Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Convolutional Neural Network with Binary Quantization Layer   In this paper we introduce a novel method for segmentation that can benefit
from general semantics of Convolutional Neural Network (CNN). Our segmentation
proposes visually and semantically coherent image segments. We use binary
encoding of CNN features to overcome the difficulty of the clustering on the
high-dimensional CNN feature space. These binary encoding can be embedded into
the CNN as an extra layer at the end of the network. This results in real-time
segmentation. To the best of our knowledge our method is the first attempt on
general semantic image segmentation using CNN. All the previous papers were
limited to few number of category of the images (e.g. PASCAL VOC). Experiments
show that our segmentation algorithm outperform the state-of-the-art
non-semantic segmentation methods by a large margin.

30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci