MIT Open Access Articles Attention as a Bayesian inference process

David Marr famously defined vision as ”knowing what is where by seeing”. In the framework described here, attention is the inference process that solves the visual recognition problem of what is where. The theory proposes a computational role for attention and leads to a model that performs well in recognition tasks and that predicts some of the main properties of attention at the level of psychophysics and physiology. We propose an algorithmic implementation a Bayesian network that can be mapped into the basic functional anatomy of attention involving the ventral stream and the dorsal stream. This description integrates bottom-up, feature-based as well as spatial (context based) attentional mechanisms. We show that the Bayesian model predicts well human eye fixations (considered as a proxy for shifts of attention) in natural scenes, and can improve accuracy in object recognition tasks involving cluttered real world images. In both cases, we found that the proposed model can predict human performance better than existing bottom-up and top-down computational models.

大卫·马尔（David Marr）有一个著名的定义，将视觉描述为“通过看见而知道什么东西在哪里”。在此处描述的框架中，注意力是解决“什么东西在哪里”这一视觉识别问题的推理过程。该理论提出了注意力的一种计算作用，并引出了一个在识别任务中表现良好的模型，该模型在心理物理学和生理学层面预测了注意力的一些主要特性。我们提出了一种贝叶斯网络的算法实现，它可以映射到涉及腹侧通路和背侧通路的注意力的基本功能解剖结构中。这种描述整合了自下而上、基于特征以及空间（基于语境）的注意力机制。我们表明，贝叶斯模型能很好地预测自然场景中人类的眼睛注视点（被视为注意力转移的一种表征），并且能够提高涉及杂乱的现实世界图像的物体识别任务的准确性。在这两种情况下，我们发现所提出的模型比现有的自下而上和自上而下的计算模型能更好地预测人类的表现。