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8,300 | Supervised learning of sparse context reconstruction coefficients for
data representation and classification | cs.LG | Context of data points, which is usually defined as the other data points in
a data set, has been found to play important roles in data representation and
classification. In this paper, we study the problem of using context of a data
point for its classification problem. Our work is inspired by the observation
that act... | computer science |
8,301 | Cascade Learning by Optimally Partitioning | cs.CV | Cascaded AdaBoost classifier is a well-known efficient object detection
algorithm. The cascade structure has many parameters to be determined. Most of
existing cascade learning algorithms are designed by assigning detection rate
and false positive rate to each stage either dynamically or statically. Their
objective fun... | computer science |
8,302 | Distributed Compressive Sensing: A Deep Learning Approach | cs.LG | Various studies that address the compressed sensing problem with Multiple
Measurement Vectors (MMVs) have been recently carried. These studies assume the
vectors of the different channels to be jointly sparse. In this paper, we relax
this condition. Instead we assume that these sparse vectors depend on each
other but t... | computer science |
8,303 | Learning Sampling Distributions for Efficient Object Detection | cs.CV | Object detection is an important task in computer vision and learning
systems. Multistage particle windows (MPW), proposed by Gualdi et al., is an
algorithm of fast and accurate object detection. By sampling particle windows
from a proposal distribution (PD), MPW avoids exhaustively scanning the image.
Despite its succ... | computer science |
8,304 | Multiple kernel multivariate performance learning using cutting plane
algorithm | cs.LG | In this paper, we propose a multi-kernel classifier learning algorithm to
optimize a given nonlinear and nonsmoonth multivariate classifier performance
measure. Moreover, to solve the problem of kernel function selection and kernel
parameter tuning, we proposed to construct an optimal kernel by weighted linear
combinat... | computer science |
8,305 | Metastatic liver tumour segmentation from discriminant Grassmannian
manifolds | cs.LG | The early detection, diagnosis and monitoring of liver cancer progression can
be achieved with the precise delineation of metastatic tumours. However,
accurate automated segmentation remains challenging due to the presence of
noise, inhomogeneity and the high appearance variability of malignant tissue.
In this paper, w... | computer science |
8,306 | CNN Based Hashing for Image Retrieval | cs.CV | Along with data on the web increasing dramatically, hashing is becoming more
and more popular as a method of approximate nearest neighbor search. Previous
supervised hashing methods utilized similarity/dissimilarity matrix to get
semantic information. But the matrix is not easy to construct for a new
dataset. Rather th... | computer science |
8,307 | DeXpression: Deep Convolutional Neural Network for Expression
Recognition | cs.CV | We propose a convolutional neural network (CNN) architecture for facial
expression recognition. The proposed architecture is independent of any
hand-crafted feature extraction and performs better than the earlier proposed
convolutional neural network based approaches. We visualize the automatically
extracted features w... | computer science |
8,308 | Online Object Tracking, Learning and Parsing with And-Or Graphs | cs.CV | This paper presents a method, called AOGTracker, for simultaneously tracking,
learning and parsing (TLP) of unknown objects in video sequences with a
hierarchical and compositional And-Or graph (AOG) representation. %The AOG
captures both structural and appearance variations of a target object in a
principled way. The ... | computer science |
8,309 | Approximate Fisher Kernels of non-iid Image Models for Image
Categorization | cs.CV | The bag-of-words (BoW) model treats images as sets of local descriptors and
represents them by visual word histograms. The Fisher vector (FV)
representation extends BoW, by considering the first and second order
statistics of local descriptors. In both representations local descriptors are
assumed to be identically and... | computer science |
8,310 | Relaxed Multiple-Instance SVM with Application to Object Discovery | cs.CV | Multiple-instance learning (MIL) has served as an important tool for a wide
range of vision applications, for instance, image classification, object
detection, and visual tracking. In this paper, we propose a novel method to
solve the classical MIL problem, named relaxed multiple-instance SVM (RMI-SVM).
We treat the po... | computer science |
8,311 | Evaluation of Joint Multi-Instance Multi-Label Learning For Breast
Cancer Diagnosis | cs.CV | Multi-instance multi-label (MIML) learning is a challenging problem in many
aspects. Such learning approaches might be useful for many medical diagnosis
applications including breast cancer detection and classification. In this
study subset of digiPATH dataset (whole slide digital breast cancer
histopathology images) a... | computer science |
8,312 | Dual Principal Component Pursuit | cs.CV | We consider the problem of outlier rejection in single subspace learning.
