Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Keypoint Communities   We present a fast bottom-up method that jointly detects over 100 keypoints on
humans or objects, also referred to as human/object pose estimation. We model
all keypoints belonging to a human or an object -- the pose -- as a graph and
leverage insights from community detection to quantify the independence of
keypoints. We use a graph centrality measure to assign training weights to
different parts of a pose. Our proposed measure quantifies how tightly a
keypoint is connected to its neighborhood. Our experiments show that our method
outperforms all previous methods for human pose estimation with fine-grained
keypoint annotations on the face, the hands and the feet with a total of 133
keypoints. We also show that our method generalizes to car poses.

15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat