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+ ---
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+ tags:
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+ - grounding
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+ - video
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+ - understanding
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+ - multimodal
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+ - MAD
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+ - long video
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+ - moments
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+ - moment retrieval
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+ license: bsd
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+ language:
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+ - en
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+ metrics:
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+ - recall
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+ pipeline_tag: text-to-video
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+ ---
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+ # Guidance Based Video Grounding.
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+
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+ The official implementation of the paper: ["Localizing Moments in Long Video Via Multimodal Guidance"](https://arxiv.org/abs/2302.13372). In this repository,
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+ we provide the predicted scores from the Guidance Model using [MAD Dataset](https://github.com/Soldelli/MAD).
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+
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+ ## Citation
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+ If you find this implementation useful in your research, please use the following BibTeX entry for citation:
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+ ```
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+ @article{Barrios2023LocalizingMI,
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+ title={Localizing Moments in Long Video Via Multimodal Guidance},
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+ author={Wayner Barrios and Mattia Soldan and Fabian Caba Heilbron and Alberto M. Ceballos-Arroyo and Bernard Ghanem},
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+ journal={ArXiv},
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+ year={2023},
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+ volume={abs/2302.13372}
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+ }
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+ ```
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+
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+ ## Prediction Zoo.
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+
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+ 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:
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+ ```python
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+ In [1]: import pickle
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+ In [2]: with open("guidance_scores_MAD_test_128.pkl",'rb') as f:
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+ ...: scores = pickle.load(f)
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+ In [3]: len(scores)
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+ Out[3]: 72044
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+ In [4]: scores[0].keys()
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+ Out[4]: dict_keys(['qid', 'vid', 'windows', 'score'])
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+ ```
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+ ```python
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+ { 'qid': '0',
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+ 'score': array([1.48404761e-05, 1.40372722e-05, 1.46572347e-05, 1.28814381e-05,
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+ 'vid': '3001_21_JUMP_STREET',
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+ 'windows': array([[ 0, 128],
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+ [ 64, 192],
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+ [ 128, 256],
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+ ...,
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+ [32704, 32832]])}
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+ ```
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+