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waybarrios/guidance-based-video-grounding
guidance-based-video-grounding is a machine learning model from waybarrios. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as bsd.
The official implementation of the paper: "Localizing Moments in Long Video Via Multimodal Guidance". In this repository, we provide the predicted scores from the Guidance Model using MAD Dataset.
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From the Hugging Face model README
The official implementation of the paper: "Localizing Moments in Long Video Via Multimodal Guidance". In this repository, we provide the predicted scores from the Guidance Model using MAD Dataset.
If you find this implementation useful in your research, please use the following BibTeX entry for citation:
@article{Barrios2023LocalizingMI,
title={Localizing Moments in Long Video Via Multimodal Guidance},
author={Wayner Barrios and Mattia Soldan and Fabian Caba Heilbron and Alberto M. Ceballos-Arroyo and Bernard Ghanem},
journal={ArXiv},
year={2023},
volume={abs/2302.13372}
}
The provided predictions correspond to the scores generated by the Guidance model using sliding windows of 64 frames and 128 frames in length. The predictions are stored in a pickle object with the following structure:
In [1]: import pickle
In [2]: with open("guidance_scores_MAD_test_128.pkl",'rb') as f:
...: scores = pickle.load(f)
In [3]: len(scores)
Out[3]: 72044
In [4]: scores[0].keys()
Out[4]: dict_keys(['qid', 'vid', 'windows', 'score'])
{ 'qid': '0',
'score': array([1.48404761e-05, 1.40372722e-05, 1.46572347e-05, 1.28814381e-05,
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9.86061990e-01, 4.11081433e-01, 1.71889886e-02, 1.37453452e-01,
1.75393507e-05, 1.92647931e-05, 5.38236709e-05, 6.90551009e-04,
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dtype=float32),
'vid': '3001_21_JUMP_STREET',
'windows': array([[ 0, 128],
[ 64, 192],
[ 128, 256],
...,
[32576, 32704],
[32640, 32768],
[32704, 32832]])}