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The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network using a minimal model. The proposed minimal convolutional neural network is presented using a layering approach. This approach provides a clear understanding of the main mathematical operations in a convolutional neural network. Hence,...
In this paper we proposed a 4-stage coarse-to-fine framework to tackle the facial landmark localization problem in-the-wild. In our system, we first predict the landmark key points on a coarse level of granularity, which sets a good initialization for the whole framework. Then we group the key points into several components and refine each component with local patches cropped within them. After that...
In the case of building large convolutional neural networks, signal propagation speed is one of priority factors. Training large neural structures requires enormous time for achieving satisfying accuracy. In addition, the networks need to be learn by very large sets of good quality training images, which is another time-consuming factor. The paper presents a fast computing framework with some methods...
Convolutional neural networks (CNN) are widely used in computer vision, especially in image classification. However, the way in which information and invariance properties are encoded through in deep CNN architectures is still an open question. In this paper, we propose to modify the standard convolutional block of CNN in order to transfer more information layer after layer while keeping some invariance...
Generating descriptions for visual data (images and video) automatically has been a complicated task in the field of Computer Vision and Artificial Intelligence. This paper discusses the working of and improvements on an algorithm called Neural Image Captioner (NIC) by Oriol Vinyals and his team, which uses a deep convolutional and recurrent architecture to generate natural language sentences to describe...
Many recent visual recognition systems can be seen as being composed of multiple layers of convolutional filter banks, interspersed with various types of non-linearities. This includes Convolutional Networks, HMAX-type architectures, as well as systems based on dense SIFT features or Histogram of Gradients. This paper describes a highly-compact and low power embedded system that can run such vision...
Modeling of facial expression plays a very important role in the research of synthesis and recognition offacial expression. However the current facial expression model can not correctly represent the daily facial expressions. The nature of the facial expressions is analyzed. A qualitative description of the corresponding facial expression space is presented. And then a new facial expression space...
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