Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs
  for Contour Prediction   Recent works have shown that exploiting multi-scale representations deeply
learned via convolutional neural networks (CNN) is of tremendous importance for
accurate contour detection. This paper presents a novel approach for predicting
contours which advances the state of the art in two fundamental aspects, i.e.
multi-scale feature generation and fusion. Different from previous works
directly consider- ing multi-scale feature maps obtained from the inner layers
of a primary CNN architecture, we introduce a hierarchical deep model which
produces more rich and complementary representations. Furthermore, to refine
and robustly fuse the representations learned at different scales, the novel
Attention-Gated Conditional Random Fields (AG-CRFs) are proposed. The
experiments ran on two publicly available datasets (BSDS500 and NYUDv2)
demonstrate the effectiveness of the latent AG-CRF model and of the overall
hierarchical framework.

10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin