Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rethinking Self-supervised Correspondence Learning: A Video Frame-level
  Similarity Perspective   Learning a good representation for space-time correspondence is the key for
various computer vision tasks, including tracking object bounding boxes and
performing video object pixel segmentation. To learn generalizable
representation for correspondence in large-scale, a variety of self-supervised
pretext tasks are proposed to explicitly perform object-level or patch-level
similarity learning. Instead of following the previous literature, we propose
to learn correspondence using Video Frame-level Similarity (VFS) learning, i.e,
simply learning from comparing video frames. Our work is inspired by the recent
success in image-level contrastive learning and similarity learning for visual
recognition. Our hypothesis is that if the representation is good for
recognition, it requires the convolutional features to find correspondence
between similar objects or parts. Our experiments show surprising results that
VFS surpasses state-of-the-art self-supervised approaches for both OTB visual
object tracking and DAVIS video object segmentation. We perform detailed
analysis on what matters in VFS and reveals new properties on image and frame
level similarity learning. Project page with code is available at
https://jerryxu.net/VFS

34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
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The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: stat
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat