Summary of Vfimamba: Video Frame Interpolation with State Space Models, by Guozhen Zhang and Chunxu Liu and Yutao Cui and Xiaotong Zhao and Kai Ma and Limin Wang
VFIMamba: Video Frame Interpolation with State Space Models
by Guozhen Zhang, Chunxu Liu, Yutao Cui, Xiaotong Zhao, Kai Ma, Limin Wang
First submitted to arxiv on: 2 Jul 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 proposes VFIMamba, a novel video frame interpolation (VFI) method that leverages Selective State Space Models (S6) for efficient inter-frame modeling. The approach introduces the Mixed-SSM Block (MSB), which rearranges tokens from adjacent frames and applies multi-directional S6 modeling to transmit information across frames while maintaining linear complexity. A novel curriculum learning strategy is also introduced to cultivate proficiency in modeling inter-frame dynamics across varying motion magnitudes. Experimental results show that VFIMamba achieves state-of-the-art performance on diverse benchmarks, particularly excelling in high-resolution scenarios like the X-TEST dataset with improvements of 0.80 dB for 4K frames and 0.96 dB for 2K frames. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make videos look smoother by filling in missing frames. The current methods are not very good because they either can’t see far enough ahead or take too long to work. Researchers have developed a special kind of model called S6 that’s great for long sequences, but it needs some help to work well with video frames. The new method, called VFIMamba, uses this S6 model and rearranges the information from nearby frames in a special way to make it easier to understand. It also has a special learning strategy to get better at filling in these missing frames. The results are very promising, showing that this new method is much better than the current ones for high-resolution videos. |
Keywords
» Artificial intelligence » Curriculum learning
