Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-image Super-resolution via Quality Map Associated Temporal
  Attention Network   With the rising interest in deep learning-based methods in remote sensing,
neural networks have made remarkable advancements in multi-image fusion and
super-resolution. To fully exploit the advantages of multi-image
super-resolution, temporal attention is crucial as it allows a model to focus
on reliable features rather than noises. Despite the presence of quality maps
(QMs) that indicate noises in images, most of the methods tested in the PROBA-V
dataset have not been used QMs for temporal attention. We present a quality map
associated temporal attention network (QA-Net), a novel method that
incorporates QMs into both feature representation and fusion processes for the
first time. Low-resolution features are temporally attended by QM features in
repeated multi-head attention modules. The proposed method achieved
state-of-the-art results in the PROBA-V dataset.

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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: hep-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
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The categories of this paper are: ao-sci
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The categories of this paper are: math
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The categories of this paper are: solv-int