Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object
  Detection   A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is
proposed for fast multi-scale object detection. The MS-CNN consists of a
proposal sub-network and a detection sub-network. In the proposal sub-network,
detection is performed at multiple output layers, so that receptive fields
match objects of different scales. These complementary scale-specific detectors
are combined to produce a strong multi-scale object detector. The unified
network is learned end-to-end, by optimizing a multi-task loss. Feature
upsampling by deconvolution is also explored, as an alternative to input
upsampling, to reduce the memory and computation costs. State-of-the-art object
detection performance, at up to 15 fps, is reported on datasets, such as KITTI
and Caltech, containing a substantial number of small objects.

37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn