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.

图像中的小，中和大规模显着性模式对于显着性检测很有价值。通过观察，在数据集中出现大小显着性模式的概率低于中等规模显着性模式的概率。结果，在这样的数据集上训练的深度显着性模型将收敛到中等规模的显着性模式，并且很难很好地推断小规模和显着性的显着性模式，因为它们在模型中由于其低概率而没有有效编码。因此，本文提出了一种使用跨尺度深度推断的新颖而简单的显着性检测方法。此外，提出了一种新的网络架构，其中多层机制利用注意力机制，以改善不同比例尺图像中不同比例尺显着性图案的接收场。提出的跨尺度深度推理可以有效地提高在多尺度图像中编码的小尺度和显着显着性模式的表示能力。定量和定性评估表明，我们的深入模型在多种指标上均取得了可喜的结果。