SANet: Smoothed Attention Network for Single Stage Face Detector Recently, significant effort has been devoted to exploring the role of feature fusion and enriching contextual information on detecting multiscale faces. However, simply integrating features of different levels could lead to introducing significant noise. Moreover, recently proposed approaches of enriching contextual information are not efficient or ignore the gridding artifacts produced by dilated convolution. To tackle these issues, we developed a smoothed attention network (dubbed SANet), which introduces an Attentionguided Feature Fusion Module (AFFM) and a Smoothed Context Enhancement Module (SCEM). In particular, the AFFM applies an attention module to highlevel semantic features and fuses attentionfocused features with lowlevel semantic features to reduce the noise of the fused feature map. The SCEM stacks dilated convolution and convolution layers alternately to relearn the relationship among completely separate sets of units produced by dilated convolution to maintain consistency of local information. The SANet achieves promising results on the WIDER FACE validation and testing datasets and is stateoftheart on the UFDD dataset.

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.