Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Short-Cut Connections for Object Counting   Object counting is an important task in computer vision due to its growing
demand in applications such as traffic monitoring or surveillance. In this
paper, we consider object counting as a learning problem of a joint feature
extraction and pixel-wise object density estimation with
Convolutional-Deconvolutional networks. We introduce a novel counting model,
named Gated U-Net (GU-Net). Specifically, we propose to enrich the U-Net
architecture with the concept of learnable short-cut connections. Standard
short-cut connections are connections between layers in deep neural networks
which skip at least one intermediate layer. Instead of simply setting short-cut
connections, we propose to learn these connections from data. Therefore, our
short-cuts can work as gating units, which optimize the flow of information
between convolutional and deconvolutional layers in the U-Net architecture. We
evaluate the introduced GU-Net architecture on three commonly used benchmark
data sets for object counting. GU-Nets consistently outperform the base U-Net
architecture, and achieve state-of-the-art performance.

13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci