Abstract
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.
Abstract (translated)
图像识别和质量评估是两种重要的观看任务,尽管可能遵循不同的视觉机制。本文研究的是,这两个任务是否可以在多任务学习的方式进行。提出了一个Sequential spatial-channel attention module,以模拟对于内容识别和质量评估至关重要的视觉注意力和对比度敏感度机制。空间注意力在内容识别和质量评估之间共享,而Channel attention只为质量评估。这种注意力模块被集成到Transformer中,以构建两个观看任务的通用模型。实验结果显示,提出的通用模型可以在质量评估和内容识别任务中表现出良好的性能。
URL
https://arxiv.org/abs/2301.09190
