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4,200 | Scene Text Eraser | cs.CV | The character information in natural scene images contains various personal
information, such as telephone numbers, home addresses, etc. It is a high risk
of leakage the information if they are published. In this paper, we proposed a
scene text erasing method to properly hide the information via an inpainting
convoluti... | computer science |
4,201 | Person Re-Identification by Deep Joint Learning of Multi-Loss
Classification | cs.CV | Existing person re-identification (re-id) methods rely mostly on either
localised or global feature representation alone. This ignores their joint
benefit and mutual complementary effects. In this work, we show the advantages
of jointly learning local and global features in a Convolutional Neural Network
(CNN) by aimin... | computer science |
4,202 | Kernel Truncated Regression Representation for Robust Subspace
Clustering | cs.CV | Subspace clustering aims to group data points into multiple clusters of which
each corresponds to one subspace. Most existing subspace clustering methods
assume that the data could be linearly represented with each other in the input
space. In practice, however, this assumption is hard to be satisfied. To
achieve nonli... | computer science |
4,203 | Learning to Represent Mechanics via Long-term Extrapolation and
Interpolation | cs.CV | While the basic laws of Newtonian mechanics are well understood, explaining a
physical scenario still requires manually modeling the problem with suitable
equations and associated parameters. In order to adopt such models for
artificial intelligence, researchers have handcrafted the relevant states, and
then used neura... | computer science |
4,204 | Revisiting Unreasonable Effectiveness of Data in Deep Learning Era | cs.CV | The success of deep learning in vision can be attributed to: (a) models with
high capacity; (b) increased computational power; and (c) availability of
large-scale labeled data. Since 2012, there have been significant advances in
representation capabilities of the models and computational capabilities of
GPUs. But the s... | computer science |
4,205 | Detection, Recognition and Tracking of Moving Objects from Real-time
Video via Visual Vocabulary Model and Species Inspired PSO | cs.CV | In this paper, we address the basic problem of recognizing moving objects in
video images using Visual Vocabulary model and Bag of Words and track our
object of interest in the subsequent video frames using species inspired PSO.
Initially, the shadow free images are obtained by background modelling followed
by foregrou... | computer science |
4,206 | Object Tracking based on Quantum Particle Swarm Optimization | cs.CV | In Computer Vision domain, moving Object Tracking considered as one of the
toughest problem.As there so many factors associated like illumination of
light, noise, occlusion, sudden start and stop of moving object, shading which
makes tracking even harder problem not only for dynamic background but also for
static backg... | computer science |
4,207 | An All-in-One Network for Dehazing and Beyond | cs.CV | This paper proposes an image dehazing model built with a convolutional neural
network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed
based on a re-formulated atmospheric scattering model. Instead of estimating
the transmission matrix and the atmospheric light separately as most previous
models did,... | computer science |
4,208 | Virtual PET Images from CT Data Using Deep Convolutional Networks:
Initial Results | cs.CV | In this work we present a novel system for PET estimation using CT scans. We
explore the use of fully convolutional networks (FCN) and conditional
generative adversarial networks (GAN) to export PET data from CT data. Our
dataset includes 25 pairs of PET and CT scans where 17 were used for training
and 8 for testing. T... | computer science |
4,209 | Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos | cs.CV | Despite rapid advances in face recognition, there remains a clear gap between
the performance of still image-based face recognition and video-based face
recognition, due to the vast difference in visual quality between the domains
and the difficulty of curating diverse large-scale video datasets. This paper
addresses b... | computer science |
4,210 | Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual
Cross Retrieval | cs.CV | Cross-modal hashing is usually regarded as an effective technique for
large-scale textual-visual cross retrieval, where data from different
modalities are mapped into a shared Hamming space for matching. Most of the
traditional textual-visual binary encoding methods only consider holistic image
representations and fail... | computer science |
4,211 | An automatic water detection approach based on Dempster-Shafer theory
for multi spectral images | cs.AI | Detection of surface water in natural environment via multi-spectral imagery
has been widely utilized in many fields, such land cover identification.
