Attentionguided RGBD Saliency Detection using Appearance Information Abstract   Most of the deep convolutional neural networks (CNNs) based RGBD saliency models either regard the RGB and depth cues as the same status or trust the depth information excessively. However, they ignore that the lowquality depth map is an interference factor and the multilevel deep features originated from RGB images contain abundant appearance information. Therefore, we propose a novel RGBD saliency model, where the attentionguided bottomup and topdown modules are powerfully combined by using multilevel deep RGB features, to utilize the deep RGB and depth features in a sufficient and appropriate way. Concretely, a twostream structure based bottomup module is first constructed to dig and fuse the RGB and depth information, yielding the fused deep feature. Besides, the module embeds the depth cue based attention maps to guide the indication of salient objects. Then, considering the abundant appearance information, a topdown module is deployed to perform coarsetofine saliency inference, where the fused deep feature is progressively integrated with appearance information. Similarly, the attention map is also inserted into this module for locating salient objects. Extensive experiments are performed on five public RGBD datasets and the corresponding experimental results firmly demonstrate the effectiveness and superiority of our model when compared with the stateoftheart RGBD saliency models.

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.