Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MuCAN: Multi-Correspondence Aggregation Network for Video
  Super-Resolution   Video super-resolution (VSR) aims to utilize multiple low-resolution frames
to generate a high-resolution prediction for each frame. In this process,
inter- and intra-frames are the key sources for exploiting temporal and spatial
information. However, there are a couple of limitations for existing VSR
methods. First, optical flow is often used to establish temporal
correspondence. But flow estimation itself is error-prone and affects recovery
results. Second, similar patterns existing in natural images are rarely
exploited for the VSR task. Motivated by these findings, we propose a temporal
multi-correspondence aggregation strategy to leverage similar patches across
frames, and a cross-scale nonlocal-correspondence aggregation scheme to explore
self-similarity of images across scales. Based on these two new modules, we
build an effective multi-correspondence aggregation network (MuCAN) for VSR.
Our method achieves state-of-the-art results on multiple benchmark datasets.
Extensive experiments justify the effectiveness of our method.

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The categories of this paper are: astro-ph
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
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The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol