Recent deep learning based salient object detection methods with attention mechanisms have made great success. However, existing attention mechanisms can be generally separated into two categories. One part chooses to calculate weights indiscriminately, which yields computational redundancy. While one part focuses randomly on a small part of the images, such as hard attention, resulting in incorrectness owing to insufficiently targeted selection of a subset of tokens. To alleviate these problems, we design a Curiosity-driven Network (CNet) and a Curiosity-driven Learning Algorithm (CLA) based on fragment attention (FA) mechanism newly defined in this paper. FA imitates the process of cognition perception driven by human curiosity, and divides the degree of curiosity into three levels, i.e. curious, a little curious and not curious. These three levels correspond to five saliency degrees, including salient and non-salient, likewise salient and likewise non-salient, completely uncertain. With more knowledge gained by the network, CLA transforms the curiosity degree of each pixel to yield enhanced detail-enriched saliency maps. In order to extract more context-aware information of potential salient objects and make a better foundation for CLA, a high-level feature extraction module (HFEM) is further proposed. Based on the much better high-level features extracted by HFEM, FA can classify the curiosity degree for each pixel more reasonably and accurately. Extensive experiments on five popular datasets clearly demonstrate that our method outperforms the state-of-the-art approaches without any pre-processing operations or post-processing operations.

中文翻译：

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具有片段注意力的好奇心驱动的显着目标检测


最近基于深度学习的具有注意机制的显着目标检测方法取得了巨大成功。然而，现有的注意力机制通常可以分为两类。一部分选择不加区别地计算权重，这会产生计算冗余。而一部分随机关注图像的一小部分，例如硬注意力，由于对标记子集的选择不够有针对性，从而导致不正确。为了缓解这些问题，我们基于本文新定义的片段注意（FA）机制设计了好奇心驱动网络（CNet）和好奇心驱动学习算法（CLA）。 FA模仿人类好奇心驱动的认知感知过程，将好奇心的程度分为三个层次，即好奇、有点好奇和不好奇。这三个层次分别对应五个显着程度，包括显着和不显着、同样显着和同样不显着、完全不确定。随着网络获得更多知识，CLA 可以改变每个像素的好奇程度，以生成增强的细节丰富的显着图。为了提取更多潜在显着对象的上下文感知信息并为CLA奠定更好的基础，进一步提出了高级特征提取模块（HFEM）。基于HFEM提取的更好的高级特征，FA可以更合理、更准确地对每个像素的好奇程度进行分类。对五个流行数据集的大量实验清楚地表明，我们的方法优于最先进的方法，无需任何预处理操作或后处理操作。