Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Label Image Recognition with Graph Convolutional Networks   The task of multi-label image recognition is to predict a set of object
labels that present in an image. As objects normally co-occur in an image, it
is desirable to model the label dependencies to improve the recognition
performance. To capture and explore such important dependencies, we propose a
multi-label classification model based on Graph Convolutional Network (GCN).
The model builds a directed graph over the object labels, where each node
(label) is represented by word embeddings of a label, and GCN is learned to map
this label graph into a set of inter-dependent object classifiers. These
classifiers are applied to the image descriptors extracted by another sub-net,
enabling the whole network to be end-to-end trainable. Furthermore, we propose
a novel re-weighted scheme to create an effective label correlation matrix to
guide information propagation among the nodes in GCN. Experiments on two
multi-label image recognition datasets show that our approach obviously
outperforms other existing state-of-the-art methods. In addition, visualization
analyses reveal that the classifiers learned by our model maintain meaningful
semantic topology.

29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin