Salient Object Detection by Pyramid Networks with Gating In the paper, we propose a salient object detection architecture named as Gating for Double Pyramid Networks (GDPNet). It consists of two pyramid structures: Feature Pyramid Network (FPN) and Pyramid Pooling Module (PPM). FPN has the capability of capturing the inherent multiscale and pyramid hierarchy, while PPM can exploit the global context information by differentregionbased context aggregation. It is known that the irrelevant information corresponding to nonsalient objects or background may deteriorate the performance of the model. We introduce two gating strategies, i.e., CrossGating for FPN and SingleGating for PPM, to suppress the incurred irrelevant information in hidden features. Our proposed approach achieves stateoftheart performance on five benchmark datasets, which demonstrates the effectiveness and robust feature extraction capability of proposed GDPNet.

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.