Classical approaches work with a direct representation of the subspace, and are
thus efficient when the subspace dimension is small. Our approach works with a
dual representation of the subspace and hence aims to find its orthogonal
complement; a... | computer science |
8,313 | Filtrated Spectral Algebraic Subspace Clustering | cs.CV | Algebraic Subspace Clustering (ASC) is a simple and elegant method based on
polynomial fitting and differentiation for clustering noiseless data drawn from
an arbitrary union of subspaces. In practice, however, ASC is limited to
equi-dimensional subspaces because the estimation of the subspace dimension via
algebraic m... | computer science |
8,314 | Learning with $\ell^{0}$-Graph: $\ell^{0}$-Induced Sparse Subspace
Clustering | cs.LG | Sparse subspace clustering methods, such as Sparse Subspace Clustering (SSC)
\cite{ElhamifarV13} and $\ell^{1}$-graph \cite{YanW09,ChengYYFH10}, are
effective in partitioning the data that lie in a union of subspaces. Most of
those methods use $\ell^{1}$-norm or $\ell^{2}$-norm with thresholding to
impose the sparsity ... | computer science |
8,315 | Batch-normalized Maxout Network in Network | cs.CV | This paper reports a novel deep architecture referred to as Maxout network In
Network (MIN), which can enhance model discriminability and facilitate the
process of information abstraction within the receptive field. The proposed
network adopts the framework of the recently developed Network In Network
structure, which ... | computer science |
8,316 | Generating Images from Captions with Attention | cs.LG | Motivated by the recent progress in generative models, we introduce a model
that generates images from natural language descriptions. The proposed model
iteratively draws patches on a canvas, while attending to the relevant words in
the description. After training on Microsoft COCO, we compare our model with
several ba... | computer science |
8,317 | Symmetries and control in generative neural nets | cs.CV | We study generative nets which can control and modify observations, after
being trained on real-life datasets. In order to zoom-in on an object, some
spatial, color and other attributes are learned by classifiers in specialized
attention nets. In field-theoretical terms, these learned symmetry statistics
form the gauge... | computer science |
8,318 | Feature Learning based Deep Supervised Hashing with Pairwise Labels | cs.LG | Recent years have witnessed wide application of hashing for large-scale image
retrieval. However, most existing hashing methods are based on hand-crafted
features which might not be optimally compatible with the hashing procedure.
Recently, deep hashing methods have been proposed to perform simultaneous
feature learnin... | computer science |
8,319 | Efficient non-greedy optimization of decision trees | cs.LG | Decision trees and randomized forests are widely used in computer vision and
machine learning. Standard algorithms for decision tree induction optimize the
split functions one node at a time according to some splitting criteria. This
greedy procedure often leads to suboptimal trees. In this paper, we present an
algorit... | computer science |
8,320 | Action Recognition using Visual Attention | cs.LG | We propose a soft attention based model for the task of action recognition in
videos. We use multi-layered Recurrent Neural Networks (RNNs) with Long
Short-Term Memory (LSTM) units which are deep both spatially and temporally.
Our model learns to focus selectively on parts of the video frames and
classifies videos afte... | computer science |
8,321 | Adaptive Affinity Matrix for Unsupervised Metric Learning | cs.CV | Spectral clustering is one of the most popular clustering approaches with the
capability to handle some challenging clustering problems. Most spectral
clustering methods provide a nonlinear map from the data manifold to a
subspace. Only a little work focuses on the explicit linear map which can be
viewed as the unsuper... | computer science |
8,322 | Similarity-based Text Recognition by Deeply Supervised Siamese Network | cs.CV | In this paper, we propose a new text recognition model based on measuring the
visual similarity of text and predicting the content of unlabeled texts. First
a Siamese convolutional network is trained with deep supervision on a labeled
training dataset. This network projects texts into a similarity manifold. The
Deeply ... | computer science |
8,323 | Deeply-Recursive Convolutional Network for Image Super-Resolution | cs.CV | We propose an image super-resolution method (SR) using a deeply-recursive
convolutional network (DRCN). Our network has a very deep recursive layer (up
to 16 recursions). Increasing recursion depth can improve performance without
introducing new parameters for additional convolutions. Albeit advantages,
learning a DRCN... | computer science |
8,324 | Accurate Image Super-Resolution Using Very Deep Convolutional Networks | cs.CV | We present a highly accurate single-image super-resolution (SR) method. Our
method uses a very deep convolutional network inspired by VGG-net used for
ImageNet classification \cite{simonyan2015very}. We find increasing our network
depth shows a significant improvement in accuracy. Our final model uses 20
weight layers.... | computer science |
8,325 | DeepFool: a simple and accurate method to fool deep neural networks | cs.LG | State-of-the-art deep neural networks have achieved impressive results on
many image classification tasks. However, these same architectures have been
shown to be unstable to small, well sought, perturbations of the images.
Despite the importance of this phenomenon, no effective methods have been
proposed to accurately... | computer science |
8,326 | Performing Highly Accurate Predictions Through Convolutional Networks
for Actual Telecommunication Challenges | cs.LG | We investigated how the application of deep learning, specifically the use of
convolutional networks trained with GPUs, can help to build better predictive
models in telecommunication business environments, and fill this gap. In
particular, we focus on the non-trivial problem of predicting customer churn in
telecommuni... | computer science |
8,327 | Learning Structured Inference Neural Networks with Label Relations | cs.CV | Images of scenes have various objects as well as abundant attributes, and
diverse levels of visual categorization are possible. A natural image could be
assigned with fine-grained labels that describe major components,
coarse-grained labels that depict high level abstraction or a set of labels
that reveal attributes. S... | computer science |
8,328 | Predicting distributions with Linearizing Belief Networks | cs.LG | Conditional belief networks introduce stochastic binary variables in neural
networks. Contrary to a classical neural network, a belief network can predict
more than the expected value of the output $Y$ given the input $X$. It can
predict a distribution of outputs $Y$ which is useful when an input can admit
multiple out... | computer science |
8,329 | What Objective Does Self-paced Learning Indeed Optimize? | cs.LG | Self-paced learning (SPL) is a recently raised methodology designed through
simulating the learning principle of humans/animals. A variety of SPL
realization schemes have been designed for different computer vision and
pattern recognition tasks, and empirically substantiated to be effective in
these applications. Howev... | computer science |
8,330 | Principled Parallel Mean-Field Inference for Discrete Random Fields | cs.CV | Mean-field variational inference is one of the most popular approaches to
inference in discrete random fields. Standard mean-field optimization is based
on coordinate descent and in many situations can be impractical. Thus, in
practice, various parallel techniques are used, which either rely on ad-hoc
smoothing with he... | computer science |
8,331 | Coreset-Based Adaptive Tracking | cs.CV | We propose a method for learning from streaming visual data using a compact,
constant size representation of all the data that was seen until a given
moment. Specifically, we construct a 'coreset' representation of streaming data
using a parallelized algorithm, which is an approximation of a set with
relation to the sq... | computer science |
8,332 | Towards Open Set Deep Networks | cs.CV | Deep networks have produced significant gains for various visual recognition
problems, leading to high impact academic and commercial applications. Recent
work in deep networks highlighted that it is easy to generate images that
humans would never classify as a particular object class, yet networks classify
such images... | computer science |
8,333 | Convolutional Clustering for Unsupervised Learning | cs.LG | The task of labeling data for training deep neural networks is daunting and
tedious, requiring millions of labels to achieve the current state-of-the-art
results. Such reliance on large amounts of labeled data can be relaxed by
exploiting hierarchical features via unsupervised learning techniques. In this
work, we prop... | computer science |
8,334 | Faster method for Deep Belief Network based Object classification using
DWT | cs.CV | A Deep Belief Network (DBN) requires large, multiple hidden layers with high
number of hidden units to learn good features from the raw pixels of large
images. This implies more training time as well as computational complexity. By
integrating DBN with Discrete Wavelet Transform (DWT), both training time and
computatio... | computer science |
8,335 | Density Modeling of Images using a Generalized Normalization
Transformation | cs.LG | We introduce a parametric nonlinear transformation that is well-suited for
Gaussianizing data from natural images. The data are linearly transformed, and
each component is then normalized by a pooled activity measure, computed by
exponentiating a weighted sum of rectified and exponentiated components and a
constant. We... | computer science |
8,336 | Foveation-based Mechanisms Alleviate Adversarial Examples | cs.LG | We show that adversarial examples, i.e., the visually imperceptible
perturbations that result in Convolutional Neural Networks (CNNs) fail, can be
alleviated with a mechanism based on foveations---applying the CNN in different
image regions. To see this, first, we report results in ImageNet that lead to a
revision of t... | computer science |
8,337 | Robust Convolutional Neural Networks under Adversarial Noise | cs.LG | Recent studies have shown that Convolutional Neural Networks (CNNs) are
vulnerable to a small perturbation of input called "adversarial examples". In
this work, we propose a new feedforward CNN that improves robustness in the
presence of adversarial noise. Our model uses stochastic additive noise added
to the input ima... | computer science |
8,338 | Spatio-temporal video autoencoder with differentiable memory | cs.LG | We describe a new spatio-temporal video autoencoder, based on a classic
spatial image autoencoder and a novel nested temporal autoencoder. The temporal
encoder is represented by a differentiable visual memory composed of
convolutional long short-term memory (LSTM) cells that integrate changes over
time. Here we target ... | computer science |
8,339 | Unsupervised Deep Embedding for Clustering Analysis | cs.LG | Clustering is central to many data-driven application domains and has been
studied extensively in terms of distance functions and grouping algorithms.
Relatively little work has focused on learning representations for clustering.
In this paper, we propose Deep Embedded Clustering (DEC), a method that
simultaneously lea... | computer science |
8,340 | FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning
and Applications | cs.LG | Features based on sparse representation, especially using the synthesis
dictionary model, have been heavily exploited in signal processing and computer
vision. However, synthesis dictionary learning typically involves NP-hard
sparse coding and expensive learning steps. Recently, sparsifying transform
learning received ... | computer science |
8,341 | Efficient inference in occlusion-aware generative models of images | cs.LG | We present a generative model of images based on layering, in which image
layers are individually generated, then composited from front to back. We are
thus able to factor the appearance of an image into the appearance of
individual objects within the image --- and additionally for each individual
object, we can factor... | computer science |
8,342 | Manifold Regularized Deep Neural Networks using Adversarial Examples | cs.LG | Learning meaningful representations using deep neural networks involves
designing efficient training schemes and well-structured networks. Currently,
the method of stochastic gradient descent that has a momentum with dropout is
one of the most popular training protocols. Based on that, more advanced
methods (i.e., Maxo... | computer science |
8,343 | Geodesics of learned representations | cs.CV | We develop a new method for visualizing and refining the invariances of
learned representations. Specifically, we test for a general form of
invariance, linearization, in which the action of a transformation is confined
to a low-dimensional subspace. Given two reference images (typically, differing
by some transformati... | computer science |
8,344 | Learning to Generate Images with Perceptual Similarity Metrics | cs.LG | Deep networks are increasingly being applied to problems involving image
synthesis, e.g., generating images from textual descriptions and reconstructing
an input image from a compact representation. Supervised training of
image-synthesis networks typically uses a pixel-wise loss (PL) to indicate the
mismatch between a ... | computer science |
8,345 | QBDC: Query by dropout committee for training deep supervised
architecture | cs.LG | While the current trend is to increase the depth of neural networks to
increase their performance, the size of their training database has to grow
accordingly. We notice an emergence of tremendous databases, although providing
labels to build a training set still remains a very expensive task. We tackle
the problem of ... | computer science |
8,346 | First Step toward Model-Free, Anonymous Object Tracking with Recurrent
Neural Networks | cs.CV | In this paper, we propose and study a novel visual object tracking approach
based on convolutional networks and recurrent networks. The proposed approach
is distinct from the existing approaches to visual object tracking, such as
filtering-based ones and tracking-by-detection ones, in the sense that the
tracking system... | computer science |
8,347 | A Controller-Recognizer Framework: How necessary is recognition for
control? | cs.LG | Recently there has been growing interest in building active visual object
recognizers, as opposed to the usual passive recognizers which classifies a
given static image into a predefined set of object categories. In this paper we
propose to generalize these recently proposed end-to-end active visual
recognizers into a ... | computer science |
8,348 | Unsupervised Representation Learning with Deep Convolutional Generative
Adversarial Networks | cs.LG | In recent years, supervised learning with convolutional networks (CNNs) has
seen huge adoption in computer vision applications. Comparatively, unsupervised
learning with CNNs has received less attention. In this work we hope to help
bridge the gap between the success of CNNs for supervised learning and
unsupervised lea... | computer science |
8,349 | A convnet for non-maximum suppression | cs.CV | Non-maximum suppression (NMS) is used in virtually all state-of-the-art
object detection pipelines. While essential object detection ingredients such
as features, classifiers, and proposal methods have been extensively researched
surprisingly little work has aimed to systematically address NMS. The de-facto
standard fo... | computer science |
8,350 | Learning Representations from EEG with Deep Recurrent-Convolutional
Neural Networks | cs.LG | One of the challenges in modeling cognitive events from electroencephalogram
(EEG) data is finding representations that are invariant to inter- and
intra-subject differences, as well as to inherent noise associated with such
data. Herein, we propose a novel approach for learning such representations
from multi-channel ... | computer science |
8,351 | Learning to decompose for object detection and instance segmentation | cs.CV | Although deep convolutional neural networks(CNNs) have achieved remarkable
results on object detection and segmentation, pre- and post-processing steps
such as region proposals and non-maximum suppression(NMS), have been required.
These steps result in high computational complexity and sensitivity to
hyperparameters, e... | computer science |
8,352 | Deep Metric Learning via Lifted Structured Feature Embedding | cs.CV | Learning the distance metric between pairs of examples is of great importance
for learning and visual recognition. With the remarkable success from the state
of the art convolutional neural networks, recent works have shown promising
results on discriminatively training the networks to learn semantic feature
embeddings... | computer science |
8,353 | DOC: Deep OCclusion Estimation From a Single Image | cs.CV | Recovering the occlusion relationships between objects is a fundamental human
visual ability which yields important information about the 3D world. In this
paper we propose a deep network architecture, called DOC, which acts on a
single image, detects object boundaries and estimates the border ownership
(i.e. which sid... | computer science |
8,354 | Integrating Deep Features for Material Recognition | cs.CV | We propose a method for integration of features extracted using deep
representations of Convolutional Neural Networks (CNNs) each of which is
learned using a different image dataset of objects and materials for material
recognition. Given a set of representations of multiple pre-trained CNNs, we
first compute activatio... | computer science |
8,355 | Compression of Deep Convolutional Neural Networks for Fast and Low Power
Mobile Applications | cs.CV | Although the latest high-end smartphone has powerful CPU and GPU, running
deeper convolutional neural networks (CNNs) for complex tasks such as ImageNet
classification on mobile devices is challenging. To deploy deep CNNs on mobile
devices, we present a simple and effective scheme to compress the entire CNN,
which we c... | computer science |
8,356 | Recurrent Semi-supervised Classification and Constrained Adversarial
Generation with Motion Capture Data | cs.CV | We explore recurrent encoder multi-decoder neural network architectures for
semi-supervised sequence classification and reconstruction. We find that the
use of multiple reconstruction modules helps models generalize in a
classification task when only a small amount of labeled data is available. Our
classification exper... | computer science |
8,357 | Images Don't Lie: Transferring Deep Visual Semantic Features to
Large-Scale Multimodal Learning to Rank | cs.CV | Search is at the heart of modern e-commerce. As a result, the task of ranking
search results automatically (learning to rank) is a multibillion dollar
machine learning problem. Traditional models optimize over a few
hand-constructed features based on the item's text. In this paper, we introduce
a multimodal learning to... | computer science |
8,358 | Learning visual groups from co-occurrences in space and time | cs.LG | We propose a self-supervised framework that learns to group visual entities
based on their rate of co-occurrence in space and time. To model statistical
dependencies between the entities, we set up a simple binary classification
problem in which the goal is to predict if two visual primitives occur in the
same spatial ... | computer science |
8,359 | Unsupervised learning of object semantic parts from internal states of
CNNs by population encoding | cs.LG | We address the key question of how object part representations can be found
from the internal states of CNNs that are trained for high-level tasks, such as
object classification. This work provides a new unsupervised method to learn
semantic parts and gives new understanding of the internal representations of
CNNs. Our... | computer science |
8,360 | Data-dependent Initializations of Convolutional Neural Networks | cs.CV | Convolutional Neural Networks spread through computer vision like a wildfire,
impacting almost all visual tasks imaginable. Despite this, few researchers
dare to train their models from scratch. Most work builds on one of a handful
of ImageNet pre-trained models, and fine-tunes or adapts these for specific
tasks. This ... | computer science |
8,361 | Zoom Better to See Clearer: Human and Object Parsing with Hierarchical
Auto-Zoom Net | cs.CV | Parsing articulated objects, e.g. humans and animals, into semantic parts
(e.g. body, head and arms, etc.) from natural images is a challenging and
fundamental problem for computer vision. A big difficulty is the large
variability of scale and location for objects and their corresponding parts.
Even limited mistakes in... | computer science |
8,362 | End-to-end Learning of Action Detection from Frame Glimpses in Videos | cs.CV | In this work we introduce a fully end-to-end approach for action detection in
videos that learns to directly predict the temporal bounds of actions. Our
intuition is that the process of detecting actions is naturally one of
observation and refinement: observing moments in video, and refining hypotheses
about when an ac... | computer science |
8,363 | ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation | cs.CV | We propose a structured prediction architecture, which exploits the local
generic features extracted by Convolutional Neural Networks and the capacity of
Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed
architecture, called ReSeg, is based on the recently introduced ReNet model for
image c... | computer science |
8,364 | NetVLAD: CNN architecture for weakly supervised place recognition | cs.CV | We tackle the problem of large scale visual place recognition, where the task
is to quickly and accurately recognize the location of a given query
photograph. We present the following three principal contributions. First, we
develop a convolutional neural network (CNN) architecture that is trainable in
an end-to-end ma... | computer science |
8,365 | Pushing the Boundaries of Boundary Detection using Deep Learning | cs.CV | In this work we show that adapting Deep Convolutional Neural Network training
to the task of boundary detection can result in substantial improvements over
the current state-of-the-art in boundary detection.
Our contributions consist firstly in combining a careful design of the loss
for boundary detection training, a... | computer science |
8,366 | Top-Down Learning for Structured Labeling with Convolutional Pseudoprior | cs.CV | Current practice in convolutional neural networks (CNN) remains largely
bottom-up and the role of top-down process in CNN for pattern analysis and
visual inference is not very clear. In this paper, we propose a new method for
structured labeling by developing convolutional pseudo-prior (ConvPP) on the
ground-truth labe... | computer science |
8,367 | Constrained Structured Regression with Convolutional Neural Networks | cs.CV | Convolutional Neural Networks (CNNs) have recently emerged as the dominant
model in computer vision. If provided with enough training data, they predict
almost any visual quantity. In a discrete setting, such as classification, CNNs
are not only able to predict a label but often predict a confidence in the form
of a pr... | computer science |
8,368 | DenseCap: Fully Convolutional Localization Networks for Dense Captioning | cs.CV | We introduce the dense captioning task, which requires a computer vision
system to both localize and describe salient regions in images in natural
language. The dense captioning task generalizes object detection when the
descriptions consist of a single word, and Image Captioning when one predicted
region covers the fu... | computer science |
8,369 | Context-aware CNNs for person head detection | cs.CV | Person detection is a key problem for many computer vision tasks. While face
detection has reached maturity, detecting people under a full variation of
camera view-points, human poses, lighting conditions and occlusions is still a
difficult challenge. In this work we focus on detecting human heads in natural
scenes. St... | computer science |
8,370 | Learning to detect video events from zero or very few video examples | cs.LG | In this work we deal with the problem of high-level event detection in video.
Specifically, we study the challenging problems of i) learning to detect video
events from solely a textual description of the event, without using any
positive video examples, and ii) additionally exploiting very few positive
training sample... | computer science |
8,371 | Iterative Instance Segmentation | cs.CV | Existing methods for pixel-wise labelling tasks generally disregard the
underlying structure of labellings, often leading to predictions that are
visually implausible. While incorporating structure into the model should
improve prediction quality, doing so is challenging - manually specifying the
form of structural con... | computer science |
8,372 | MidRank: Learning to rank based on subsequences | cs.CV | We present a supervised learning to rank algorithm that effectively orders
images by exploiting the structure in image sequences. Most often in the
supervised learning to rank literature, ranking is approached either by
analyzing pairs of images or by optimizing a list-wise surrogate loss function
on full sequences. In... | computer science |
8,373 | Approximated and User Steerable tSNE for Progressive Visual Analytics | cs.CV | Progressive Visual Analytics aims at improving the interactivity in existing
analytics techniques by means of visualization as well as interaction with
intermediate results. One key method for data analysis is dimensionality
reduction, for example, to produce 2D embeddings that can be visualized and
analyzed efficientl... | computer science |
8,374 | Blockout: Dynamic Model Selection for Hierarchical Deep Networks | cs.CV | Most deep architectures for image classification--even those that are trained
to classify a large number of diverse categories--learn shared image
representations with a single model. Intuitively, however, categories that are
more similar should share more information than those that are very different.
While hierarchi... | computer science |
8,375 | Relay Backpropagation for Effective Learning of Deep Convolutional
Neural Networks | cs.CV | Learning deeper convolutional neural networks becomes a tendency in recent
years. However, many empirical evidences suggest that performance improvement
cannot be gained by simply stacking more layers. In this paper, we consider the
issue from an information theoretical perspective, and propose a novel method
Relay Bac... | computer science |
8,376 | Kernel principal component analysis network for image classification | cs.LG | In order to classify the nonlinear feature with linear classifier and improve
the classification accuracy, a deep learning network named kernel principal
component analysis network (KPCANet) is proposed. First, mapping the data into
higher space with kernel principal component analysis to make the data linearly
separab... | computer science |
8,377 | Low-Rank Representation over the Manifold of Curves | cs.CV | In machine learning it is common to interpret each data point as a vector in
Euclidean space. However the data may actually be functional i.e.\ each data
point is a function of some variable such as time and the function is
discretely sampled. The naive treatment of functional data as traditional
multivariate data can ... | computer science |
8,378 | Creativity in Machine Learning | cs.CV | Recent machine learning techniques can be modified to produce creative
results. Those results did not exist before; it is not a trivial combination of
the data which was fed into the machine learning system. The obtained results
come in multiple forms: As images, as text and as audio.
This paper gives a high level ov... | computer science |
8,379 | A Theory of Local Matching: SIFT and Beyond | cs.CV | Why has SIFT been so successful? Why its extension, DSP-SIFT, can further
improve SIFT? Is there a theory that can explain both? How can such theory
benefit real applications? Can it suggest new algorithms with reduced
computational complexity or new descriptors with better accuracy for matching?
We construct a general... | computer science |
8,380 | When is Clustering Perturbation Robust? | cs.LG | Clustering is a fundamental data mining tool that aims to divide data into
groups of similar items. Generally, intuition about clustering reflects the
ideal case -- exact data sets endowed with flawless dissimilarity between
individual instances.
In practice however, these cases are in the minority, and clustering
ap... | computer science |
8,381 | Survey on the attention based RNN model and its applications in computer
vision | cs.CV | The recurrent neural networks (RNN) can be used to solve the sequence to
sequence problem, where both the input and the output have sequential
structures. Usually there are some implicit relations between the structures.
However, it is hard for the common RNN model to fully explore the relations
between the sequences. ... | computer science |
8,382 | Unsupervised Deep Hashing for Large-scale Visual Search | cs.CV | Learning based hashing plays a pivotal role in large-scale visual search.
However, most existing hashing algorithms tend to learn shallow models that do
not seek representative binary codes. In this paper, we propose a novel hashing
approach based on unsupervised deep learning to hierarchically transform
features into ... | computer science |
8,383 | Homogeneity of Cluster Ensembles | cs.LG | The expectation and the mean of partitions generated by a cluster ensemble
are not unique in general. This issue poses challenges in statistical inference
and cluster stability. In this contribution, we state sufficient conditions for
uniqueness of expectation and mean. The proposed conditions show that a unique
mean i... | computer science |
8,384 | Semi-supervised Learning with Explicit Relationship Regularization | cs.CV | In many learning tasks, the structure of the target space of a function holds
rich information about the relationships between evaluations of functions on
different data points. Existing approaches attempt to exploit this relationship
information implicitly by enforcing smoothness on function evaluations only.
However,... | computer science |
8,385 | Wavelet-Based Semantic Features for Hyperspectral Signature
Discrimination | cs.CV | Hyperspectral signature classification is a quantitative analysis approach
for hyperspectral imagery which performs detection and classification of the
constituent materials at the pixel level in the scene. The classification
procedure can be operated directly on hyperspectral data or performed by using
some features e... | computer science |
8,386 | Convolutional Radio Modulation Recognition Networks | cs.LG | We study the adaptation of convolutional neural networks to the complex
temporal radio signal domain. We compare the efficacy of radio modulation
classification using naively learned features against using expert features
which are widely used in the field today and we show significant performance
improvements. We show... | computer science |
8,387 | Convolutional Tables Ensemble: classification in microseconds | cs.CV | We study classifiers operating under severe classification time constraints,
corresponding to 1-1000 CPU microseconds, using Convolutional Tables Ensemble
(CTE), an inherently fast architecture for object category recognition. The
architecture is based on convolutionally-applied sparse feature extraction,
using trees o... | computer science |
8,388 | Generating images with recurrent adversarial networks | cs.LG | Gatys et al. (2015) showed that optimizing pixels to match features in a
convolutional network with respect reference image features is a way to render
images of high visual quality. We show that unrolling this gradient-based
optimization yields a recurrent computation that creates images by
incrementally adding onto a... | computer science |
8,389 | Cell segmentation with random ferns and graph-cuts | cs.CV | The progress in imaging techniques have allowed the study of various aspect
of cellular mechanisms. To isolate individual cells in live imaging data, we
introduce an elegant image segmentation framework that effectively extracts
cell boundaries, even in the presence of poor edge details. Our approach works
in two stage... | computer science |
8,390 | Boost Picking: A Universal Method on Converting Supervised
Classification to Semi-supervised Classification | cs.CV | This paper proposes a universal method, Boost Picking, to train supervised
classification models mainly by un-labeled data. Boost Picking only adopts two
weak classifiers to estimate and correct the error. It is theoretically proved
that Boost Picking could train a supervised model mainly by un-labeled data as
effectiv... | computer science |
8,391 | A survey of sparse representation: algorithms and applications | cs.CV | Sparse representation has attracted much attention from researchers in fields
of signal processing, image processing, computer vision and pattern
recognition. Sparse representation also has a good reputation in both
theoretical research and practical applications. Many different algorithms have
been proposed for sparse... | computer science |
8,392 | Learning to Generate with Memory | cs.LG | Memory units have been widely used to enrich the capabilities of deep
networks on capturing long-term dependencies in reasoning and prediction tasks,
but little investigation exists on deep generative models (DGMs) which are good
at inferring high-level invariant representations from unlabeled data. This
paper presents... | computer science |
8,393 | Auto-JacoBin: Auto-encoder Jacobian Binary Hashing | cs.CV | Binary codes can be used to speed up nearest neighbor search tasks in large
scale data sets as they are efficient for both storage and retrieval. In this
paper, we propose a robust auto-encoder model that preserves the geometric
relationships of high-dimensional data sets in Hamming space. This is done by
considering a... | computer science |
8,394 | Shallow and Deep Convolutional Networks for Saliency Prediction | cs.CV | The prediction of salient areas in images has been traditionally addressed
with hand-crafted features based on neuroscience principles. This paper,
however, addresses the problem with a completely data-driven approach by
training a convolutional neural network (convnet). The learning process is
formulated as a minimiza... | computer science |
8,395 | Gaussian Process Regression for Out-of-Sample Extension | cs.LG | Manifold learning methods are useful for high dimensional data analysis. Many
of the existing methods produce a low dimensional representation that attempts
to describe the intrinsic geometric structure of the original data. Typically,
this process is computationally expensive and the produced embedding is limited
to t... | computer science |
8,396 | Visual Concept Recognition and Localization via Iterative Introspection | cs.CV | Convolutional neural networks have been shown to develop internal
representations, which correspond closely to semantically meaningful objects
and parts, although trained solely on class labels. Class Activation Mapping
(CAM) is a recent method that makes it possible to easily highlight the image
regions contributing t... | computer science |
8,397 | Object Contour Detection with a Fully Convolutional Encoder-Decoder
Network | cs.CV | We develop a deep learning algorithm for contour detection with a fully
convolutional encoder-decoder network. Different from previous low-level edge
detection, our algorithm focuses on detecting higher-level object contours. Our
network is trained end-to-end on PASCAL VOC with refined ground truth from
inaccurate poly... | computer science |
8,398 | Revisiting Batch Normalization For Practical Domain Adaptation | cs.CV | Deep neural networks (DNN) have shown unprecedented success in various
computer vision applications such as image classification and object detection.
However, it is still a common annoyance during the training phase, that one has
to prepare at least thousands of labeled images to fine-tune a network to a
specific doma... | computer science |
8,399 | Identity Mappings in Deep Residual Networks | cs.CV | Deep residual networks have emerged as a family of extremely deep
architectures showing compelling accuracy and nice convergence behaviors. In
this paper, we analyze the propagation formulations behind the residual
building blocks, which suggest that the forward and backward signals can be
directly propagated from one ... | computer science |
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