Pengfei Wei¹   Lingdong Kong^1,2   Xinghua Qu¹   Yi Ren¹   Zhiqiang Xu³   Jing Jiang⁴   Xiang Yin^1
1ByteDance AI Lab   ²National University of Singapore   ³MBZUAI   ⁴University of Technology Sydney

NeurIPS 2023

TranSVAE is a disentanglement framework designed for unsupervised video domain adaptation. It aims at disentangling the domain information from the data during the adaptation process. We consider the generation of cross-domain videos from two sets of latent factors: one encoding the static domain-related information and another encoding the temporal and semantic-related information. Objectives are enforced to constrain these latent factors to achieve domain disentanglement and transfer.

Col1: Original sequences ("Human" $\mathcal{D}=\mathbf{P}_1$ and "Alien" $\mathcal{D}=\mathbf{P}_2$); Col2: Sequence reconstructions; Col3: Reconstructed sequences using $z_1^{\mathcal{D}},...,z_T^{\mathcal{D}}$; Col4: Domain transferred sequences with exchanged $z_d^{\mathcal{D}}$.

Visit our project page to explore more details. 🐾

• [2023.10] - We provide our extracted I3D features, kindly refer to this page for more details.
• [2023.09] - TranSVAE was accepted to NeurIPS 2023! 🎉
• [2022.08] - TranSVAE achieves 1st place among the UDA leaderboards of UCF-HMDB, Jester, and Epic-Kitchens, based on Paper-with-Code.
• [2022.08] - Try a Gradio demo for domain disentanglement in TranSVAE at Hugging Face Spaces! 🤗
• [2022.08] - Our paper is available on arXiv, click here to check it out!

• Highlights
• Installation
• Data Preparation
• Getting Started
• Main Results
• TODO List
• License
• Acknowledgement
• Citation

Conceptual Comparison
Graphical Model
Framework Overview

Please refer to INSTALL.md for the installation details.

Please refer to DATA_PREPARE.md for the details to prepare the ¹UCF₁₀₁, ²HMDB₅₁, ³Jester, ⁴Epic-Kitchens, and ⁵Sprites datasets.

Please refer to GET_STARTED.md to learn more usage about this codebase.

UCF₁₀₁ → HMDB₅₁

HMDB₅₁ → UCF₁₀₁

Domain Transfer Example

Source (Original)	Target (Original)		Source (Original)	Target (Original)
Reconstruct ($\mathbf{z}_d^{\mathcal{S}}$ + $\mathbf{z}_t^{\mathcal{S}}$)	Reconstruct ($\mathbf{z}_d^{\mathcal{T}}$ + $\mathbf{z}_t^{\mathcal{T}}$)		Reconstruct ($\mathbf{z}_d^{\mathcal{S}}$ + $\mathbf{z}_t^{\mathcal{S}}$)	Reconstruct ($\mathbf{z}_d^{\mathcal{T}}$ + $\mathbf{z}_t^{\mathcal{T}}$)
Reconstruct ($\mathbf{z}_d^{\mathcal{S}} + \mathbf{0}$)	Reconstruct ($\mathbf{z}_d^{\mathcal{T}} + \mathbf{0}$)		Reconstruct ($\mathbf{z}_d^{\mathcal{S}} + \mathbf{0}$)	Reconstruct ($\mathbf{z}_d^{\mathcal{T}} + \mathbf{0}$)
Reconstruct ($\mathbf{0} + \mathbf{z}_t^{\mathcal{S}}$)	Reconstruct ($\mathbf{0} + \mathbf{z}_t^{\mathcal{T}}$)		Reconstruct ($\mathbf{0} + \mathbf{z}_t^{\mathcal{S}}$)	Reconstruct ($\mathbf{0} + \mathbf{z}_t^{\mathcal{T}}$)
Reconstruct ($\mathbf{z}_d^{\mathcal{S}} + \mathbf{z}_t^{\mathcal{T}}$)	Reconstruct ($\mathbf{z}_d^{\mathcal{T}} + \mathbf{z}_t^{\mathcal{S}}$)		Reconstruct ($\mathbf{z}_d^{\mathcal{S}} + \mathbf{z}_t^{\mathcal{T}}$)	Reconstruct ($\mathbf{z}_d^{\mathcal{T}} + \mathbf{z}_t^{\mathcal{S}}$)

• Initial release. 🚀
• Add license. See here for more details.
• Add demo at Hugging Face Spaces.
• Add installation details.
• Add data preparation details.
• Add evaluation details.
• Add training details.

This work is under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

We acknowledge the use of the following public resources during the course of this work: ¹UCF₁₀₁, ²HMDB₅₁, ³Jester, ⁴Epic-Kitchens, ⁵Sprites, ⁶I3D, and ⁷TRN.

If you find this work helpful, please kindly consider citing our paper:

@inproceedings{wei2023transvae,
  title = {Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective},
  author = {Wei, Pengfei and Kong, Lingdong and Qu, Xinghua and Ren, Yi and Xu, Zhiqiang and Jiang, Jing and Yin, Xiang},
  booktitle = {Advances in Neural Information Processing Systems}, 
  year = {2023},
}