Visual saliency models have enjoyed a big leap in performance in recent years, thanks to advances in deep learning and large scale annotated data. Despite enormous effort and huge breakthroughs, however, models still fall short in reaching human-level accuracy. In this work, I explore the landscape of the field emphasizing on new deep saliency models, benchmarks, and datasets. A large number of image and video saliency models are reviewed and compared over two image benchmarks and two large scale video datasets. Further, I identify factors that contribute to the gap between models and humans and discuss the remaining issues that need to be addressed to build the next generation of more powerful saliency models. Some specific questions that are addressed include: in what ways current models fail, how to remedy them, what can be learned from cognitive studies of attention, how explicit saliency judgments relate to fixations, how to conduct fair model comparison, and what are the emerging applications of saliency models. Index Terms-Visual saliency, eye movement prediction, attention, video saliency, benchmark, deep learning. ! 1. It is worth noting that bottom-up saliency models have gradually shifted from locating low-level conspicuous image regions to predicting eye movements. Please refer to [9] for a discussion on this.
Please refer to REF for a comprehensive survey on deep learning based saliency models.
Saliency Prediction in the Deep Learning Era: An Empirical Investigation
ArXiv
ArXiv
Computer Science