20 research outputs found
Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition
We consider the problem of comparing the similarity of image sets with
variable-quantity, quality and un-ordered heterogeneous images. We use feature
restructuring to exploit the correlations of both innerinter-set images.
Specifically, the residual self-attention can effectively restructure the
features using the other features within a set to emphasize the discriminative
images and eliminate the redundancy. Then, a sparse/collaborative
learning-based dependency-guided representation scheme reconstructs the probe
features conditional to the gallery features in order to adaptively align the
two sets. This enables our framework to be compatible with both verification
and open-set identification. We show that the parametric self-attention network
and non-parametric dictionary learning can be trained end-to-end by a unified
alternative optimization scheme, and that the full framework is
permutation-invariant. In the numerical experiments we conducted, our method
achieves top performance on competitive image set/video-based face recognition
and person re-identification benchmarks.Comment: Accepted to ICCV 201
AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation
This paper targets on learning-based novel view synthesis from a single or
limited 2D images without the pose supervision. In the viewer-centered
coordinates, we construct an end-to-end trainable conditional variational
framework to disentangle the unsupervisely learned relative-pose/rotation and
implicit global 3D representation (shape, texture and the origin of
viewer-centered coordinates, etc.). The global appearance of the 3D object is
given by several appearance-describing images taken from any number of
viewpoints. Our spatial correlation module extracts a global 3D representation
from the appearance-describing images in a permutation invariant manner. Our
system can achieve implicitly 3D understanding without explicitly 3D
reconstruction. With an unsupervisely learned viewer-centered
relative-pose/rotation code, the decoder can hallucinate the novel view
continuously by sampling the relative-pose in a prior distribution. In various
applications, we demonstrate that our model can achieve comparable or even
better results than pose/3D model-supervised learning-based novel view
synthesis (NVS) methods with any number of input views.Comment: ECCV 202
CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS
The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research
Design and Analysis of Statistical Learning Algorithms which Control False Discoveries
In this thesis, general theoretical tools are constructed which can be applied to develop ma- chine learning algorithms which are consistent, with fast convergence and which minimize the generalization error by asymptotically controlling the rate of false discoveries (FDR) of features, especially for high dimensional datasets. Even though the main inspiration of this work comes from biological applications, where the data is extremely high dimensional and often hard to obtain, the developed methods are applicable to any general statistical learning problem.
In this work, the various machine learning tasks like hypothesis testing, classification, regression, etc are formulated as risk minimization algorithms. This allows such learning tasks to be viewed as optimization problems, which can be solved using first order optimization techniques in case of large data scenarios, while one could use faster converging second order techniques for small to moderately sized data sets. Further, such a formulation allows us to estimate the first order convergence rates of an empirical risk estimator for any arbitrary learning problem, using techniques from large deviation theory.
In many scientific applications, robust discovery of factors affecting an outcome or a phe- notype, is more important than the accuracy of predictions. Hence, it is essential to find an appropriate approach to regularize an under-determined estimation problem and thereby control the generalization error. In this work, the use of local probability of false discovery is explored as such a regularization parameter, which forces the optimized solution towards functions with a lower probability to be a false discovery. Again, techniques from large devi- ation theory and the Gibbs principle allow the derivation of an appropriately regularized cost function.
These two theoretical results are then used to develop concrete applications. First, the problem of multi-classification is analyzed, which classifies a sample from an arbitrary proba- bility measure into a finite number of categories, based on a given training data set. A general risk functional is derived, which can be used to learn Bayes optimal classifiers controlling the false discovery rate.
Secondly, the problem of model selection in the regression context is considered, aiming to select a subset of given regressors which explains most of the observed variation i.e. perform ANOVA. Again, using techniques mentioned above, a risk function is derived which when optimized, controls the rate of false discoveries. This technique is shown to outperform the popular LASSO algorithm, which can be proven to not control the FDR, but only the FWER.
Finally, the problem of inferring under-sampled and partially observed non-negative dis- crete random variables is addressed, which has applications to analyzing RNA sequencing data. By assuming infinite divisibility of the underlying random variable, its characterization as being a discrete Compound Poisson Measure (DCP), is derived. This allows construction of a non-parametric Bayesian model of DCPs with a Pitman-Yor Mixture process prior, which is shown to allow for consistent inference under Kullback-Liebler and Renyi divergences even in the under-sampled regime