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12,701 | Decreasing Weighted Sorted $\ell_1$ Regularization | cs.CV | We consider a new family of regularizers, termed {\it weighted sorted
$\ell_1$ norms} (WSL1), which generalizes the recently introduced {\it
octagonal shrinkage and clustering algorithm for regression} (OSCAR) and also
contains the $\ell_1$ and $\ell_{\infty}$ norms as particular instances. We
focus on a special case o... | computer science |
12,702 | Sparse Bilinear Logistic Regression | math.OC | In this paper, we introduce the concept of sparse bilinear logistic
regression for decision problems involving explanatory variables that are
two-dimensional matrices. Such problems are common in computer vision,
brain-computer interfaces, style/content factorization, and parallel factor
analysis. The underlying optimi... | computer science |
12,703 | Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference | cs.CV | We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF
inference problems. The core of our method is a very efficient bounding
procedure, which combines scalable semidefinite programming (SDP) and a
cutting-plane method for seeking violated constraints. In order to further
speed up the computation, severa... | computer science |
12,704 | A theoretical contribution to the fast implementation of null linear
discriminant analysis method using random matrix multiplication with scatter
matrices | cs.NA | The null linear discriminant analysis method is a competitive approach for
dimensionality reduction. The implementation of this method, however, is
computationally expensive. Recently, a fast implementation of null linear
discriminant analysis method using random matrix multiplication with scatter
matrices was proposed... | computer science |
12,705 | Combining human and machine learning for morphological analysis of
galaxy images | cs.CV | The increasing importance of digital sky surveys collecting many millions of
galaxy images has reinforced the need for robust methods that can perform
morphological analysis of large galaxy image databases. Citizen science
initiatives such as Galaxy Zoo showed that large datasets of galaxy images can
be analyzed effect... | computer science |
12,706 | Vision and Learning for Deliberative Monocular Cluttered Flight | cs.RO | Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomo... | computer science |
12,707 | Generalized Singular Value Thresholding | cs.CV | This work studies the Generalized Singular Value Thresholding (GSVT) operator
${\Prox}_{g}^{\bm{\sigma}}(\cdot)$, \begin{equation*}
{\Prox}_{g}^{\bm{\sigma}}(\B)=\arg\min\limits_{\X}\sum_{i=1}^{m}g(\sigma_{i}(\X))
+ \frac{1}{2}||\X-\B||_{F}^{2}, \end{equation*} associated with a nonconvex
function $g$ defined on the ... | computer science |
12,708 | Web image annotation by diffusion maps manifold learning algorithm | cs.CV | Automatic image annotation is one of the most challenging problems in machine
vision areas. The goal of this task is to predict number of keywords
automatically for images captured in real data. Many methods are based on
visual features in order to calculate similarities between image samples. But
the computation cost ... | computer science |
12,709 | Automatic Photo Adjustment Using Deep Neural Networks | cs.CV | Photo retouching enables photographers to invoke dramatic visual impressions
by artistically enhancing their photos through stylistic color and tone
adjustments. However, it is also a time-consuming and challenging task that
requires advanced skills beyond the abilities of casual photographers. Using an
automated algor... | computer science |
12,710 | ModDrop: adaptive multi-modal gesture recognition | cs.CV | We present a method for gesture detection and localisation based on
multi-scale and multi-modal deep learning. Each visual modality captures
spatial information at a particular spatial scale (such as motion of the upper
body or a hand), and the whole system operates at three temporal scales. Key to
our technique is a t... | computer science |
12,711 | HOG based Fast Human Detection | cs.RO | Objects recognition in image is one of the most difficult problems in
computer vision. It is also an important step for the implementation of several
existing applications that require high-level image interpretation. Therefore,
there is a growing interest in this research area during the last years. In
this paper, we ... | computer science |
12,712 | Feature Selection based on Machine Learning in MRIs for Hippocampal
Segmentation | cs.CV | Neurodegenerative diseases are frequently associated with structural changes
in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations
and therefore be used as a supportive feature for a number of neurodegenerative
diseases. The hippocampus has been known to be a biomarker for Alzheimer
disease and... | computer science |
12,713 | Quantum image classification using principal component analysis | cs.CV | We present a novel quantum algorithm for classification of images. The
algorithm is constructed using principal component analysis and von Neuman
quantum measurements. In order to apply the algorithm we present a new quantum
representation of grayscale images. | computer science |
12,714 | End-to-End Training of Deep Visuomotor Policies | cs.LG | Policy search methods can allow robots to learn control policies for a wide
range of tasks, but practical applications of policy search often require
hand-engineered components for perception, state estimation, and low-level
control. In this paper, we aim to answer the following question: does training
the perception a... | computer science |
12,715 | Robust Anomaly Detection Using Semidefinite Programming | math.OC | This paper presents a new approach, based on polynomial optimization and the
method of moments, to the problem of anomaly detection. The proposed technique
only requires information about the statistical moments of the normal-state
distribution of the features of interest and compares favorably with existing
approaches... | computer science |
12,716 | Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How
Many Objects can iCub Learn? | cs.RO | The ability to visually recognize objects is a fundamental skill for robotics
systems. Indeed, a large variety of tasks involving manipulation, navigation or
interaction with other agents, deeply depends on the accurate understanding of
the visual scene. Yet, at the time being, robots are lacking good visual
perceptual... | computer science |
12,717 | Pose Estimation Based on 3D Models | cs.CV | In this paper, we proposed a pose estimation system based on rendered image
training set, which predicts the pose of objects in real image, with knowledge
of object category and tight bounding box. We developed a patch-based
multi-class classification algorithm, and an iterative approach to improve the
accuracy. We ach... | computer science |
12,718 | Modality-dependent Cross-media Retrieval | cs.CV | In this paper, we investigate the cross-media retrieval between images and
text, i.e., using image to search text (I2T) and using text to search images
(T2I). Existing cross-media retrieval methods usually learn one couple of
projections, by which the original features of images and text can be projected
into a common ... | computer science |
12,719 | Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty | cs.LG | We propose new methods to speed up convergence of the Alternating Direction
Method of Multipliers (ADMM), a common optimization tool in the context of
large scale and distributed learning. The proposed method accelerates the speed
of convergence by automatically deciding the constraint penalty needed for
parameter cons... | computer science |
12,720 | Closed Curves and Elementary Visual Object Identification | cs.CV | For two closed curves on a plane (discrete version) and local criteria for
similarity of points on the curves one gets a potential, which describes the
similarity between curve points. This is the base for a global similarity
measure of closed curves (Fr\'echet distance). I use borderlines of handwritten
digits to demo... | computer science |
12,721 | Deep Multimodal Speaker Naming | cs.CV | Automatic speaker naming is the problem of localizing as well as identifying
each speaking character in a TV/movie/live show video. This is a challenging
problem mainly attributes to its multimodal nature, namely face cue alone is
insufficient to achieve good performance. Previous multimodal approaches to
this problem ... | computer science |
12,722 | A novel multivariate performance optimization method based on sparse
coding and hyper-predictor learning | cs.LG | In this paper, we investigate the problem of optimization multivariate
performance measures, and propose a novel algorithm for it. Different from
traditional machine learning methods which optimize simple loss functions to
learn prediction function, the problem studied in this paper is how to learn
effective hyper-pred... | computer science |
12,723 | Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze
Classification | cs.CV | Accurate, robust, inexpensive gaze tracking in the car can help keep a driver
safe by facilitating the more effective study of how to improve (1) vehicle
interfaces and (2) the design of future Advanced Driver Assistance Systems. In
this paper, we estimate head pose and eye pose from monocular video using
methods devel... | computer science |
12,724 | Robust Image Sentiment Analysis Using Progressively Trained and Domain
Transferred Deep Networks | cs.CV | Sentiment analysis of online user generated content is important for many
social media analytics tasks. Researchers have largely relied on textual
sentiment analysis to develop systems to predict political elections, measure
economic indicators, and so on. Recently, social media users are increasingly
using images and ... | computer science |
12,725 | Deep Spatial Autoencoders for Visuomotor Learning | cs.LG | Reinforcement learning provides a powerful and flexible framework for
automated acquisition of robotic motion skills. However, applying reinforcement
learning requires a sufficiently detailed representation of the state,
including the configuration of task-relevant objects. We present an approach
that automates state-s... | computer science |
12,726 | Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700
Robot Hours | cs.LG | Current learning-based robot grasping approaches exploit human-labeled
datasets for training the models. However, there are two problems with such a
methodology: (a) since each object can be grasped in multiple ways, manually
labeling grasp locations is not a trivial task; (b) human labeling is biased by
semantics. Whi... | computer science |
12,727 | Modeling Curiosity in a Mobile Robot for Long-Term Autonomous
Exploration and Monitoring | cs.RO | This paper presents a novel approach to modeling curiosity in a mobile robot,
which is useful for monitoring and adaptive data collection tasks, especially
in the context of long term autonomous missions where pre-programmed missions
are likely to have limited utility. We use a realtime topic modeling technique
to buil... | computer science |
12,728 | Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted
Nuclear Norm | cs.LG | The nuclear norm is widely used as a convex surrogate of the rank function in
compressive sensing for low rank matrix recovery with its applications in image
recovery and signal processing. However, solving the nuclear norm based relaxed
convex problem usually leads to a suboptimal solution of the original rank
minimiz... | computer science |
12,729 | Enhanced Low-Rank Matrix Approximation | cs.CV | This letter proposes to estimate low-rank matrices by formulating a convex
optimization problem with non-convex regularization. We employ parameterized
non-convex penalty functions to estimate the non-zero singular values more
accurately than the nuclear norm. A closed-form solution for the global optimum
of the propos... | computer science |
12,730 | Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing | cs.IR | A typical image retrieval pipeline starts with the comparison of global
descriptors from a large database to find a short list of candidate matches. A
good image descriptor is key to the retrieval pipeline and should reconcile two
contradictory requirements: providing recall rates as high as possible and
being as compa... | computer science |
12,731 | Towards Vision-Based Deep Reinforcement Learning for Robotic Motion
Control | cs.LG | This paper introduces a machine learning based system for controlling a
robotic manipulator with visual perception only. The capability to autonomously
learn robot controllers solely from raw-pixel images and without any prior
knowledge of configuration is shown for the first time. We build upon the
success of recent d... | computer science |
12,732 | Deep Learning for Tactile Understanding From Visual and Haptic Data | cs.RO | Robots which interact with the physical world will benefit from a
fine-grained tactile understanding of objects and surfaces. Additionally, for
certain tasks, robots may need to know the haptic properties of an object
before touching it. To enable better tactile understanding for robots, we
propose a method of classify... | computer science |
12,733 | Picking a Conveyor Clean by an Autonomously Learning Robot | cs.RO | We present a research picking prototype related to our company's industrial
waste sorting application. The goal of the prototype is to be as autonomous as
possible and it both calibrates itself and improves its picking with minimal
human intervention. The system learns to pick objects better based on a
feedback sensor ... | computer science |
12,734 | Fast Optimization Algorithm on Riemannian Manifolds and Its Application
in Low-Rank Representation | cs.NA | The paper addresses the problem of optimizing a class of composite functions
on Riemannian manifolds and a new first order optimization algorithm (FOA) with
a fast convergence rate is proposed. Through the theoretical analysis for FOA,
it has been proved that the algorithm has quadratic convergence. The
experiments in ... | computer science |
12,735 | Window-Object Relationship Guided Representation Learning for Generic
Object Detections | cs.CV | In existing works that learn representation for object detection, the
relationship between a candidate window and the ground truth bounding box of an
object is simplified by thresholding their overlap. This paper shows
information loss in this simplification and picks up the relative location/size
information discarded... | computer science |
12,736 | Poseidon: A System Architecture for Efficient GPU-based Deep Learning on
Multiple Machines | cs.LG | Deep learning (DL) has achieved notable successes in many machine learning
tasks. A number of frameworks have been developed to expedite the process of
designing and training deep neural networks (DNNs), such as Caffe, Torch and
Theano. Currently they can harness multiple GPUs on a single machine, but are
unable to use... | computer science |
12,737 | Deep Learning for Surface Material Classification Using Haptic And
Visual Information | cs.RO | When a user scratches a hand-held rigid tool across an object surface, an
acceleration signal can be captured, which carries relevant information about
the surface. More importantly, such a haptic signal is complementary to the
visual appearance of the surface, which suggests the combination of both
modalities for the ... | computer science |
12,738 | Visually Indicated Sounds | cs.CV | Objects make distinctive sounds when they are hit or scratched. These sounds
reveal aspects of an object's material properties, as well as the actions that
produced them. In this paper, we propose the task of predicting what sound an
object makes when struck as a way of studying physical interactions within a
visual sc... | computer science |
12,739 | Denoising and Completion of 3D Data via Multidimensional Dictionary
Learning | cs.LG | In this paper a new dictionary learning algorithm for multidimensional data
is proposed. Unlike most conventional dictionary learning methods which are
derived for dealing with vectors or matrices, our algorithm, named KTSVD,
learns a multidimensional dictionary directly via a novel algebraic approach
for tensor factor... | computer science |
12,740 | Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep
Learning Architecture | cs.RO | Advanced Driver Assistance Systems (ADAS) have made driving safer over the
last decade. They prepare vehicles for unsafe road conditions and alert drivers
if they perform a dangerous maneuver. However, many accidents are unavoidable
because by the time drivers are alerted, it is already too late. Anticipating
maneuvers... | computer science |
12,741 | Fast Binary Embedding via Circulant Downsampled Matrix -- A
Data-Independent Approach | cs.IT | Binary embedding of high-dimensional data aims to produce low-dimensional
binary codes while preserving discriminative power. State-of-the-art methods
often suffer from high computation and storage costs. We present a simple and
fast embedding scheme by first downsampling N-dimensional data into
M-dimensional data and ... | computer science |
12,742 | A Taxonomy of Deep Convolutional Neural Nets for Computer Vision | cs.CV | Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, ... | computer science |
12,743 | Segmentation Rectification for Video Cutout via One-Class Structured
Learning | cs.CV | Recent works on interactive video object cutout mainly focus on designing
dynamic foreground-background (FB) classifiers for segmentation propagation.
However, the research on optimally removing errors from the FB classification
is sparse, and the errors often accumulate rapidly, causing significant errors
in the propa... | computer science |
12,744 | Weighted Unsupervised Learning for 3D Object Detection | cs.CV | This paper introduces a novel weighted unsupervised learning for object
detection using an RGB-D camera. This technique is feasible for detecting the
moving objects in the noisy environments that are captured by an RGB-D camera.
The main contribution of this paper is a real-time algorithm for detecting each
object usin... | computer science |
12,745 | Multimodal Emotion Recognition Using Multimodal Deep Learning | cs.HC | To enhance the performance of affective models and reduce the cost of
acquiring physiological signals for real-world applications, we adopt
multimodal deep learning approach to construct affective models from multiple
physiological signals. For unimodal enhancement task, we indicate that the best
recognition accuracy o... | computer science |
12,746 | Watch-n-Patch: Unsupervised Learning of Actions and Relations | cs.CV | There is a large variation in the activities that humans perform in their
everyday lives. We consider modeling these composite human activities which
comprises multiple basic level actions in a completely unsupervised setting.
Our model learns high-level co-occurrence and temporal relations between the
actions. We cons... | computer science |
12,747 | Deep Fully-Connected Networks for Video Compressive Sensing | cs.CV | In this work we present a deep learning framework for video compressive
sensing. The proposed formulation enables recovery of video frames in a few
seconds at significantly improved reconstruction quality compared to previous
approaches. Our investigation starts by learning a linear mapping between video
sequences and ... | computer science |
12,748 | Learning Compatibility Across Categories for Heterogeneous Item
Recommendation | cs.IR | Identifying relationships between items is a key task of an online
recommender system, in order to help users discover items that are functionally
complementary or visually compatible. In domains like clothing recommendation,
this task is particularly challenging since a successful system should be
capable of handling ... | computer science |
12,749 | All Weather Perception: Joint Data Association, Tracking, and
Classification for Autonomous Ground Vehicles | cs.SY | A novel probabilistic perception algorithm is presented as a real-time joint
solution to data association, object tracking, and object classification for an
autonomous ground vehicle in all-weather conditions. The presented algorithm
extends a Rao-Blackwellized Particle Filter originally built with a particle
filter fo... | computer science |
12,750 | CNN based texture synthesize with Semantic segment | cs.CV | Deep learning algorithm display powerful ability in Computer Vision area, in
recent year, the CNN has been applied to solve problems in the subarea of
Image-generating, which has been widely applied in areas such as photo editing,
image design, computer animation, real-time rendering for large scale of scenes
and for v... | computer science |
12,751 | cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey | cs.CV | The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki
Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers
on computer vision, pattern recognition, and related fields. For this
particular review, we focused on reading the ALL 602 conference papers
presented at the CVPR2... | computer science |
12,752 | Robust Deep-Learning-Based Road-Prediction for Augmented Reality
Navigation Systems | cs.CV | This paper proposes an approach that predicts the road course from camera
sensors leveraging deep learning techniques. Road pixels are identified by
training a multi-scale convolutional neural network on a large number of
full-scene-labeled night-time road images including adverse weather conditions.
