Department of Computer Science, University of Southern California, 3641 Watt Way, Los Angeles, CA 90089, USA.
IEEE Trans Pattern Anal Mach Intell. 2013 Jan;35(1):185-207. doi: 10.1109/TPAMI.2012.89.
Modeling visual attention--particularly stimulus-driven, saliency-based attention--has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.
建模视觉注意——特别是受刺激驱动、基于显著度的注意——在过去 25 年中一直是一个非常活跃的研究领域。现在有许多不同的注意模型，除了对其他领域做出理论贡献外，它们还在计算机视觉、移动机器人和认知系统中展示了成功的应用。在这里，我们从计算的角度回顾了这些模型中实现的基本注意概念。我们提出了一个近 65 个模型的分类法，对方法、能力和缺点进行了批判性比较。特别是，从行为和计算研究中得出了 13 个标准，用于对注意模型进行定性比较。此外，我们还解决了模型的几个具有挑战性的问题，包括计算的生物合理性、与眼动数据集的相关性、自下而上和自上而下的分离，以及构建有意义的性能指标。最后，我们强调了当前注意建模的研究趋势，并为未来提供了一些见解。
