  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.
