Recent saliency models extensively explore to incorporate multi-scale contextual information from Convolutional Neural Networks (CNNs). Besides direct fusion strategies, many approaches introduce message-passing to enhance CNN features or predictions. However, the messages are mainly transmitted in two ways, by feature-to-feature passing, and by prediction-to-prediction passing. In this paper, we add message-passing between features and predictions and propose a deep unified CRF saliency model . We design a novel cascade CRFs architecture with CNN to jointly refine deep features and predictions at each scale and progressively compute a final refined saliency map. We formulate the CRF graphical model that involves message-passing of feature-feature, feature-prediction, and prediction-prediction, from the coarse scale to the finer scale, to update the features and the corresponding predictions. Also, we formulate the mean-field updates for joint end-to-end model training with CNN through back propagation. The proposed deep unified CRF saliency model is evaluated over six datasets and shows highly competitive performance among the state of the arts.
翻译:最近的一些显著模型广泛探索了将革命神经网络(CNNs)的多尺度背景信息纳入其中。除了直接融合战略外,许多方法还采用传递信息的方法,以加强CNN的特征或预测。然而,信息主要通过两种方式传递:一是地对地传递,二是预测传递;在本文件中,我们添加了特征和预测之间的信息传递,并提出一个深度统一的通用报告格式模型;我们与CNN设计了一个新型的级联通用报告格式结构,以共同完善每个尺度的深度特征和预测,并逐步绘制最后精细化的显著地图;我们开发了通用报告格式图形模型,其中涉及从粗糙的尺度到细微的尺度,对特征、特征和预测预测进行信息传递,以更新特征和相应的预测;此外,我们还为通过后传方式与CNN进行联合端对端模式培训制定了中值更新。拟议的深度统一通用报告格式模型经过六套数据集的评估,显示艺术状态的高度竞争性性能。