92,744 research outputs found
CSGNet: Neural Shape Parser for Constructive Solid Geometry
We present a neural architecture that takes as input a 2D or 3D shape and
outputs a program that generates the shape. The instructions in our program are
based on constructive solid geometry principles, i.e., a set of boolean
operations on shape primitives defined recursively. Bottom-up techniques for
this shape parsing task rely on primitive detection and are inherently slow
since the search space over possible primitive combinations is large. In
contrast, our model uses a recurrent neural network that parses the input shape
in a top-down manner, which is significantly faster and yields a compact and
easy-to-interpret sequence of modeling instructions. Our model is also more
effective as a shape detector compared to existing state-of-the-art detection
techniques. We finally demonstrate that our network can be trained on novel
datasets without ground-truth program annotations through policy gradient
techniques.Comment: Accepted at CVPR-201
How Much Information is in a Jet?
Machine learning techniques are increasingly being applied toward data
analyses at the Large Hadron Collider, especially with applications for
discrimination of jets with different originating particles. Previous studies
of the power of machine learning to jet physics has typically employed image
recognition, natural language processing, or other algorithms that have been
extensively developed in computer science. While these studies have
demonstrated impressive discrimination power, often exceeding that of
widely-used observables, they have been formulated in a non-constructive manner
and it is not clear what additional information the machines are learning. In
this paper, we study machine learning for jet physics constructively,
expressing all of the information in a jet onto sets of observables that
completely and minimally span N-body phase space. For concreteness, we study
the application of machine learning for discrimination of boosted, hadronic
decays of Z bosons from jets initiated by QCD processes. Our results
demonstrate that the information in a jet that is useful for discrimination
power of QCD jets from Z bosons is saturated by only considering observables
that are sensitive to 4-body (8 dimensional) phase space.Comment: 14 pages + appendices, 10 figures; v2: JHEP version, updated neural
network, included deeper network and boosted decision tree result
Curve network interpolation by quadratic B-spline surfaces
In this paper we investigate the problem of interpolating a B-spline curve
network, in order to create a surface satisfying such a constraint and defined
by blending functions spanning the space of bivariate quadratic splines
on criss-cross triangulations. We prove the existence and uniqueness of the
surface, providing a constructive algorithm for its generation. We also present
numerical and graphical results and comparisons with other methods.Comment: With respect to the previous version, this version of the paper is
improved. The results have been reorganized and it is more general since it
deals with non uniform knot partitions. Accepted for publication in Computer
Aided Geometric Design, October 201
Time-Efficient Hybrid Approach for Facial Expression Recognition
Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database
Small-variance asymptotics for Bayesian neural networks
Bayesian neural networks (BNNs) are a rich and flexible class of models that have several advantages over standard feedforward networks, but are typically expensive to train on large-scale data. In this thesis, we explore the use of small-variance asymptotics-an approach to yielding fast algorithms from probabilistic models-on various Bayesian neural network models. We first demonstrate how small-variance asymptotics shows precise connections between standard neural networks and BNNs; for example, particular sampling algorithms for BNNs reduce to standard backpropagation in the small-variance limit. We then explore a more complex BNN where the number of hidden units is additionally treated as a random variable in the model. While standard sampling schemes would be too slow to be practical, our asymptotic approach yields a simple method for extending standard backpropagation to the case where the number of hidden units is not fixed. We show on several data sets that the resulting algorithm has benefits over backpropagation on networks with a fixed architecture.2019-01-02T00:00:00
Deep Extreme Cut: From Extreme Points to Object Segmentation
This paper explores the use of extreme points in an object (left-most,
right-most, top, bottom pixels) as input to obtain precise object segmentation
for images and videos. We do so by adding an extra channel to the image in the
input of a convolutional neural network (CNN), which contains a Gaussian
centered in each of the extreme points. The CNN learns to transform this
information into a segmentation of an object that matches those extreme points.
We demonstrate the usefulness of this approach for guided segmentation
(grabcut-style), interactive segmentation, video object segmentation, and dense
segmentation annotation. We show that we obtain the most precise results to
date, also with less user input, in an extensive and varied selection of
benchmarks and datasets. All our models and code are publicly available on
http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr/.Comment: CVPR 2018 camera ready. Project webpage and code:
http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr
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