Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiview Supervision By Registration   This paper presents a semi-supervised learning framework to train a keypoint
detector using multiview image streams given the limited labeled data
(typically $<$4\%). We leverage the complementary relationship between
multiview geometry and visual tracking to provide three types of supervisionary
signals to utilize the unlabeled data: (1) keypoint detection in one view can
be supervised by other views via the epipolar geometry; (2) a keypoint moves
smoothly over time where its optical flow can be used to temporally supervise
consecutive image frames to each other; (3) visible keypoint in one view is
likely to be visible in the adjacent view. We integrate these three signals in
a differentiable fashion to design a new end-to-end neural network composed of
three pathways. This design allows us to extensively use the unlabeled data to
train the keypoint detector. We show that our approach outperforms existing
detectors including DeepLabCut tailored to the keypoint detection of non-human
species such as monkeys, dogs, and mice.

29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math