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Eventful Transformers: Leveraging Temporal Redundancy in Vision Transformers
The paper introduces Eventful Transformers, a method for reducing the computational costs of Vision Transformers in video recognition tasks by identifying and re-processing only significant changes between subsequent frames. The approach achieves significant computational savings with minimal accuracy reduction.
https://arxiv.org/abs//2308.13494
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
The paper introduces Eventful Transformers, a method for reducing the computational costs of Vision Transformers in video recognition tasks by identifying and re-processing only significant changes between subsequent frames. The approach achieves significant computational savings with minimal accuracy reduction.
https://arxiv.org/abs//2308.13494
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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