A framework is pre... | computer science |
12,753 | Unsupervised Feature Learning Based on Deep Models for Environmental
Audio Tagging | cs.SD | Environmental audio tagging aims to predict only the presence or absence of
certain acoustic events in the interested acoustic scene. In this paper we make
contributions to audio tagging in two parts, respectively, acoustic modeling
and feature learning. We propose to use a shrinking deep neural network (DNN)
framework... | computer science |
12,754 | Hierarchical learning for DNN-based acoustic scene classification | cs.SD | In this paper, we present a deep neural network (DNN)-based acoustic scene
classification framework. Two hierarchical learning methods are proposed to
improve the DNN baseline performance by incorporating the hierarchical taxonomy
information of environmental sounds. Firstly, the parameters of the DNN are
initialized b... | computer science |
12,755 | Concatenated image completion via tensor augmentation and completion | cs.LG | This paper proposes a novel framework called concatenated image completion
via tensor augmentation and completion (ICTAC), which recovers missing entries
of color images with high accuracy. Typical images are second- or third-order
tensors (2D/3D) depending if they are grayscale or color, hence tensor
completion algori... | computer science |
12,756 | Spoofing 2D Face Detection: Machines See People Who Aren't There | cs.CR | Machine learning is increasingly used to make sense of the physical world yet
may suffer from adversarial manipulation. We examine the Viola-Jones 2D face
detection algorithm to study whether images can be created that humans do not
notice as faces yet the algorithm detects as faces. We show that it is possible
to cons... | computer science |
12,757 | Deep Learning a Grasp Function for Grasping under Gripper Pose
Uncertainty | cs.RO | This paper presents a new method for parallel-jaw grasping of isolated
objects from depth images, under large gripper pose uncertainty. Whilst most
approaches aim to predict the single best grasp pose from an image, our method
first predicts a score for every possible grasp pose, which we denote the grasp
function. Wit... | computer science |
12,758 | Anomaly detection and classification for streaming data using PDEs | cs.LG | Nondominated sorting, also called Pareto Depth Analysis (PDA), is widely used
in multi-objective optimization and has recently found important applications
in multi-criteria anomaly detection. Recently, a partial differential equation
(PDE) continuum limit was discovered for nondominated sorting leading to a very
fast ... | computer science |
12,759 | An image compression and encryption scheme based on deep learning | cs.CV | Stacked Auto-Encoder (SAE) is a kind of deep learning algorithm for
unsupervised learning. Which has multi layers that project the vector
representation of input data into a lower vector space. These projection
vectors are dense representations of the input data. As a result, SAE can be
used for image compression. Usin... | computer science |
12,760 | Classifying and sorting cluttered piles of unknown objects with robots:
a learning approach | cs.RO | We consider the problem of sorting a densely cluttered pile of unknown
objects using a robot. This yet unsolved problem is relevant in the robotic
waste sorting business.
By extending previous active learning approaches to grasping, we show a
system that learns the task autonomously. Instead of predicting just whethe... | computer science |
12,761 | Human Body Orientation Estimation using Convolutional Neural Network | cs.RO | Personal robots are expected to interact with the user by recognizing the
user's face. However, in most of the service robot applications, the user needs
to move himself/herself to allow the robot to see him/her face to face. To
overcome such limitations, a method for estimating human body orientation is
required. Prev... | computer science |
12,762 | Generating Videos with Scene Dynamics | cs.CV | We capitalize on large amounts of unlabeled video in order to learn a model
of scene dynamics for both video recognition tasks (e.g. action classification)
and video generation tasks (e.g. future prediction). We propose a generative
adversarial network for video with a spatio-temporal convolutional architecture
that un... | computer science |
12,763 | 3D Simulation for Robot Arm Control with Deep Q-Learning | cs.RO | Recent trends in robot arm control have seen a shift towards end-to-end
solutions, using deep reinforcement learning to learn a controller directly
from raw sensor data, rather than relying on a hand-crafted, modular pipeline.
