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license: cc-by-4.0
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license: cc-by-4.0
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---
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# ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video(ECCV2024)
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This repo is the official model checkpoints of ["ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video"](https://arxiv.org/abs/2310.01324)(ECCV2024)
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## Models
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We provide the checkpoints before reparameterization, you could reparameter the weight refer to `tools\weight_reparam.py` in our [codes](https://github.com/MCG-NJU/ZeroI2V/blob/main/tools/weight_reparam.py).
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### Kinetics 400
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| Backbone | Pretrain | GFLOPs | Param | New Param (M) | acc@1 | Views
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| ViT-B/16 | CLIP | 422 | 86 | 0 | 83.0 | 8x1x3 |
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| ViT-L/14 | CLIP | 1946 | 304 | 0 | 86.3 | 8x1x3 |
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| ViT-L/14 | CLIP | 7783 | 304 | 0 | 87.2 | 32x1x3 |
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### Something Something V2
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| Backbone | Pretrain | GFLOPs | Param | New Param (M) | acc@1 | Views |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| ViT-L/14 | CLIP | 7783 | 304 | 0 | 72.2 | 32x3x1 |
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If you find our work useful in your research, please cite:
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```
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@article{li2023zeroi2v,
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title={ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video},
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author={Li, Xinhao and Zhu, Yuhan and Wang, Limin},
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journal={arXiv preprint arXiv:2310.01324},
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year={2023}
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}
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```
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