

Model	Distillation	Task	R@1	R@5	R@10	R@50	MdR	Links
CE+	Caption	t2v	20.0	50.8	67.3	93.7	5.0	config, model
TeachText - CE+	Caption	t2v	23.8	56.9	73.2	96.2	4.0	config, model

Data for training

You can download the high quality features used for TeachText from:

For MSRVTT:
http:/www.robots.ox.ac.uk/~vgg/research/teachtext/data-hq/high-quality/high-quality-MSRVTT-experts.tar.gz
sha1sum: 734650c3b98509996da75cdedc12101836624917

For MSVD:
http:/www.robots.ox.ac.uk/~vgg/research/teachtext/data-hq/high-quality/high-quality-MSVD-experts.tar.gz
sha1sum: c8eba8c5291dd6bb501757ed0cc327cd22217965

For DiDeMo:
http:/www.robots.ox.ac.uk/~vgg/research/teachtext/data-hq/high-quality/high-quality-DiDeMo-experts.tar.gz
sha1sum: 8e128309f12cf3260fe538f82578b5ad91a46bd0

For ActivityNet:
http:/www.robots.ox.ac.uk/~vgg/research/teachtext/data-hq/high-quality/high-quality-activity-net-experts.tar.gz
sha1sum: 2f3c7c2fe86bd6d0c6230464a940c429291a4012

Evaluating a pretrained model

Evaluating a pretrained model for a given dataset requires:

1. The pretrained experts for the target dataset, which should be located in <root>/data/<dataset-name>/symlinked-feats.
2. A config.json file.
3. A trained_model.pth file.

Evaluation is then performed with the following command:

python3 test.py --config <path-to-config.json> --resume <path-to-trained_model.pth> --device <gpu-id> --eval_from_training_config

where <gpu-id> is the index of the GPU to evaluate on.

Training a new model

Training a new video-text embedding requires:

1. The pretrained experts for the dataset used for training, which should be located in <root>/data/<dataset-name>/symlinked-feats.
2. A config.json file. You can define your own, or use one of the provided configs in the configs directory.

Training is then performed with the following command:

python3 train.py --config <path-to-config.json> --device <gpu-id>

where <gpu-id> is the index of the GPU to train on.

References

If you find this code useful or use the extracted features, please consider citing:

@inproceedings{Tran-et-al-ACCV22,
  author    = {Tran, Vinh and Balasubramanian, Niranjan and and Hoai, Minh},
  booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
  title     = {From Within to Between: Knowledge Distillation for Cross Modality Retrieval},
  month     = {December},
  date      = {2022},
}

Acknowledgements

This codebase was built on top of TeachText and Collaborative Experts. Many thanks to the authors.