论文标题
更深入地查看显着对象检测：带有小型培训数据集的双流网络
A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset
论文作者
论文摘要
与传统的手工制作的方法相比，基于深度学习的方法通过在大规模训练集中精心制作的精美的精美网络来取得了巨大的性能改进。但是，我们是否真的需要大规模的训练集来进行显着对象检测（SOD）？在本文中，我们可以更深入地了解SOD表演与训练集之间的相互关系。为了减轻对大型培训数据的常规需求，我们提供了一种可行的方式来构建新型的小型训练集，该培训集仅包含4K图像。此外，我们提出了一个新型的双流网络，以充分利用我们提出的小型训练集，该集合由两个具有不同结构的特征骨架组成，通过拟议的栅极控制单元实现了互补的语义显着融合。据我们所知，这是首次使用小规模训练集以优于在大规模训练集中训练的最先进模型；然而，我们的方法仍然可以在五个基准数据集上实现领先的最先进性能。
Compared with the conventional hand-crafted approaches, the deep learning based methods have achieved tremendous performance improvements by training exquisitely crafted fancy networks over large-scale training sets. However, do we really need large-scale training set for salient object detection (SOD)? In this paper, we provide a deeper insight into the interrelationship between the SOD performances and the training sets. To alleviate the conventional demands for large-scale training data, we provide a feasible way to construct a novel small-scale training set, which only contains 4K images. Moreover, we propose a novel bi-stream network to take full advantage of our proposed small training set, which is consisted of two feature backbones with different structures, achieving complementary semantical saliency fusion via the proposed gate control unit. To our best knowledge, this is the first attempt to use a small-scale training set to outperform state-of-the-art models which are trained on large-scale training sets; nevertheless, our method can still achieve the leading state-of-the-art performance on five benchmark datasets.
