We are excited to release a new video-text benchmark for multi-shot video understanding. This release contains a 134k version of our dataset. It includes detailed long summaries (human annotated + GPTV generated) for 134k videos and shot captions (human annotated) for 188k video shots. Please check the dataset
here
.
{20k,134k}-version/sum_shot_best_epoch.pth:
Model tuned on our multi-shot summary data. Used in the config files
ckpt
.
{20k,134k}-version/shot_av_best_epoch.pth:
Model trained on our single-shot caption data. Used in the config files
ckpt
.
transnetv2-pytorch-weights.pth:
Checkpoint used for automatic shot detection method, which is used in the Bot demo. Please following the original license of the TransNetv2.
BLIP.cache.tar:
Cached checkpoints for training, testing and offline demos. This is only to ease the usage case that servers can't access huggingface. Please be restriected the original license to the different models.
Users must refer to
HD-VILA-100M
for original video access. By downloading our annotations, you agree to these terms. Respect for video copyright holders is paramount. Ensure your use of the videos aligns with the original source's terms.
Citation
If you find our work useful for your research, please consider citing the paper
@misc{han2023shot2story20k,
title={Shot2Story20K: A New Benchmark for Comprehensive Understanding of Multi-shot Videos},
author={Mingfei Han and Linjie Yang and Xiaojun Chang and Heng Wang},
year={2023},
eprint={2312.10300},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Runs of ByteDance shot2story on huggingface.co
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More Information About shot2story huggingface.co Model
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