Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distraction-Aware Feature Learning for Human Attribute Recognition via
  Coarse-to-Fine Attention Mechanism   Recently, Human Attribute Recognition (HAR) has become a hot topic due to its
scientific challenges and application potentials, where localizing attributes
is a crucial stage but not well handled. In this paper, we propose a novel deep
learning approach to HAR, namely Distraction-aware HAR (Da-HAR). It enhances
deep CNN feature learning by improving attribute localization through a
coarse-to-fine attention mechanism. At the coarse step, a self-mask block is
built to roughly discriminate and reduce distractions, while at the fine step,
a masked attention branch is applied to further eliminate irrelevant regions.
Thanks to this mechanism, feature learning is more accurate, especially when
heavy occlusions and complex backgrounds exist. Extensive experiments are
conducted on the WIDER-Attribute and RAP databases, and state-of-the-art
results are achieved, demonstrating the effectiveness of the proposed approach.

9
The categories of this paper are: nlin
2
The categories of this paper are: math
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc