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8,200 | Scribbler: Controlling Deep Image Synthesis with Sketch and Color | cs.CV | Recently, there have been several promising methods to generate realistic
imagery from deep convolutional networks. These methods sidestep the
traditional computer graphics rendering pipeline and instead generate imagery
at the pixel level by learning from large collections of photos (e.g. faces or
bedrooms). However, ... | computer science |
8,201 | Deep Metric Learning via Facility Location | cs.CV | Learning the representation and the similarity metric in an end-to-end
fashion with deep networks have demonstrated outstanding results for clustering
and retrieval. However, these recent approaches still suffer from the
performance degradation stemming from the local metric training procedure which
is unaware of the g... | computer science |
8,202 | Deep Image Category Discovery using a Transferred Similarity Function | cs.CV | Automatically discovering image categories in unlabeled natural images is one
of the important goals of unsupervised learning. However, the task is
challenging and even human beings define visual categories based on a large
amount of prior knowledge. In this paper, we similarly utilize prior knowledge
to facilitate the... | computer science |
8,203 | Core Sampling Framework for Pixel Classification | cs.CV | The intermediate map responses of a Convolutional Neural Network (CNN)
contain information about an image that can be used to extract contextual
knowledge about it. In this paper, we present a core sampling framework that is
able to use these activation maps from several layers as features to another
neural network usi... | computer science |
8,204 | Spatially Adaptive Computation Time for Residual Networks | cs.CV | This paper proposes a deep learning architecture based on Residual Network
that dynamically adjusts the number of executed layers for the regions of the
image. This architecture is end-to-end trainable, deterministic and
problem-agnostic. It is therefore applicable without any modifications to a
wide range of computer ... | computer science |
8,205 | Inverse Compositional Spatial Transformer Networks | cs.CV | In this paper, we establish a theoretical connection between the classical
Lucas & Kanade (LK) algorithm and the emerging topic of Spatial Transformer
Networks (STNs). STNs are of interest to the vision and learning communities
due to their natural ability to combine alignment and classification within the
same theoret... | computer science |
8,206 | Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise
Labeling | cs.CV | Pixel wise image labeling is an interesting and challenging problem with
great significance in the computer vision community. In order for a dense
labeling algorithm to be able to achieve accurate and precise results, it has
to consider the dependencies that exist in the joint space of both the input
and the output var... | computer science |
8,207 | Towards Score Following in Sheet Music Images | cs.LG | This paper addresses the matching of short music audio snippets to the
corresponding pixel location in images of sheet music. A system is presented
that simultaneously learns to read notes, listens to music and matches the
currently played music to its corresponding notes in the sheet. It consists of
an end-to-end mult... | computer science |
8,208 | CSVideoNet: A Real-time End-to-end Learning Framework for
High-frame-rate Video Compressive Sensing | cs.CV | This paper addresses the real-time encoding-decoding problem for
high-frame-rate video compressive sensing (CS). Unlike prior works that perform
reconstruction using iterative optimization-based approaches, we propose a
non-iterative model, named "CSVideoNet". CSVideoNet directly learns the inverse
mapping of CS and re... | computer science |
8,209 | Learning to predict where to look in interactive environments using deep
recurrent q-learning | cs.CV | Bottom-Up (BU) saliency models do not perform well in complex interactive
environments where humans are actively engaged in tasks (e.g., sandwich making
and playing the video games). In this paper, we leverage Reinforcement Learning
(RL) to highlight task-relevant locations of input frames. We propose a soft
attention ... | computer science |
8,210 | Deep Multi-instance Networks with Sparse Label Assignment for Whole
Mammogram Classification | cs.CV | Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods requires great effort to annotate the
training data by costly manual labeling and specialized computational models to
detect these annotations during test. Inspired by the success of using deep
convolutional f... | computer science |
8,211 | Adversarial Deep Structural Networks for Mammographic Mass Segmentation | cs.CV | Mass segmentation is an important task in mammogram analysis, providing
effective morphological features and regions of interest (ROI) for mass
detection and classification. Inspired by the success of using deep
convolutional features for natural image analysis and conditional random fields
(CRF) for structural learnin... | computer science |
8,212 | On Random Weights for Texture Generation in One Layer Neural Networks | cs.CV | Recent work in the literature has shown experimentally that one can use the
lower layers of a trained convolutional neural network (CNN) to model natural
textures. More interestingly, it has also been experimentally shown that only
one layer with random filters can also model textures although with less
variability. In... | computer science |
8,213 | Beyond Skip Connections: Top-Down Modulation for Object Detection | cs.CV | In recent years, we have seen tremendous progress in the field of object
detection. Most of the recent improvements have been achieved by targeting
deeper feedforward networks. However, many hard object categories such as
bottle, remote, etc. require representation of fine details and not just
coarse, semantic represen... | computer science |
8,214 | An Empirical Study of Language CNN for Image Captioning | cs.CV | Language Models based on recurrent neural networks have dominated recent
image caption generation tasks. In this paper, we introduce a Language CNN
model which is suitable for statistical language modeling tasks and shows
competitive performance in image captioning. In contrast to previous models
which predict next wor... | computer science |
8,215 | Offline Signature Identification by Fusion of Multiple Classifiers using
Statistical Learning Theory | cs.CV | This paper uses Support Vector Machines (SVM) to fuse multiple classifiers
for an offline signature system. From the signature images, global and local
features are extracted and the signatures are verified with the help of
Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers.
SVM is used to fu... | computer science |
8,216 | A Machine Learning Approach to Recovery of Scene Geometry from Images | cs.CV | Recovering the 3D structure of the scene from images yields useful
information for tasks such as shape and scene recognition, object detection, or
motion planning and object grasping in robotics. In this thesis, we introduce a
general machine learning approach called unsupervised CRF learning based on
maximizing the co... | computer science |
8,217 | Visual Transfer Learning: Informal Introduction and Literature Overview | cs.CV | Transfer learning techniques are important to handle small training sets and
to allow for quick generalization even from only a few examples. The following
paper is the introduction as well as the literature overview part of my thesis
related to the topic of transfer learning for visual recognition problems. | computer science |
8,218 | Image denoising with multi-layer perceptrons, part 1: comparison with
existing algorithms and with bounds | cs.CV | Image denoising can be described as the problem of mapping from a noisy image
to a noise-free image. The best currently available denoising methods
approximate this mapping with cleverly engineered algorithms. In this work we
attempt to learn this mapping directly with plain multi layer perceptrons (MLP)
applied to ima... | computer science |
8,219 | Image denoising with multi-layer perceptrons, part 2: training
trade-offs and analysis of their mechanisms | cs.CV | Image denoising can be described as the problem of mapping from a noisy image
to a noise-free image. In another paper, we show that multi-layer perceptrons
can achieve outstanding image denoising performance for various types of noise
(additive white Gaussian noise, mixed Poisson-Gaussian noise, JPEG artifacts,
salt-an... | computer science |
8,220 | Tangent-based manifold approximation with locally linear models | cs.LG | In this paper, we consider the problem of manifold approximation with affine
subspaces. Our objective is to discover a set of low dimensional affine
subspaces that represents manifold data accurately while preserving the
manifold's structure. For this purpose, we employ a greedy technique that
partitions manifold sampl... | computer science |
8,221 | Deep and Wide Multiscale Recursive Networks for Robust Image Labeling | cs.CV | Feedforward multilayer networks trained by supervised learning have recently
demonstrated state of the art performance on image labeling problems such as
boundary prediction and scene parsing. As even very low error rates can limit
practical usage of such systems, methods that perform closer to human accuracy
remain de... | computer science |
8,222 | Efficient pedestrian detection by directly optimize the partial area
under the ROC curve | cs.CV | Many typical applications of object detection operate within a prescribed
false-positive range. In this situation the performance of a detector should be
assessed on the basis of the area under the ROC curve over that range, rather
than over the full curve, as the performance outside the range is irrelevant.
This mea... | computer science |
8,223 | Unsupervised Learning of Invariant Representations in Hierarchical
Architectures | cs.CV | The present phase of Machine Learning is characterized by supervised learning
algorithms relying on large sets of labeled examples ($n \to \infty$). The next
phase is likely to focus on algorithms capable of learning from very few
labeled examples ($n \to 1$), like humans seem able to do. We propose an
approach to this... | computer science |
8,224 | Dual coordinate solvers for large-scale structural SVMs | cs.LG | This manuscript describes a method for training linear SVMs (including binary
SVMs, SVM regression, and structural SVMs) from large, out-of-core training
datasets. Current strategies for large-scale learning fall into one of two
camps; batch algorithms which solve the learning problem given a finite
datasets, and onlin... | computer science |
8,225 | Classifiers With a Reject Option for Early Time-Series Classification | cs.CV | Early classification of time-series data in a dynamic environment is a
challenging problem of great importance in signal processing. This paper
proposes a classifier architecture with a reject option capable of online
decision making without the need to wait for the entire time series signal to
be present. The main ide... | computer science |
8,226 | ECOC-Based Training of Neural Networks for Face Recognition | cs.CV | Error Correcting Output Codes, ECOC, is an output representation method
capable of discovering some of the errors produced in classification tasks.
This paper describes the application of ECOC to the training of feed forward
neural networks, FFNN, for improving the overall accuracy of classification
systems. Indeed, to... | computer science |
8,227 | Using Web Co-occurrence Statistics for Improving Image Categorization | cs.CV | Object recognition and localization are important tasks in computer vision.
The focus of this work is the incorporation of contextual information in order
to improve object recognition and localization. For instance, it is natural to
expect not to see an elephant to appear in the middle of an ocean. We consider
a simpl... | computer science |
8,228 | Sequentially Generated Instance-Dependent Image Representations for
Classification | cs.CV | In this paper, we investigate a new framework for image classification that
adaptively generates spatial representations. Our strategy is based on a
sequential process that learns to explore the different regions of any image in
order to infer its category. In particular, the choice of regions is specific
to each image... | computer science |
8,229 | Active Deformable Part Models | cs.CV | This paper presents an active approach for part-based object detection, which
optimizes the order of part filter evaluations and the time at which to stop
and make a prediction. Statistics, describing the part responses, are learned
from training data and are used to formalize the part scheduling problem as an
offline ... | computer science |
8,230 | Exploiting Linear Structure Within Convolutional Networks for Efficient
Evaluation | cs.CV | We present techniques for speeding up the test-time evaluation of large
convolutional networks, designed for object recognition tasks. These models
deliver impressive accuracy but each image evaluation requires millions of
floating point operations, making their deployment on smartphones and
Internet-scale clusters pro... | computer science |
8,231 | Fast Supervised Hashing with Decision Trees for High-Dimensional Data | cs.CV | Supervised hashing aims to map the original features to compact binary codes
that are able to preserve label based similarity in the Hamming space.
Non-linear hash functions have demonstrated the advantage over linear ones due
to their powerful generalization capability. In the literature, kernel
functions are typicall... | computer science |
8,232 | Cost-Effective HITs for Relative Similarity Comparisons | cs.CV | Similarity comparisons of the form "Is object a more similar to b than to c?"
are useful for computer vision and machine learning applications.
Unfortunately, an embedding of $n$ points is specified by $n^3$ triplets,
making collecting every triplet an expensive task. In noticing this difficulty,
other researchers have... | computer science |
8,233 | Scalable Similarity Learning using Large Margin Neighborhood Embedding | cs.CV | Classifying large-scale image data into object categories is an important
problem that has received increasing research attention. Given the huge amount
of data, non-parametric approaches such as nearest neighbor classifiers have
shown promising results, especially when they are underpinned by a learned
distance or sim... | computer science |
8,234 | Geometric Tight Frame based Stylometry for Art Authentication of van
Gogh Paintings | cs.LG | This paper is about authenticating genuine van Gogh paintings from forgeries.
The authentication process depends on two key steps: feature extraction and
outlier detection. In this paper, a geometric tight frame and some simple
statistics of the tight frame coefficients are used to extract features from
the paintings. ... | computer science |
8,235 | Optimizing Ranking Measures for Compact Binary Code Learning | cs.LG | Hashing has proven a valuable tool for large-scale information retrieval.
Despite much success, existing hashing methods optimize over simple objectives
such as the reconstruction error or graph Laplacian related loss functions,
instead of the performance evaluation criteria of interest---multivariate
performance measu... | computer science |
8,236 | Weakly Supervised Action Labeling in Videos Under Ordering Constraints | cs.CV | We are given a set of video clips, each one annotated with an {\em ordered}
list of actions, such as "walk" then "sit" then "answer phone" extracted from,
for example, the associated text script. We seek to temporally localize the
individual actions in each clip as well as to learn a discriminative classifier
for each ... | computer science |
8,237 | An SVM Based Approach for Cardiac View Planning | cs.LG | We consider the problem of automatically prescribing oblique planes (short
axis, 4 chamber and 2 chamber views) in Cardiac Magnetic Resonance Imaging
(MRI). A concern with technologist-driven acquisitions of these planes is the
quality and time taken for the total examination. We propose an automated
solution incorpora... | computer science |
8,238 | An landcover fuzzy logic classification by maximumlikelihood | cs.CV | In present days remote sensing is most used application in many sectors. This
remote sensing uses different images like multispectral, hyper spectral or
ultra spectral. The remote sensing image classification is one of the
significant method to classify image. In this state we classify the maximum
likelihood classifica... | computer science |
8,239 | Feature and Region Selection for Visual Learning | cs.CV | Visual learning problems such as object classification and action recognition
are typically approached using extensions of the popular bag-of-words (BoW)
model. Despite its great success, it is unclear what visual features the BoW
model is learning: Which regions in the image or video are used to discriminate
among cla... | computer science |
8,240 | The U-curve optimization problem: improvements on the original algorithm
and time complexity analysis | cs.LG | The U-curve optimization problem is characterized by a decomposable in
U-shaped curves cost function over the chains of a Boolean lattice. This
problem can be applied to model the classical feature selection problem in
Machine Learning. Recently, the U-Curve algorithm was proposed to give optimal
solutions to the U-cur... | computer science |
8,241 | Beyond KernelBoost | cs.CV | In this Technical Report we propose a set of improvements with respect to the
KernelBoost classifier presented in [Becker et al., MICCAI 2013]. We start with
a scheme inspired by Auto-Context, but that is suitable in situations where the
lack of large training sets poses a potential problem of overfitting. The aim
is t... | computer science |
8,242 | Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive
Features | cs.CV | This paper aims at constructing a good graph for discovering intrinsic data
structures in a semi-supervised learning setting. Firstly, we propose to build
a non-negative low-rank and sparse (referred to as NNLRS) graph for the given
data representation. Specifically, the weights of edges in the graph are
obtained by se... | computer science |
8,243 | Linear, Deterministic, and Order-Invariant Initialization Methods for
the K-Means Clustering Algorithm | cs.LG | Over the past five decades, k-means has become the clustering algorithm of
choice in many application domains primarily due to its simplicity, time/space
efficiency, and invariance to the ordering of the data points. Unfortunately,
the algorithm's sensitivity to the initial selection of the cluster centers
remains to b... | computer science |
8,244 | Unsupervised learning of clutter-resistant visual representations from
natural videos | cs.CV | Populations of neurons in inferotemporal cortex (IT) maintain an explicit
code for object identity that also tolerates transformations of object
appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning
rules are not known, recent results [4, 5, 6] suggest the operation of an
unsupervised temporal-... | computer science |
8,245 | Transfer Learning for Video Recognition with Scarce Training Data for
Deep Convolutional Neural Network | cs.CV | Unconstrained video recognition and Deep Convolution Network (DCN) are two
active topics in computer vision recently. In this work, we apply DCNs as
frame-based recognizers for video recognition. Our preliminary studies,
however, show that video corpora with complete ground truth are usually not
large and diverse enoug... | computer science |
8,246 | Compute Less to Get More: Using ORC to Improve Sparse Filtering | cs.CV | Sparse Filtering is a popular feature learning algorithm for image
classification pipelines. In this paper, we connect the performance of Sparse
Filtering with spectral properties of the corresponding feature matrices. This
connection provides new insights into Sparse Filtering; in particular, it
suggests early stoppin... | computer science |
8,247 | Pedestrian Detection with Spatially Pooled Features and Structured
Ensemble Learning | cs.CV | Many typical applications of object detection operate within a prescribed
false-positive range. In this situation the performance of a detector should be
assessed on the basis of the area under the ROC curve over that range, rather
than over the full curve, as the performance outside the range is irrelevant.
This measu... | computer science |
8,248 | A non-linear learning & classification algorithm that achieves full
training accuracy with stellar classification accuracy | cs.CV | A fast Non-linear and non-iterative learning and classification algorithm is
synthesized and validated. This algorithm named the "Reverse Ripple
Effect(R.R.E)", achieves 100% learning accuracy but is computationally
expensive upon classification. The R.R.E is a (deterministic) algorithm that
super imposes Gaussian weig... | computer science |
8,249 | HSR: L1/2 Regularized Sparse Representation for Fast Face Recognition
using Hierarchical Feature Selection | cs.CV | In this paper, we propose a novel method for fast face recognition called
L1/2 Regularized Sparse Representation using Hierarchical Feature Selection
(HSR). By employing hierarchical feature selection, we can compress the scale
and dimension of global dictionary, which directly contributes to the decrease
of computatio... | computer science |
8,250 | Large-scale Online Feature Selection for Ultra-high Dimensional Sparse
Data | cs.LG | Feature selection with large-scale high-dimensional data is important yet
very challenging in machine learning and data mining. Online feature selection
is a promising new paradigm that is more efficient and scalable than batch
feature section methods, but the existing online approaches usually fall short
in their infe... | computer science |
8,251 | $\ell_1$-K-SVD: A Robust Dictionary Learning Algorithm With Simultaneous
Update | cs.CV | We develop a dictionary learning algorithm by minimizing the $\ell_1$
distortion metric on the data term, which is known to be robust for
non-Gaussian noise contamination. The proposed algorithm exploits the idea of
iterative minimization of weighted $\ell_2$ error. We refer to this algorithm
as $\ell_1$-K-SVD, where t... | computer science |
8,252 | Bayesian Robust Tensor Factorization for Incomplete Multiway Data | cs.CV | We propose a generative model for robust tensor factorization in the presence
of both missing data and outliers. The objective is to explicitly infer the
underlying low-CP-rank tensor capturing the global information and a sparse
tensor capturing the local information (also considered as outliers), thus
providing the r... | computer science |
8,253 | Implicit segmentation of Kannada characters in offline handwriting
recognition using hidden Markov models | cs.LG | We describe a method for classification of handwritten Kannada characters
using Hidden Markov Models (HMMs). Kannada script is agglutinative, where
simple shapes are concatenated horizontally to form a character. This results
in a large number of characters making the task of classification difficult.
Character segment... | computer science |
8,254 | MKL-RT: Multiple Kernel Learning for Ratio-trace Problems via Convex
Optimization | cs.CV | In the recent past, automatic selection or combination of kernels (or
features) based on multiple kernel learning (MKL) approaches has been receiving
significant attention from various research communities. Though MKL has been
extensively studied in the context of support vector machines (SVM), it is
relatively less ex... | computer science |
8,255 | KCRC-LCD: Discriminative Kernel Collaborative Representation with
Locality Constrained Dictionary for Visual Categorization | cs.CV | We consider the image classification problem via kernel collaborative
representation classification with locality constrained dictionary (KCRC-LCD).
Specifically, we propose a kernel collaborative representation classification
(KCRC) approach in which kernel method is used to improve the discrimination
ability of colla... | computer science |
8,256 | Geodesic Exponential Kernels: When Curvature and Linearity Conflict | cs.LG | We consider kernel methods on general geodesic metric spaces and provide both
negative and positive results. First we show that the common Gaussian kernel
can only be generalized to a positive definite kernel on a geodesic metric
space if the space is flat. As a result, for data on a Riemannian manifold, the
geodesic G... | computer science |
8,257 | Electrocardiography Separation of Mother and Baby | cs.CV | Extraction of Electrocardiography (ECG or EKG) signals of mother and baby is
a challenging task, because one single device is used and it receives a mixture
of multiple heart beats. In this paper, we would like to design a filter to
separate the signals from each other. | computer science |
8,258 | Deep Belief Network Training Improvement Using Elite Samples Minimizing
Free Energy | cs.LG | Nowadays this is very popular to use deep architectures in machine learning.
Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted
Boltzmann Machines (RBM) to create a powerful generative model using training
data. In this paper we present an improvement in a common method that is
usually used... | computer science |
8,259 | Anisotropic Agglomerative Adaptive Mean-Shift | cs.CV | Mean Shift today, is widely used for mode detection and clustering. The
technique though, is challenged in practice due to assumptions of isotropicity
and homoscedasticity. We present an adaptive Mean Shift methodology that allows
for full anisotropic clustering, through unsupervised local bandwidth
selection. The band... | computer science |
8,260 | Joint cross-domain classification and subspace learning for unsupervised
adaptation | cs.CV | Domain adaptation aims at adapting the knowledge acquired on a source domain
to a new different but related target domain. Several approaches have
beenproposed for classification tasks in the unsupervised scenario, where no
labeled target data are available. Most of the attention has been dedicated to
searching a new d... | computer science |
8,261 | Multiple Instance Reinforcement Learning for Efficient Weakly-Supervised
Detection in Images | cs.CV | State-of-the-art visual recognition and detection systems increasingly rely
on large amounts of training data and complex classifiers. Therefore it becomes
increasingly expensive both to manually annotate datasets and to keep running
times at levels acceptable for practical applications. In this paper, we
propose two s... | computer science |
8,262 | Metric Learning Driven Multi-Task Structured Output Optimization for
Robust Keypoint Tracking | cs.CV | As an important and challenging problem in computer vision and graphics,
keypoint-based object tracking is typically formulated in a spatio-temporal
statistical learning framework. However, most existing keypoint trackers are
incapable of effectively modeling and balancing the following three aspects in
a simultaneous ... | computer science |
8,263 | Learning Multi-target Tracking with Quadratic Object Interactions | cs.CV | We describe a model for multi-target tracking based on associating
collections of candidate detections across frames of a video. In order to model
pairwise interactions between different tracks, such as suppression of
overlapping tracks and contextual cues about co-occurence of different objects,
we augment a standard ... | computer science |
8,264 | Nearest Descent, In-Tree, and Clustering | cs.LG | In this paper, we propose a physically inspired graph-theoretical clustering
method, which first makes the data points organized into an attractive graph,
called In-Tree, via a physically inspired rule, called Nearest Descent (ND). In
particular, the rule of ND works to select the nearest node in the descending
directi... | computer science |
8,265 | Automatic Training Data Synthesis for Handwriting Recognition Using the
Structural Crossing-Over Technique | cs.CV | The paper presents a novel technique called "Structural Crossing-Over" to
synthesize qualified data for training machine learning-based handwriting
recognition. The proposed technique can provide a greater variety of patterns
of training data than the existing approaches such as elastic distortion and
tangent-based aff... | computer science |
8,266 | Speeding-up Convolutional Neural Networks Using Fine-tuned
CP-Decomposition | cs.CV | We propose a simple two-step approach for speeding up convolution layers
within large convolutional neural networks based on tensor decomposition and
discriminative fine-tuning. Given a layer, we use non-linear least squares to
compute a low-rank CP-decomposition of the 4D convolution kernel tensor into a
sum of a smal... | computer science |
8,267 | Automatic Discovery and Optimization of Parts for Image Classification | cs.CV | Part-based representations have been shown to be very useful for image
classification. Learning part-based models is often viewed as a two-stage
problem. First, a collection of informative parts is discovered, using
heuristics that promote part distinctiveness and diversity, and then
classifiers are trained on the vect... | computer science |
8,268 | Video (language) modeling: a baseline for generative models of natural
videos | cs.LG | We propose a strong baseline model for unsupervised feature learning using
video data. By learning to predict missing frames or extrapolate future frames
from an input video sequence, the model discovers both spatial and temporal
correlations which are useful to represent complex deformations and motion
patterns. The m... | computer science |
8,269 | A Novel Feature Selection and Extraction Technique for Classification | cs.LG | This paper presents a versatile technique for the purpose of feature
selection and extraction - Class Dependent Features (CDFs). We use CDFs to
improve the accuracy of classification and at the same time control
computational expense by tackling the curse of dimensionality. In order to
demonstrate the generality of thi... | computer science |
8,270 | Riemannian Metric Learning for Symmetric Positive Definite Matrices | cs.CV | Over the past few years, symmetric positive definite (SPD) matrices have been
receiving considerable attention from computer vision community. Though various
distance measures have been proposed in the past for comparing SPD matrices,
the two most widely-used measures are affine-invariant distance and
log-Euclidean dis... | computer science |
8,271 | Pairwise Constraint Propagation on Multi-View Data | cs.CV | This paper presents a graph-based learning approach to pairwise constraint
