Salient object detection based on backbone enhanced network

摘要

The Convolutional Neural Networks (CNNs) with encoder-decoder architecture has shown powerful ability in semantic segmentation and it has also been applied in saliency detection. In most researches, the parameters of the backbone network which have been pre-trained on the ImageNet dataset will be retrained using the new training dataset to let CNNs adapt to the new task better. But the retraining will weaken generalization of the pre-trained backbone network and result in over-fitting, especially when the scale of the new training data is not very large. To make a balance between generalization and precision, and to further improve the performance of the CNNs with encoder-decoder architecture in salient object detection, we proposed a framework with enhanced backbone network (BENet). A encoder with structure of dual backbone networks (DBNs) is adopted in BENet to extract more diverse feature maps. In addition, BENet includes a connection module based on improved Res2Net to efficiently fuse feature maps from the two backbone networks and a decoder based on weighted multi-scale feedback module (WMFM) to perform synchronous learning. Our approach is extensively evaluated on six public datasets, and experimental results show significant and consistent improvements over the state-of-the-art methods without any additional supervision.