Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: No-Reference Image Quality Assessment via Transformers, Relative
  Ranking, and Self-Consistency   The goal of No-Reference Image Quality Assessment (NR-IQA) is to estimate the
perceptual image quality in accordance with subjective evaluations, it is a
complex and unsolved problem due to the absence of the pristine reference
image. In this paper, we propose a novel model to address the NR-IQA task by
leveraging a hybrid approach that benefits from Convolutional Neural Networks
(CNNs) and self-attention mechanism in Transformers to extract both local and
non-local features from the input image. We capture local structure information
of the image via CNNs, then to circumvent the locality bias among the extracted
CNNs features and obtain a non-local representation of the image, we utilize
Transformers on the extracted features where we model them as a sequential
input to the Transformer model. Furthermore, to improve the monotonicity
correlation between the subjective and objective scores, we utilize the
relative distance information among the images within each batch and enforce
the relative ranking among them. Last but not least, we observe that the
performance of NR-IQA models degrades when we apply equivariant transformations
(e.g. horizontal flipping) to the inputs. Therefore, we propose a method that
leverages self-consistency as a source of self-supervision to improve the
robustness of NRIQA models. Specifically, we enforce self-consistency between
the outputs of our quality assessment model for each image and its
transformation (horizontally flipped) to utilize the rich self-supervisory
information and reduce the uncertainty of the model. To demonstrate the
effectiveness of our work, we evaluate it on seven standard IQA datasets (both
synthetic and authentic) and show that our model achieves state-of-the-art
results on various datasets.

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The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn