4,567 research outputs found
Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
Head-pose estimation has many applications, such as social event analysis,
human-robot and human-computer interaction, driving assistance, and so forth.
Head-pose estimation is challenging because it must cope with changing
illumination conditions, variabilities in face orientation and in appearance,
partial occlusions of facial landmarks, as well as bounding-box-to-face
alignment errors. We propose tu use a mixture of linear regressions with
partially-latent output. This regression method learns to map high-dimensional
feature vectors (extracted from bounding boxes of faces) onto the joint space
of head-pose angles and bounding-box shifts, such that they are robustly
predicted in the presence of unobservable phenomena. We describe in detail the
mapping method that combines the merits of unsupervised manifold learning
techniques and of mixtures of regressions. We validate our method with three
publicly available datasets and we thoroughly benchmark four variants of the
proposed algorithm with several state-of-the-art head-pose estimation methods.Comment: 12 pages, 5 figures, 3 table
Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters
The classical method of determining the atomic structure of complex molecules
by analyzing diffraction patterns is currently undergoing drastic developments.
Modern techniques for producing extremely bright and coherent X-ray lasers
allow a beam of streaming particles to be intercepted and hit by an ultrashort
high energy X-ray beam. Through machine learning methods the data thus
collected can be transformed into a three-dimensional volumetric intensity map
of the particle itself. The computational complexity associated with this
problem is very high such that clusters of data parallel accelerators are
required.
We have implemented a distributed and highly efficient algorithm for
inversion of large collections of diffraction patterns targeting clusters of
hundreds of GPUs. With the expected enormous amount of diffraction data to be
produced in the foreseeable future, this is the required scale to approach real
time processing of data at the beam site. Using both real and synthetic data we
look at the scaling properties of the application and discuss the overall
computational viability of this exciting and novel imaging technique
Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation
In the traditional object recognition pipeline, descriptors are densely
sampled over an image, pooled into a high dimensional non-linear representation
and then passed to a classifier. In recent years, Fisher Vectors have proven
empirically to be the leading representation for a large variety of
applications. The Fisher Vector is typically taken as the gradients of the
log-likelihood of descriptors, with respect to the parameters of a Gaussian
Mixture Model (GMM). Motivated by the assumption that different distributions
should be applied for different datasets, we present two other Mixture Models
and derive their Expectation-Maximization and Fisher Vector expressions. The
first is a Laplacian Mixture Model (LMM), which is based on the Laplacian
distribution. The second Mixture Model presented is a Hybrid Gaussian-Laplacian
Mixture Model (HGLMM) which is based on a weighted geometric mean of the
Gaussian and Laplacian distribution. An interesting property of the
Expectation-Maximization algorithm for the latter is that in the maximization
step, each dimension in each component is chosen to be either a Gaussian or a
Laplacian. Finally, by using the new Fisher Vectors derived from HGLMMs, we
achieve state-of-the-art results for both the image annotation and the image
search by a sentence tasks.Comment: new version includes text synthesis by an RNN and experiments with
the COCO benchmar
SVMs for Automatic Speech Recognition: a Survey
Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact.
During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed.
These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research
- …