Essay

“I got up and pretended to study the pictures on the walls like I was a lover of religious art. When I got to the Merciful Mother right above Sinita’s head, I reached in my pocket and pulled out the bottom I’d found on the train. It was sparkly like a diamond and had a little hole in back so you could thread a ribbon through it and wear it like a romantic lady’s choker necklace. It wasn’t something I’d do, but I could see the button would make a good trade with someone inclined in that direction.

List and describe the four force multipliers. How do terrorists us force multipliers? Give examples for each force multipliers.

Even though the Unified Deep Net (UDN) method learned features by designing hidden layers for the Convolutional Neural Network such that features, deformable parts, occlusions, and classification can be jointly optimized, one of its problems is it treats pedestrian detection as a single binary classification task, which is not able to capture rich pedestrian variations. For example, the method is not able to distinguish pedestrians from hard negatives due to their visual similarities. This problem can be resolved by jointly optimizing pedestrian detection with auxiliary semantic tasks, such as including pedestrian attributes and scene attributes, which was represented in our previous report.

The rest of the paper is organised as follows. In Sect. 1.1 we review the related work on action recognition using both shallow and deep architectures. In Sect. 2 we introduce the two-stream architecture and specify the Spatial ConvNet. Sect. 3 introduces the Temporal ConvNet and in particular how it generalizes the previous architectures reviewed in Sect. 1.1. A mult-task learning framework is developed in Sect. 4 in order to allow effortless combination of training data over

In order to specify the middle layer of an RBF we have to decide the number of neurons of the layer and their kernel functions which are usually Gaussian functions. In this paper we use a Gaussian function as a kernel function. A Gaussian function is specified by its center and width. The simplest and most general method to decide the middle layer neurons is to create a neuron for each training pattern. However the method is usually not practical since in most applications there are a large number of training patterns and the dimension of the input space is fairly large. Therefore it is usual and practical to first cluster the training patterns to a reasonable number of groups by using a clustering algorithm such as K-means or SOFM and then to assign a neuron to each cluster. A simple way, though not always effective, is to choose a relatively small number of patterns randomly among the training patterns and create only that many neurons. A clustering algorithm is a kind of an unsupervised learning algorithm and is used when the class of each training pattern is not known. But an RBFN is a supervised learning network. And we know at least the class of each training pattern. So we’d better take advantage of the information of these class memberships when we cluster the training patterns. Namely we cluster the training patterns class by class instead of the entire patterns at the same time (Moody and Darken, 1989; Musavi et al., 1992). In this way we can reduce at least the

In order to accomplish this, we are making use of Torch framework [2]. It is a scientific computing framework and supports wide range of machine learning algorithms. Torch has built-in support for large ecosystem of community-driven packages in machine learning. For more efficient and faster results, we are making use of CUDA, which stands for Compute Unified Device Architecture [3]. It is a parallel computing platform built by NVIDIA. As the torch framework has support for CUDA libraries, it is possible to deploy the neural network directly on the GPU instead of the CPU. This greatly helps in optimizing the performance of neural network and provides much faster results than standard CPUs.

Wang, Limin, et al. used [5] VGG networks to find state of the art accuracy on Place205, MIT67 and SUN397. They used Caffe toolbox to design ConvNets. They desirned three types of VGG nets named VGG11, VGG13, VGG16. Trained the VGG nets on Place205 dataset. To reduce computation cost they loaded weight to VGG13 from VGG11 and from VGG13 to VGG16. Using VGG nets they found state of the art accuracy on these three datasets compared to GoogleNet and AlexNet.

Steve Lawrence and Andrew D. Back in 1997 referred to [1] proposed a convolutional neural network approach which includes the

These stages represent the network from input to pooling, pooling to pooling, and pooling to output layers. Using this proposal, CNNs evolved by the GA resulted in very different architectures when compared with hand-designed ones, but often with a better performance. However, a major disadvantage of the proposed framework is the training process, which has to be done from scratch for every evolved new architecture. Fixed-size convolutional blocks also limit the proposed method by not incorporating multi-scaling analysis, and this issue will be present in most of the direct encoded methods designed for this task.

Recently, George Azzopardi and Nicolai Petkov [36] propose a trainable filter which we call Combination Of Shifted FIlter REsponses (COSFIRE) and use for keypoint detection and pattern recognition.

A flatten layer is then applied to produce 1 dimensional vector from 512x7x7 input. A fully-connected layer with 256 then added which is connected with every neurons of previous layer. A dropout layer of probability 0.5 is added just after first fully connected layer to reduce overfitting.

Deep architectures are algorithms that apply successive non-linear transformations to data, in an attempt to discover higher and higher level representations of this data [1]. The most common example is an Artificial Neural Network (ANN) with multiple hidden layers. While deep learning is an old concept, learning algorithms on deep architectures were slow and thus impractical [2]. Discovery of new training methods and improved processing power in recent years have allowed for state-of-the-art empirical results in computer vision and speech recognition. Most famously, a group of researchers from Stanford and Google trained a deep network on millions of unlabeled images, which was then able to detect human faces, bodies, and cats [3]. Deep

The network settings are described in Table I, and represented as 64(9)-32(1)-32(5) - 32(3)-64(1)-1[9]-s. We used a strided convolution and deconvolution technique with stride s > 1 for network speed up. In our experiment we consider Y luminance color components only and the compressed luminance frames become input to MDCNN network. There is no max pooling or full-connected (FC) layers in our network. Rectified Linear Unit (ReLu) max (0, X) [19] is applied on the filter responses. Our CNN network is designed to learn the residue between output and input frame, as there is lots of similarity between them. So the final output frame F(Y) is just a combination of input frame and learnt residual frame.

On other hand of the proposed recognition system, another feature extraction is modified and optimized to be used known as

Over the last two years, a sequence of results on benchmark visual recognition tasks has demonstrated that convolutional neural networks (CNNs) [3] will likely replace engineered features, such as SIFT and HOG, for a wide variety of problems. This sequence started with the breakthrough ImageNet classification results reported by Krizhevsky et al. [10].