Salient object detection based on backbone enhanced network Abstract   The Convolutional Neural Networks(CNNs) with encoderdecoder 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 pretrained 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 pretrained backbone network and result in overfitting, 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 encoderdecoder 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 addtion, 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 multiscale 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 stateoftheart methods without any additional supervision.

Salient Object Detection based on CNN Fusion of Two Types of Saliency Models Salient object detection has attracted significant attention, and recently many heuristic computational models have been developed for object detection. In this paper, we introduced a new approach that is based on convolutional neural network fusion strategy to combine the saliency maps generated by highdimensional color transform and salient object detection integrating discriminative regional features methods. Our method is based on the observation of salient regions and has a distinctive role in removing background information, which in humanxe2x80x99s perspective is not required in the object detection task. The key contribution of this work is a pipeline of CNN based fusion method, which not only performs best but also generates the saliency maps closer to the ground truth. The experimental results show that our method demonstrates the effectiveness of the proposed pipeline while comparing it with other stateoftheart methods.