MPTC-FPN: A Multilayer Progressive FPN With Transformer-CNN Based Encoder for Salient Object Detection

Due to the development of Convolutional Neural Networks (CNN), significant progress has been made in Salient Object Detection (SOD). However, methods based on CNN are difficult to achieve good results in learning global context information. Recently, with the rapid development of vision transformer, it provides a new perspective for the performance improvement of salient object detection. Benefiting from the powerful capability of global modeling, transformer can supplement rich global contextual information. For lacking the ability to learn local details, it is suboptimal to only adopt transformer as encoder. Therefore, how to skillfully combine local details and global context information is crucial. We conbine CNN and transformer to propose a Multilayer Progressive FPN with Transformer-CNN Based Encoder For Salient Object Detection (MPTC-FPN). Similar to most of the previous methods, we adopt the FPN network as the basic structure. But the difference from previous methods is that we have six initial features before feature fusion, instead of the traditional four or five. We use a low-level feature generation module (LFGM) to generate a lower-level feature to supplement local details. In addition, we also propose a module to reduce the difference between features (DRM), making the features more conducive to fusion. On the basis of FPN, we add a large number of feature fusion nodes, which makes the process of feature fusion smoother. Moreover, we adjust the supervision strategy, use multiple supervision points, and adopt an appropriate weight distribution strategy among the multiple supervision points. A series of comprehensive experimental results demonstrates that our proposed method outperforms previous state-of-the-art methods on five datasets.