Benefiting from the powerful discriminative feature learning capability of convolutional neural networks (CNNs), deep learning techniques have achieved remarkable performance improvement for the task of salient object detection (SOD) in recent years. However, most existing deep SOD models do not fully exploit informative contextual features, which often leads to suboptimal detection performance in the presence of a cluttered background. This paper presents a context-aware attention module that detects salient objects by simultaneously constructing connections between each image pixel and its local and global contextual pixels. Specifically, each pixel and its neighbors bidirectionally exchange semantic information by computing their correlation coefficients, and this process aggregates contextual attention features both locally and globally. In addition, an attention-guided hierarchical network architecture is designed to capture fine-grained spatial details by transmitting contextual information from deeper to shallower network layers in a top-down manner. Extensive experiments on six public SOD datasets show that our proposed model demonstrates superior SOD performance against most of the current state-of-the-art models under different evaluation metrics.

中文翻译：

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通过融合局部和全局上下文的显着对象检测

受益于卷积神经网络（CNN）强大的判别特征学习能力，深度学习技术近年来在显着目标检测（SOD）任务方面取得了显着的性能提升。但是，大多数现有的深层SOD模型并未充分利用信息性上下文特征，这在背景混乱的情况下常常导致检测性能欠佳。本文提出了一种上下文感知注意模块，该模块通过同时构造每个图像像素与其局部和全局上下文像素之间的连接来检测显着对象。具体来说，每个像素及其邻居都通过计算它们的相关系数来双向交换语义信息，并且此过程会聚合本地和全局的上下文关注特征。另外，设计了一种注意力导向的分层网络体系结构，以自上而下的方式通过从较深的网络层到较浅的网络层传输上下文信息来捕获细粒度的空间细节。在六个公共SOD数据集上进行的广泛实验表明，我们提出的模型在不同的评估指标下，相对于大多数当前最新模型而言，具有卓越的SOD性能。