Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast End-to-End Trainable Guided Filter   Dense pixel-wise image prediction has been advanced by harnessing the
capabilities of Fully Convolutional Networks (FCNs). One central issue of FCNs
is the limited capacity to handle joint upsampling. To address the problem, we
present a novel building block for FCNs, namely guided filtering layer, which
is designed for efficiently generating a high-resolution output given the
corresponding low-resolution one and a high-resolution guidance map. Such a
layer contains learnable parameters, which can be integrated with FCNs and
jointly optimized through end-to-end training. To further take advantage of
end-to-end training, we plug in a trainable transformation function for
generating the task-specific guidance map. Based on the proposed layer, we
present a general framework for pixel-wise image prediction, named deep guided
filtering network (DGF). The proposed network is evaluated on five image
processing tasks. Experiments on MIT-Adobe FiveK Dataset demonstrate that DGF
runs 10-100 times faster and achieves the state-of-the-art performance. We also
show that DGF helps to improve the performance of multiple computer vision
tasks.

18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom