Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attribute Prototype Network for Zero-Shot Learning   From the beginning of zero-shot learning research, visual attributes have
been shown to play an important role. In order to better transfer
attribute-based knowledge from known to unknown classes, we argue that an image
representation with integrated attribute localization ability would be
beneficial for zero-shot learning. To this end, we propose a novel zero-shot
representation learning framework that jointly learns discriminative global and
local features using only class-level attributes. While a visual-semantic
embedding layer learns global features, local features are learned through an
attribute prototype network that simultaneously regresses and decorrelates
attributes from intermediate features. We show that our locality augmented
image representations achieve a new state-of-the-art on three zero-shot
learning benchmarks. As an additional benefit, our model points to the visual
evidence of the attributes in an image, e.g. for the CUB dataset, confirming
the improved attribute localization ability of our image representation.

2
The categories of this paper are: math
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th