Albeit current salient object detection (SOD) works have achieved fantastic progress, they are cast into the shade when it comes to the integrity of the predicted salient regions. We define the concept of integrity at both the micro and macro level. Specifically, at the micro level, the model should highlight all parts that belong to a certain salient object, while at the macro level, the model needs to discover all salient objects from the given image scene. To facilitate integrity learning for salient object detection, we design a novel Integrity Cognition Network (ICON), which explores three important components to learn strong integrity features. 1) Unlike the existing models that focus more on feature discriminability, we introduce a diverse feature aggregation (DFA) component to aggregate features with various receptive fields (i.e.,, kernel shape and context) and increase the feature diversity. Such diversity is the foundation for mining the integral salient objects. 2) Based on the DFA features, we introduce the integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects at the macro level, while suppressing the other distracting ones. 3) After extracting the enhanced features, the part-whole verification (PWV) method is employed to determine whether the part and whole object features have strong agreement. Such part-whole agreements can further improve the micro-level integrity for each salient object. To demonstrate the effectiveness of ICON, comprehensive experiments are conducted on seven challenging benchmarks, where promising results are achieved.

翻译：尽管目前显著的物体探测(SOD)工程取得了惊人的进展,但在预测突出区域的完整性方面,这些工程却被抛入阴影之中。 我们定义了微观和宏观层面的完整性概念。具体地说,在微观层面,模型应突出属于某一突出对象的所有部分,而在宏观层面,模型需要从特定图像场景中发现所有突出对象。 2 为促进发现突出对象的廉正学习,我们设计了一个新的完整性认知网络(ICON),它探索了三个重要组成部分,以学习强有力的完整性特征。 1 与更加侧重于特征差异化的现有模型不同,我们从微观和宏观层面界定了完整性概念的概念概念。 我们引入了多种特性集合(DFA)部分,将不同特性组合组合组合起来,以综合各种可接受领域(即内核形状和背景),并增加特征多样性。这种多样性是开采整体突出对象的基础。 2 根据DFA的特征,我们引入了完整性频道增强部分,目的是加强特征频道,突出宏观层面的整体目标,同时压制目标性差异性特征水平,同时抑制其他目标性汇总性汇总性特征水平,每个测试是强化性协议的一部分。