Recently, heatmap regression models have become popular due to their superior performance in locating facial landmarks. However, three major problems still exist among these models: (1) they are computationally expensive; (2) usually lack explicit constraints on global shapes; (3) domain gaps commonly present. To address problems, we propose Pixel-in-Pixel Net (PIPNet) for landmark detection. T...

As deep neural networks (DNNs) are widely used in the field of remote sensing image recognition, there is a model security issue that cannot be ignored. DNNs have been shown to vulnerable small perturbations large number studies past, and this risk naturally exists object detection models based on DNNs. The complexity makes it difficult implement adversarial attacks them, resulting current lack...

in this research the temporal-spatial changing trend of nightly temperatures in isfahan province has been studied with the purpose of manifesting temperature changes, recognition of temperature abnormality and comparison for the two methods of pixel and station. on this basis, the data of the minimum temperature average of 21 synoptic and climatology stations were processed in the said province...

Pixel-swapping algorithm is a simple and efficient technique for sub-pixel mapping (Atkinson, 2001 and 2005). It was initially applied in shoreline and rural land-cover mapping but has been expanded to other land-cover mapping. However, due to its random initializing process, this algorithm must swap a large number of sub-pixels, and therefore it is computation intensive. This computing power c...

We propose an end-to-end learning framework for foreground object segmentation. Given a single novel image, our approach produces a pixel-level mask for all “object-like” regions—even for object categories never seen during training. We formulate the task as a structured prediction problem of assigning a foreground/background label to each pixel, implemented using a deep fully convolutional net...