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Apples and Oranges? Assessing Image Quality over Content Recognition

arXiv 2301.09190 ↗First posted 22 Jan 2023cs.CV eess.IVcited by 0

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Abstract (arXiv)

Image recognition and quality assessment are two important viewing tasks, while potentially following different visual mechanisms. This paper investigates if the two tasks can be performed in a multitask learning manner. A sequential spatial-channel attention module is proposed to simulate the visual attention and contrast sensitivity mechanisms that are crucial for content recognition and quality assessment. Spatial attention is shared between content recognition and quality assessment, while channel attention is solely for quality assessment. Such attention module is integrated into Transformer to build a uniform model for the two viewing tasks. The experimental results have demonstrated that the proposed uniform model can achieve promising performance for both quality assessment and content recognition tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 22 Jan 2023.

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18 of 37 references.

References · 18

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6 more
  1. 2017Residual Attention Network for Image Classificationcited by 385
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  5. 2014The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classificationcited by 67
  6. 2016On the Use of Deep Learning for Blind Image Quality Assessmentcited by 34

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