Summary of Ssm Meets Video Diffusion Models: Efficient Long-term Video Generation with Structured State Spaces, by Yuta Oshima et al.
SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces
by Yuta Oshima, Shohei Taniguchi, Masahiro Suzuki, Yutaka Matsuo
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on improving video generation capabilities using diffusion models. Recent attempts have employed attention layers to capture temporal features, but these layers are computationally expensive and scale poorly with sequence length. To address this limitation, the authors propose leveraging state-space models (SSMs) as an alternative feature extractor. SSMs, such as Mamba, offer linear-time memory consumption relative to sequence length, making them more suitable for generating longer video sequences. The authors found that bidirectional SSMs are beneficial for capturing temporal features in video data, rather than relying on traditional unidirectional SSMs. Comprehensive evaluations were conducted on multiple long-term video datasets, including MineRL Navigate, across various model sizes. Results show that SSM-based models require less memory to achieve the same FVD as attention-based models and often deliver better performance with comparable GPU memory usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at generating videos using a special kind of AI called diffusion models. Right now, these models are great at making pictures, but they struggle when it comes to videos because they get stuck on the order in which things happen over time. The researchers found a way to fix this problem by using a different type of model that’s better at understanding sequences of events. They tested their new method on lots of different video datasets and found that it worked really well, using less computer memory than the old method and producing more realistic videos. |
Keywords
» Artificial intelligence » Attention » Diffusion
