Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Part Discovery from Contrastive Reconstruction   The goal of self-supervised visual representation learning is to learn
strong, transferable image representations, with the majority of research
focusing on object or scene level. On the other hand, representation learning
at part level has received significantly less attention. In this paper, we
propose an unsupervised approach to object part discovery and segmentation and
make three contributions. First, we construct a proxy task through a set of
objectives that encourages the model to learn a meaningful decomposition of the
image into its parts. Secondly, prior work argues for reconstructing or
clustering pre-computed features as a proxy to parts; we show empirically that
this alone is unlikely to find meaningful parts; mainly because of their low
resolution and the tendency of classification networks to spatially smear out
information. We suggest that image reconstruction at the level of pixels can
alleviate this problem, acting as a complementary cue. Lastly, we show that the
standard evaluation based on keypoint regression does not correlate well with
segmentation quality and thus introduce different metrics, NMI and ARI, that
better characterize the decomposition of objects into parts. Our method yields
semantic parts which are consistent across fine-grained but visually distinct
categories, outperforming the state of the art on three benchmark datasets.
Code is available at the project page:
https://www.robots.ox.ac.uk/~vgg/research/unsup-parts/.

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The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
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The categories of this paper are: econ
34
The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: hep-lat