propagation on multi-view data. Although pairwise constraint propagation has
been studied extensively, pairwise constraints are usually defined over pairs
of data points from a single view, i.e., only intra-view constraint propagation
is conside... | computer science |
8,272 | Robust Face Recognition by Constrained Part-based Alignment | cs.CV | Developing a reliable and practical face recognition system is a
long-standing goal in computer vision research. Existing literature suggests
that pixel-wise face alignment is the key to achieve high-accuracy face
recognition. By assuming a human face as piece-wise planar surfaces, where each
surface corresponds to a f... | computer science |
8,273 | Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval | cs.CV | With the rapid growth of web images, hashing has received increasing
interests in large scale image retrieval. Research efforts have been devoted to
learning compact binary codes that preserve semantic similarity based on
labels. However, most of these hashing methods are designed to handle simple
binary similarity. Th... | computer science |
8,274 | Hyper-parameter optimization of Deep Convolutional Networks for object
recognition | cs.CV | Recently sequential model based optimization (SMBO) has emerged as a
promising hyper-parameter optimization strategy in machine learning. In this
work, we investigate SMBO to identify architecture hyper-parameters of deep
convolution networks (DCNs) object recognition. We propose a simple SMBO
strategy that starts from... | computer science |
8,275 | Real-World Font Recognition Using Deep Network and Domain Adaptation | cs.CV | We address a challenging fine-grain classification problem: recognizing a
font style from an image of text. In this task, it is very easy to generate
lots of rendered font examples but very hard to obtain real-world labeled
images. This real-to-synthetic domain gap caused poor generalization to new
real data in previou... | computer science |
8,276 | Direct l_(2,p)-Norm Learning for Feature Selection | cs.LG | In this paper, we propose a novel sparse learning based feature selection
method that directly optimizes a large margin linear classification model
sparsity with l_(2,p)-norm (0 < p < 1)subject to data-fitting constraints,
rather than using the sparsity as a regularization term. To solve the direct
sparsity optimizatio... | computer science |
8,277 | Unsupervised Feature Learning from Temporal Data | cs.CV | Current state-of-the-art classification and detection algorithms rely on
supervised training. In this work we study unsupervised feature learning in the
context of temporally coherent video data. We focus on feature learning from
unlabeled video data, using the assumption that adjacent video frames contain
semantically... | computer science |
8,278 | Performance measures for classification systems with rejection | cs.CV | Classifiers with rejection are essential in real-world applications where
misclassifications and their effects are critical. However, if no problem
specific cost function is defined, there are no established measures to assess
the performance of such classifiers. We introduce a set of desired properties
for performance... | computer science |
8,279 | Learning Multiple Visual Tasks while Discovering their Structure | cs.LG | Multi-task learning is a natural approach for computer vision applications
that require the simultaneous solution of several distinct but related
problems, e.g. object detection, classification, tracking of multiple agents,
or denoising, to name a few. The key idea is that exploring task relatedness
(structure) can lea... | computer science |
8,280 | F-SVM: Combination of Feature Transformation and SVM Learning via Convex
Relaxation | cs.LG | The generalization error bound of support vector machine (SVM) depends on the
ratio of radius and margin, while standard SVM only considers the maximization
of the margin but ignores the minimization of the radius. Several approaches
have been proposed to integrate radius and margin for joint learning of feature
transf... | computer science |
8,281 | Self-Tuned Deep Super Resolution | cs.LG | Deep learning has been successfully applied to image super resolution (SR).
In this paper, we propose a deep joint super resolution (DJSR) model to exploit
both external and self similarities for SR. A Stacked Denoising Convolutional
Auto Encoder (SDCAE) is first pre-trained on external examples with proper data
augmen... | computer science |
8,282 | Exploit Bounding Box Annotations for Multi-label Object Recognition | cs.CV | Convolutional neural networks (CNNs) have shown great performance as general
feature representations for object recognition applications. However, for
multi-label images that contain multiple objects from different categories,
scales and locations, global CNN features are not optimal. In this paper, we
incorporate loca... | computer science |
8,283 | Max-margin Deep Generative Models | cs.LG | Deep generative models (DGMs) are effective on learning multilayered
representations of complex data and performing inference of input data by
exploring the generative ability. However, little work has been done on
examining or empowering the discriminative ability of DGMs on making accurate
predictions. This paper pre... | computer science |
8,284 | FlowNet: Learning Optical Flow with Convolutional Networks | cs.CV | Convolutional neural networks (CNNs) have recently been very successful in a
variety of computer vision tasks, especially on those linked to recognition.
Optical flow estimation has not been among the tasks where CNNs were
successful. In this paper we construct appropriate CNNs which are capable of
solving the optical ... | computer science |
8,285 | Linear Spatial Pyramid Matching Using Non-convex and non-negative Sparse
Coding for Image Classification | cs.CV | Recently sparse coding have been highly successful in image classification
mainly due to its capability of incorporating the sparsity of image
representation. In this paper, we propose an improved sparse coding model based
on linear spatial pyramid matching(SPM) and Scale Invariant Feature Transform
(SIFT ) descriptors... | computer science |
8,286 | SVM and ELM: Who Wins? Object Recognition with Deep Convolutional
Features from ImageNet | cs.LG | Deep learning with a convolutional neural network (CNN) has been proved to be
very effective in feature extraction and representation of images. For image
classification problems, this work aim at finding which classifier is more
competitive based on high-level deep features of images. In this report, we
have discussed... | computer science |
8,287 | Constrained Convolutional Neural Networks for Weakly Supervised
Segmentation | cs.CV | We present an approach to learn a dense pixel-wise labeling from image-level
tags. Each image-level tag imposes constraints on the output labeling of a
Convolutional Neural Network (CNN) classifier. We propose Constrained CNN
(CCNN), a method which uses a novel loss function to optimize for any set of
linear constraint... | computer science |
8,288 | Time Series Classification using the Hidden-Unit Logistic Model | cs.LG | We present a new model for time series classification, called the hidden-unit
logistic model, that uses binary stochastic hidden units to model latent
structure in the data. The hidden units are connected in a chain structure that
models temporal dependencies in the data. Compared to the prior models for time
series cl... | computer science |
8,289 | CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique
Splits | cs.LG | We propose a novel algorithm for optimizing multivariate linear threshold
functions as split functions of decision trees to create improved Random Forest
classifiers. Standard tree induction methods resort to sampling and exhaustive
search to find good univariate split functions. In contrast, our method
computes a line... | computer science |
8,290 | Learning Discriminative Bayesian Networks from High-dimensional
Continuous Neuroimaging Data | cs.CV | Due to its causal semantics, Bayesian networks (BN) have been widely employed
to discover the underlying data relationship in exploratory studies, such as
brain research. Despite its success in modeling the probability distribution of
variables, BN is naturally a generative model, which is not necessarily
discriminativ... | computer science |
8,291 | Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical
Volumetric Image Segmentation | cs.CV | Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D
videos to segment them. They have a fixed input size and typically perceive
only small local contexts of the pixels to be classified as foreground or
background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive
the entire spat... | computer science |
8,292 | AttentionNet: Aggregating Weak Directions for Accurate Object Detection | cs.CV | We present a novel detection method using a deep convolutional neural network
(CNN), named AttentionNet. We cast an object detection problem as an iterative
classification problem, which is the most suitable form of a CNN. AttentionNet
provides quantized weak directions pointing a target object and the ensemble of
iter... | computer science |
8,293 | Variational Inference for Background Subtraction in Infrared Imagery | cs.CV | We propose a Gaussian mixture model for background subtraction in infrared
imagery. Following a Bayesian approach, our method automatically estimates the
number of Gaussian components as well as their parameters, while simultaneously
it avoids over/under fitting. The equations for estimating model parameters are
analyt... | computer science |
8,294 | Unsupervised Learning from Narrated Instruction Videos | cs.CV | We address the problem of automatically learning the main steps to complete a
certain task, such as changing a car tire, from a set of narrated instruction
videos. The contributions of this paper are three-fold. First, we develop a new
unsupervised learning approach that takes advantage of the complementary nature
of t... | computer science |
8,295 | Clustering Tree-structured Data on Manifold | cs.CV | Tree-structured data usually contain both topological and geometrical
information, and are necessarily considered on manifold instead of Euclidean
space for appropriate data parameterization and analysis. In this study, we
propose a novel tree-structured data parameterization, called
Topology-Attribute matrix (T-A matr... | computer science |
8,296 | Building a Large-scale Multimodal Knowledge Base System for Answering
Visual Queries | cs.CV | The complexity of the visual world creates significant challenges for
comprehensive visual understanding. In spite of recent successes in visual
recognition, today's vision systems would still struggle to deal with visual
queries that require a deeper reasoning. We propose a knowledge base (KB)
framework to handle an a... | computer science |
8,297 | Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian | cs.LG | Most visual recognition methods implicitly assume the data distribution
remains unchanged from training to testing. However, in practice domain shift
often exists, where real-world factors such as lighting and sensor type change
between train and test, and classifiers do not generalise from source to target
domains. It... | computer science |
8,298 | Nonlinear Metric Learning for kNN and SVMs through Geometric
Transformations | cs.LG | In recent years, research efforts to extend linear metric learning models to
handle nonlinear structures have attracted great interests. In this paper, we
propose a novel nonlinear solution through the utilization of deformable
geometric models to learn spatially varying metrics, and apply the strategy to
boost the per... | computer science |
8,299 | Manifold regularization in structured output space for semi-supervised
structured output prediction | cs.LG | Structured output prediction aims to learn a predictor to predict a
structured output from a input data vector. The structured outputs include
vector, tree, sequence, etc. We usually assume that we have a training set of
input-output pairs to train the predictor. However, in many real-world appli-
cations, it is diffic... | computer science |
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