Saliency detection via cross-scale deep inference.

The small, moderate, and large scale saliency patterns in images are valuable to be extracted in saliency detection. By the observation that the probability of small and large saliency patterns appearing in datasets is lower than that of moderate scale saliency patterns. As results, a deep saliency model trained on such datasets would converge to moderate scale saliency patterns, and it is hard to well infer the small and large scale saliency patterns because they are not encoded efficiently in the model for their low probability. Thus a novel but simple saliency detection method using cross-scale deep inference is presented in this paper. Moreover, a new network architecture, in which the attention mechanism is exploited by multiple layers, is proposed to improve the receptive fields of various scale saliency patterns in different scale images. The presented cross-scale deep inference could improve the representation power of small and large scale saliency patterns encoded in multiple scale images efficiently. The quantitative and qualitative evaluation demonstrates our deep model achieves a promising results across a wide of metrics.