However, the high dimensionality of the state space often means that it is
impractical to gen... | computer science |
12,764 | A Perspective on Deep Imaging | cs.CV | The combination of tomographic imaging and deep learning, or machine learning
in general, promises to empower not only image analysis but also image
reconstruction. The latter aspect is considered in this perspective article
with an emphasis on medical imaging to develop a new generation of image
reconstruction theorie... | computer science |
12,765 | Large Margin Nearest Neighbor Classification using Curved Mahalanobis
Distances | cs.LG | We consider the supervised classification problem of machine learning in
Cayley-Klein projective geometries: We show how to learn a curved Mahalanobis
metric distance corresponding to either the hyperbolic geometry or the elliptic
geometry using the Large Margin Nearest Neighbor (LMNN) framework. We report on
our exper... | computer science |
12,766 | Learning to Push by Grasping: Using multiple tasks for effective
learning | cs.RO | Recently, end-to-end learning frameworks are gaining prevalence in the field
of robot control. These frameworks input states/images and directly predict the
torques or the action parameters. However, these approaches are often critiqued
due to their huge data requirements for learning a task. The argument of the
diffic... | computer science |
12,767 | Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the
Amazon Picking Challenge | cs.CV | Robot warehouse automation has attracted significant interest in recent
years, perhaps most visibly in the Amazon Picking Challenge (APC). A fully
autonomous warehouse pick-and-place system requires robust vision that reliably
recognizes and locates objects amid cluttered environments, self-occlusions,
sensor noise, an... | computer science |
12,768 | Multi-dimensional signal approximation with sparse structured priors
using split Bregman iterations | cs.DS | This paper addresses the structurally-constrained sparse decomposition of
multi-dimensional signals onto overcomplete families of vectors, called
dictionaries. The contribution of the paper is threefold. Firstly, a generic
spatio-temporal regularization term is designed and used together with the
standard $\ell_1$ regu... | computer science |
12,769 | Multi-View Representation Learning: A Survey from Shallow Methods to
Deep Methods | cs.LG | Recently, multi-view representation learning has become a rapidly growing
direction in machine learning and data mining areas. This paper introduces
several principles for multi-view representation learning: correlation,
consensus, and complementarity principles. Consequently, we first review the
representative methods... | computer science |
12,770 | Supervision via Competition: Robot Adversaries for Learning Tasks | cs.RO | There has been a recent paradigm shift in robotics to data-driven learning
for planning and control. Due to large number of experiences required for
training, most of these approaches use a self-supervised paradigm: using
sensors to measure success/failure. However, in most cases, these sensors
provide weak supervision... | computer science |
12,771 | Distributed Averaging CNN-ELM for Big Data | cs.LG | Increasing the scalability of machine learning to handle big volume of data
is a challenging task. The scale up approach has some limitations. In this
paper, we proposed a scale out approach for CNN-ELM based on MapReduce on
classifier level. Map process is the CNN-ELM training for certain partition of
data. It involve... | computer science |
12,772 | Boost K-Means | cs.LG | Due to its simplicity and versatility, k-means remains popular since it was
proposed three decades ago. The performance of k-means has been enhanced from
different perspectives over the years. Unfortunately, a good trade-off between
quality and efficiency is hardly reached. In this paper, a novel k-means
variant is pre... | computer science |
12,773 | Image Segmentation for Fruit Detection and Yield Estimation in Apple
Orchards | cs.RO | Ground vehicles equipped with monocular vision systems are a valuable source
of high resolution image data for precision agriculture applications in
orchards. This paper presents an image processing framework for fruit detection
and counting using orchard image data. A general purpose image segmentation
approach is use... | computer science |
12,774 | SoundNet: Learning Sound Representations from Unlabeled Video | cs.CV | We learn rich natural sound representations by capitalizing on large amounts
of unlabeled sound data collected in the wild. We leverage the natural
synchronization between vision and sound to learn an acoustic representation
using two-million unlabeled videos. Unlabeled video has the advantage that it
can be economical... | computer science |
12,775 | Cross-Modal Scene Networks | cs.CV | People can recognize scenes across many different modalities beyond natural
images. In this paper, we investigate how to learn cross-modal scene
representations that transfer across modalities. To study this problem, we
introduce a new cross-modal scene dataset. While convolutional neural networks
can categorize scenes... | computer science |
12,776 | Discovering containment: from infants to machines | cs.CV | Current artificial learning systems can recognize thousands of visual
categories, or play Go at a champion"s level, but cannot explain infants
learning, in particular the ability to learn complex concepts without guidance,
in a specific order. A notable example is the category of 'containers' and the
notion of containm... | computer science |
12,777 | CAD2RL: Real Single-Image Flight without a Single Real Image | cs.LG | Deep reinforcement learning has emerged as a promising and powerful technique
for automatically acquiring control policies that can process raw sensory
inputs, such as images, and perform complex behaviors. However, extending deep
RL to real-world robotic tasks has proven challenging, particularly in
safety-critical do... | computer science |
12,778 | Audio Event and Scene Recognition: A Unified Approach using Strongly and
Weakly Labeled Data | cs.LG | In this paper we propose a novel learning framework called Supervised and
Weakly Supervised Learning where the goal is to learn simultaneously from
weakly and strongly labeled data. Strongly labeled data can be simply
understood as fully supervised data where all labeled instances are available.
In weakly supervised le... | computer science |
12,779 | Deep Learning for the Classification of Lung Nodules | cs.CV | Deep learning, as a promising new area of machine learning, has attracted a
rapidly increasing attention in the field of medical imaging. Compared to the
conventional machine learning methods, deep learning requires no hand-tuned
feature extractor, and has shown a superior performance in many visual object
recognition ... | computer science |
12,780 | What Can Be Predicted from Six Seconds of Driver Glances? | cs.CV | We consider a large dataset of real-world, on-road driving from a 100-car
naturalistic study to explore the predictive power of driver glances and,
specifically, to answer the following question: what can be predicted about the
state of the driver and the state of the driving environment from a 6-second
sequence of mac... | computer science |
12,781 | Fast Supervised Discrete Hashing and its Analysis | cs.CV | In this paper, we propose a learning-based supervised discrete hashing
method. Binary hashing is widely used for large-scale image retrieval as well
as video and document searches because the compact representation of binary
code is essential for data storage and reasonable for query searches using
bit-operations. The ... | computer science |
12,782 | On Hölder projective divergences | cs.LG | We describe a framework to build distances by measuring the tightness of
inequalities, and introduce the notion of proper statistical divergences and
improper pseudo-divergences. We then consider the H\"older ordinary and reverse
inequalities, and present two novel classes of H\"older divergences and
pseudo-divergences... | computer science |
12,783 | Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications
Using HyperMapper | cs.CV | In this paper we investigate an emerging application, 3D scene understanding,
likely to be significant in the mobile space in the near future. The goal of
this exploration is to reduce execution time while meeting our quality of
result objectives. In previous work we showed for the first time that it is
possible to map... | computer science |
12,784 | Video Frame Synthesis using Deep Voxel Flow | cs.CV | We address the problem of synthesizing new video frames in an existing video,
either in-between existing frames (interpolation), or subsequent to them
(extrapolation). This problem is challenging because video appearance and
motion can be highly complex. Traditional optical-flow-based solutions often
fail where flow es... | computer science |
12,785 | Developing a comprehensive framework for multimodal feature extraction | cs.CV | Feature extraction is a critical component of many applied data science
workflows. In recent years, rapid advances in artificial intelligence and
machine learning have led to an explosion of feature extraction tools and
services that allow data scientists to cheaply and effectively annotate their
data along a vast arra... | computer science |
12,786 | A GPU-Outperforming FPGA Accelerator Architecture for Binary
Convolutional Neural Networks | cs.DC | FPGA-based hardware accelerators for convolutional neural networks (CNNs)
have obtained great attentions due to their higher energy efficiency than GPUs.
