Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-supervised Video Transformer   In this paper, we propose self-supervised training for video transformers
using unlabeled video data. From a given video, we create local and global
spatiotemporal views with varying spatial sizes and frame rates. Our
self-supervised objective seeks to match the features of these different views
representing the same video, to be invariant to spatiotemporal variations in
actions. To the best of our knowledge, the proposed approach is the first to
alleviate the dependency on negative samples or dedicated memory banks in
Self-supervised Video Transformer (SVT). Further, owing to the flexibility of
Transformer models, SVT supports slow-fast video processing within a single
architecture using dynamically adjusted positional encoding and supports
long-term relationship modeling along spatiotemporal dimensions. Our approach
performs well on four action recognition benchmarks (Kinetics-400, UCF-101,
HMDB-51, and SSv2) and converges faster with small batch sizes. Code:
https://git.io/J1juJ

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The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
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The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph