interpretable Concept-Based Prototypical Networks For Few-Shot Learning

As a subjective concept, perceived image quality is heavily affected by visual mechanisms, e.g., selective attention and contrast sensitivity. This work proposes a lightweight attention module in image quality assessment (IQA) to simulate spatial attention and contrast sensitivity mechanisms. The attention module can extract essential information from a CNN backbone for image quality perception using two sequential attention blocks: a spatial block for mimicking selective attention in spatial domain and a channel block for contrast sensitivity. Experimental results on two large-scale publicly available IQA datasets have demonstrated promising performance of the proposed approach. The source code can be found at https://github.com/junyongyou/sca_iqa.