Visual saliency detection, toward the simulation of human visual system (HVS), has drawn much attention in recent decades. Reconstruction based saliency detection models are established for saliency detection, which predict unexpected regions via linear combination or auto-encoder network. However, these models are ineffective in dealing with images due to the loss of spatial information caused by the conversion from images to vectors. In this paper, a novel approach is proposed to solve this problem. The core is a deep reconstruction model, i.e., convolutional neural network for reconstruction stacked with auto-encoder (CN-NR). On the one hand, the use of CNN is able to directly take two-dimensional data as input instead of having to convert the matrix to a series of vectors as in conventional reconstruction based saliency detection methods. On the other

Pedestrian detection has been researched for decades. Recently, an anchor-free method CSP is proposed to generate the pedestrian bounding box directly. When the predicted center deviates from the ground truth in the testing phase, the CSP model generates deviated pedestrian bounding box, which leads to false detection in occlusion situations. To handle this problem, we refine the scale regression branch of the CSP model to generate a more accurate prediction. The new scale regression branch outputs the distances between the center and the four edges of the pedestrian bounding box. Even if the predicted center deviates from the ground truth, an accurate bounding box can still be obtained. Moreover, we integrate a self-attention module into our model to take full advantage of the features in different depth layers. Our proposed model achieves better performance than the state-of-the-art detectors in comparison experiments on the two datasets, i.e., Citypersons and Caltech.