However, it is challenging for FPGA-based solutions to achieve a higher
throughput than GPU counterparts. In this paper, we demonstrate that FPGA
acceleration can be ... | computer science |
12,787 | PixelNet: Representation of the pixels, by the pixels, and for the
pixels | cs.CV | We explore design principles for general pixel-level prediction problems,
from low-level edge detection to mid-level surface normal estimation to
high-level semantic segmentation. Convolutional predictors, such as the
fully-convolutional network (FCN), have achieved remarkable success by
exploiting the spatial redundan... | computer science |
12,788 | CHAOS: A Parallelization Scheme for Training Convolutional Neural
Networks on Intel Xeon Phi | cs.DC | Deep learning is an important component of big-data analytic tools and
intelligent applications, such as, self-driving cars, computer vision, speech
recognition, or precision medicine. However, the training process is
computationally intensive, and often requires a large amount of time if
performed sequentially. Modern... | computer science |
12,789 | Learning Deep Visual Object Models From Noisy Web Data: How to Make it
Work | cs.CV | Deep networks thrive when trained on large scale data collections. This has
given ImageNet a central role in the development of deep architectures for
visual object classification. However, ImageNet was created during a specific
period in time, and as such it is prone to aging, as well as dataset bias
issues. Moving be... | computer science |
12,790 | Combining Self-Supervised Learning and Imitation for Vision-Based Rope
Manipulation | cs.CV | Manipulation of deformable objects, such as ropes and cloth, is an important
but challenging problem in robotics. We present a learning-based system where a
robot takes as input a sequence of images of a human manipulating a rope from
an initial to goal configuration, and outputs a sequence of actions that can
reproduc... | computer science |
12,791 | PathTrack: Fast Trajectory Annotation with Path Supervision | cs.CV | Progress in Multiple Object Tracking (MOT) has been historically limited by
the size of the available datasets. We present an efficient framework to
annotate trajectories and use it to produce a MOT dataset of unprecedented
size. In our novel path supervision the annotator loosely follows the object
with the cursor whi... | computer science |
12,792 | Content-based similar document image retrieval using fusion of CNN
features | cs.CV | Rapid increase of digitized document give birth to high demand of document
image retrieval. While conventional document image retrieval approaches depend
on complex OCR-based text recognition and text similarity detection, this paper
proposes a new content-based approach, in which more attention is paid to
features ext... | computer science |
12,793 | Feature Fusion using Extended Jaccard Graph and Stochastic Gradient
Descent for Robot | cs.CV | Robot vision is a fundamental device for human-robot interaction and robot
complex tasks. In this paper, we use Kinect and propose a feature graph fusion
(FGF) for robot recognition. Our feature fusion utilizes RGB and depth
information to construct fused feature from Kinect. FGF involves multi-Jaccard
similarity to co... | computer science |
12,794 | Hidden Two-Stream Convolutional Networks for Action Recognition | cs.CV | Analyzing videos of human actions involves understanding the temporal
relationships among video frames. CNNs are the current state-of-the-art methods
for action recognition in videos. However, the CNN architectures currently
being used have difficulty in capturing these relationships. State-of-the-art
action recognitio... | computer science |
12,795 | Satellite Image-based Localization via Learned Embeddings | cs.RO | We propose a vision-based method that localizes a ground vehicle using
publicly available satellite imagery as the only prior knowledge of the
environment. Our approach takes as input a sequence of ground-level images
acquired by the vehicle as it navigates, and outputs an estimate of the
vehicle's pose relative to a g... | computer science |
12,796 | Feature Squeezing: Detecting Adversarial Examples in Deep Neural
Networks | cs.CV | Although deep neural networks (DNNs) have achieved great success in many
tasks, they can often be fooled by \emph{adversarial examples} that are
generated by adding small but purposeful distortions to natural examples.
Previous studies to defend against adversarial examples mostly focused on
refining the DNN models, bu... | computer science |
12,797 | Learning to Fly by Crashing | cs.RO | How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? One approach is to use a small dataset collected by human experts:
however, high capacity learning algorithms tend to overfit when trained with
little data. An alternative is to use simulation. But the gap between
simulation and real wor... | computer science |
12,798 | Deep Learning in the Automotive Industry: Applications and Tools | cs.LG | Deep Learning refers to a set of machine learning techniques that utilize
neural networks with many hidden layers for tasks, such as image
classification, speech recognition, language understanding. Deep learning has
been proven to be very effective in these domains and is pervasively used by
many Internet services. In... | computer science |
12,799 | Deep Learning Microscopy | cs.LG | We demonstrate that a deep neural network can significantly improve optical
microscopy, enhancing its spatial resolution over a large field-of-view and
depth-of-field. After its training, the only input to this network is an image
acquired using a regular optical microscope, without any changes to its design.
We blindl... | computer science |
12,800 | Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection
Methods | cs.LG | Neural networks are known to be vulnerable to adversarial examples: inputs
that are close to natural inputs but classified incorrectly. In order to better
understand the space of adversarial examples, we survey ten recent proposals
that are designed for detection and compare their efficacy. We show that all
can be defe... | computer science |
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