Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contrastive Representation Learning for Hand Shape Estimation   This work presents improvements in monocular hand shape estimation by
building on top of recent advances in unsupervised learning. We extend momentum
contrastive learning and contribute a structured collection of hand images,
well suited for visual representation learning, which we call HanCo. We find
that the representation learned by established contrastive learning methods can
be improved significantly by exploiting advanced background removal techniques
and multi-view information. These allow us to generate more diverse instance
pairs than those obtained by augmentations commonly used in exemplar based
approaches. Our method leads to a more suitable representation for the hand
shape estimation task and shows a 4.7% reduction in mesh error and a 3.6%
improvement in F-score compared to an ImageNet pretrained baseline. We make our
benchmark dataset publicly available, to encourage further research into this
direction.

19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph