Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Alleviating Human-level Shift : A Robust Domain Adaptation Method for
  Multi-person Pose Estimation   Human pose estimation has been widely studied with much focus on supervised
learning requiring sufficient annotations. However, in real applications, a
pretrained pose estimation model usually need be adapted to a novel domain with
no labels or sparse labels. Such domain adaptation for 2D pose estimation
hasn't been explored. The main reason is that a pose, by nature, has typical
topological structure and needs fine-grained features in local keypoints. While
existing adaptation methods do not consider topological structure of
object-of-interest and they align the whole images coarsely. Therefore, we
propose a novel domain adaptation method for multi-person pose estimation to
conduct the human-level topological structure alignment and fine-grained
feature alignment. Our method consists of three modules: Cross-Attentive
Feature Alignment (CAFA), Intra-domain Structure Adaptation (ISA) and
Inter-domain Human-Topology Alignment (IHTA) module. The CAFA adopts a
bidirectional spatial attention module (BSAM)that focuses on fine-grained local
feature correlation between two humans to adaptively aggregate consistent
features for adaptation. We adopt ISA only in semi-supervised domain adaptation
(SSDA) to exploit the corresponding keypoint semantic relationship for reducing
the intra-domain bias. Most importantly, we propose an IHTA to learn more
domain-invariant human topological representation for reducing the inter-domain
discrepancy. We model the human topological structure via the graph convolution
network (GCN), by passing messages on which, high-order relations can be
considered. This structure preserving alignment based on GCN is beneficial to
the occluded or extreme pose inference. Extensive experiments are conducted on
two popular benchmarks and results demonstrate the competency of our method
compared with existing supervised approaches.

13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin