Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhancing Self-supervised Video Representation Learning via Multi-level
  Feature Optimization   The crux of self-supervised video representation learning is to build general
features from unlabeled videos. However, most recent works have mainly focused
on high-level semantics and neglected lower-level representations and their
temporal relationship which are crucial for general video understanding. To
address these challenges, this paper proposes a multi-level feature
optimization framework to improve the generalization and temporal modeling
ability of learned video representations. Concretely, high-level features
obtained from naive and prototypical contrastive learning are utilized to build
distribution graphs, guiding the process of low-level and mid-level feature
learning. We also devise a simple temporal modeling module from multi-level
features to enhance motion pattern learning. Experiments demonstrate that
multi-level feature optimization with the graph constraint and temporal
modeling can greatly improve the representation ability in video understanding.
Code is available at
https://github.com/shvdiwnkozbw/Video-Representation-via-Multi-level-Optimization.

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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph