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MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition. (arXiv:2201.08383v1 [cs.CV])
Jan. 21, 2022, 2:10 a.m. | Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, Christoph Feichtenhofer
cs.CV updates on arXiv.org arxiv.org
While today's video recognition systems parse snapshots or short clips
accurately, they cannot connect the dots and reason across a longer range of
time yet. Most existing video architectures can only process <5 seconds of a
video without hitting the computation or memory bottlenecks.
In this paper, we propose a new strategy to overcome this challenge. Instead
of trying to process more frames at once like most existing methods, we propose
to process videos in an online fashion and cache …
More from arxiv.org / cs.CV updates on arXiv.org
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