HowToCaption: Prompting LLMs to Transform Video Annotations at Scale
arxiv
Vicuna-13B based captions:
HowToCaption dataset that we use in the paper can be found here ~1.5GB
Unfiltered HowToCaption with the corresponding scores based on BLIP model can be found here ~4.6GB
MiniGPT-4 based captions:
HowToCaption-grounded dataset can be found here ~1.5GB
Unfiltered HowToCaption-grounded dataset with corresponding scores based on BLIP model can be found here ~4.5GB
- Each file is a dictionary with video-ids as keys
- For each video we provide ‘start’, ‘end’, and ‘text’ lists of the same lengths
- ’start’ and ‘end’ correspond to starts and ends seconds of the clips in the video
To note:
- ‘text’ is list of lists of strings as to the same position in the video can correspond several captions
- Starting seconds in ‘start’ list are not ordered; however, ‘end’ seconds always correspond to ’start’ positions ordering
Example:
<<< HowToCaption[‘---39MFGZ-k’]
{
'start': [12, 19, 29, 25, 55, 81, 82],
'end': [20, 27, 37, 33, 63, 89, 90],
'text': [
[‘Show how to unload a 12-gauge shotgun’],
[‘Loading a 12-gauge shotgun’],
[‘Demonstrating how to unload a 12-gauge shotgun', 'A better way to unload a gun’],
[‘Putting another round into the gun', 'The danger of loading a gun the usual way’],
[‘Loading the gun safely', 'Short stroke to load the gun', 'Loading the gun today’],
[‘Lifting up the bar to extract rounds’],
[‘Going forward and lifting up the bar to extract rounds']
}
The difference to standard HowToCaption dataset is that ‘text’ is list of lists of tuples of (string, score).
Example:
<<< HowToCaption[‘---39MFGZ-k’]
{
'start': [12, 19, 25, 29, 55, 54, 65, 81, 82, 105, 103],
'end': [20, 27, 33, 37, 63, 62, 73, 89, 90, 113, 111],
'text': [
[('Show how to unload a 12-gauge shotgun', 0.5699871778488159)],
[('Loading a 12-gauge shotgun', 0.5876383185386658)],
[('Unloading and removing a round from the chamber', 0.31276029348373413), ('Putting another round into the gun', 0.4805337190628052), ('The danger of loading a gun the usual way', 0.4611629843711853)],
[('Demonstrating how to unload a 12-gauge shotgun', 0.617999255657196), ('A better way to unload a gun', 0.5126216411590576)],
[('Loading the gun safely', 0.539146363735199), ('Short stroke to load the gun', 0.5076732635498047), ('Loading the gun today', 0.4759426712989807)],
[('Being nervous on camera', 0.3465729355812073), ('Nervousness on camera', 0.27738460898399353)],
[('Extracting rounds by lifting up the bar', 0.41076189279556274)],
[('Lifting up the bar to extract rounds', 0.4220432639122009)],
[('Going forward and lifting up the bar to extract rounds', 0.42620745301246643)],
[('A person is speaking and pointing out that there are no ramps present', 0.30187565088272095)],
[('The speaker mentions that they can be found online', 0.30197498202323914), ('The speaker concludes the video by saying "WWE" and ending the video', 0.36031144857406616)]]
}
- BLIP is the model for text-video encoder and score function
- Vicuna is open source instructional LLM to generate HowToCaption dataset
- MiniGPT-4 is open-source LLM with image conditioning to generate HowToCaption-grounded dataset
If you're using HowToCaption or HowToCaption-grounded dataset in your research or applications, please cite using this BibTeX:
@article{shvetsova2023howtocaption,
title={HowToCaption: Prompting LLMs to Transform Video Annotations at Scale},
author={Shvetsova, Nina and Kukleva, Anna and Hong, Xudong and Rupprecht, Christian and Schiele, Bernt and Kuehne, Hilde},
journal={arXiv preprint arXiv:2310.04900},
year={2023}
}
HowToCaption and HowToCaption-grounded are based on Vicuna and MiniGpt-4 that are fine-tuned LLaMA and should be used under LLaMA's model license.
This repository is under Apache License.
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