However, due to the similarity of the spectra of water bodies, built-up areas,
approaches based on high-resolution satellites sometimes confuse these
features. A popular ... | computer science |
4,212 | Systematic Testing of Convolutional Neural Networks for Autonomous
Driving | cs.CV | We present a framework to systematically analyze convolutional neural
networks (CNNs) used in classification of cars in autonomous vehicles. Our
analysis procedure comprises an image generator that produces synthetic
pictures by sampling in a lower dimension image modification subspace and a
suite of visualization tool... | computer science |
4,213 | Belief Tree Search for Active Object Recognition | cs.AI | Active Object Recognition (AOR) has been approached as an unsupervised
learning problem, in which optimal trajectories for object inspection are not
known and are to be discovered by reducing label uncertainty measures or
training with reinforcement learning. Such approaches have no guarantees of the
quality of their s... | computer science |
4,214 | Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks | cs.AI | Recurrent Neural Networks (RNNs) continue to show outstanding performance in
sequence modeling tasks. However, training RNNs on long sequences often face
challenges like slow inference, vanishing gradients and difficulty in capturing
long term dependencies. In backpropagation through time settings, these issues
are tig... | computer science |
4,215 | Non-linear Convolution Filters for CNN-based Learning | cs.CV | During the last years, Convolutional Neural Networks (CNNs) have achieved
state-of-the-art performance in image classification. Their architectures have
largely drawn inspiration by models of the primate visual system. However,
while recent research results of neuroscience prove the existence of non-linear
operations i... | computer science |
4,216 | Deep Belief Networks used on High Resolution Multichannel
Electroencephalography Data for Seizure Detection | cs.CV | Ubiquitous bio-sensing for personalized health monitoring is slowly becoming
a reality with the increasing availability of small, diverse, robust, high
fidelity sensors. This oncoming flood of data begs the question of how we will
extract useful information from it. In this paper we explore the use of a
variety of repr... | computer science |
4,217 | A Deep Learning Approach for Population Estimation from Satellite
Imagery | cs.AI | Knowing where people live is a fundamental component of many decision making
processes such as urban development, infectious disease containment, evacuation
planning, risk management, conservation planning, and more. While bottom-up,
survey driven censuses can provide a comprehensive view into the population
landscape ... | computer science |
4,218 | CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced
Annotations | cs.CV | This paper introduces a novel activity dataset which exhibits real-life and
diverse scenarios of complex, temporally-extended human activities and actions.
The dataset presents a set of videos of actors performing everyday activities
in a natural and unscripted manner. The dataset was recorded using a static
Kinect 2 s... | computer science |
4,219 | Embedding Deep Networks into Visual Explanations | cs.CV | In this paper, we propose a novel explanation module to explain the
predictions made by deep learning. Explanation modules work by embedding a
high-dimensional deep network layer nonlinearly into a low-dimensional
explanation space, while retaining faithfulness in that the original deep
learning predictions can be cons... | computer science |
4,220 | Scene-centric Joint Parsing of Cross-view Videos | cs.CV | Cross-view video understanding is an important yet under-explored area in
computer vision. In this paper, we introduce a joint parsing framework that
integrates view-centric proposals into scene-centric parse graphs that
represent a coherent scene-centric understanding of cross-view scenes. Our key
observations are tha... | computer science |
4,221 | A Causal And-Or Graph Model for Visibility Fluent Reasoning in
Human-Object Interactions | cs.CV | Tracking humans that are interacting with the other subjects or environment
remains unsolved in visual tracking, because the visibility of the human of
interests in videos is unknown and might vary over times. In particular, it is
still difficult for state-of-the-art human trackers to recover complete human
trajectorie... | computer science |
4,222 | Direction-Aware Semi-Dense SLAM | cs.CV | To aide simultaneous localization and mapping (SLAM), future perception
systems will incorporate forms of scene understanding. In a step towards fully
integrated probabilistic geometric scene understanding, localization and
mapping we propose the first direction-aware semi-dense SLAM system. It jointly
infers the direc... | computer science |
4,223 | Open Source Dataset and Deep Learning Models for Online Digit Gesture
Recognition on Touchscreens | cs.CV | This paper presents an evaluation of deep neural networks for recognition of
digits entered by users on a smartphone touchscreen. A new large dataset of
Arabic numerals was collected for training and evaluation of the network. The
dataset consists of spatial and temporal touch data recorded for 80 digits
entered by 260... | computer science |
4,224 | Semi-Supervised Hierarchical Semantic Object Parsing | cs.AI | Models based on Convolutional Neural Networks (CNNs) have been proven very
successful for semantic segmentation and object parsing that yield hierarchies
of features. Our key insight is to build convolutional networks that take input
of arbitrary size and produce object parsing output with efficient inference
and learn... | computer science |
4,225 | Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF | cs.CV | Event learning is one of the most important problems in AI. However,
notwithstanding significant research efforts, it is still a very complex task,
especially when the events involve the interaction of humans or agents with
other objects, as it requires modeling human kinematics and object movements.
This study propose... | computer science |
4,226 | Learning event representation: As sparse as possible, but not sparser | cs.CV | Selecting an optimal event representation is essential for event
classification in real world contexts. In this paper, we investigate the
application of qualitative spatial reasoning (QSR) frameworks for
classification of human-object interaction in three dimensional space, in
comparison with the use of quantitative fe... | computer science |
4,227 | Learning Pose Grammar to Encode Human Body Configuration for 3D Pose
Estimation | cs.CV | In this paper, we propose a pose grammar to tackle the problem of 3D human
pose estimation. Our model directly takes 2D pose as input and learns a
generalized 2D-3D mapping function. The proposed model consists of a base
network which efficiently captures pose-aligned features and a hierarchy of
Bi-directional RNNs (BR... | computer science |
4,228 | Classification Driven Dynamic Image Enhancement | cs.CV | Convolutional neural networks rely on image texture and structure to serve as
discriminative features to classify the image content. Image enhancement
techniques can be used as preprocessing steps to help improve the overall image
quality and in turn improve the overall effectiveness of a CNN. Existing image
enhancemen... | computer science |
4,229 | Investigating the feature collection for semantic segmentation via
single skip connection | cs.CV | Since the study of deep convolutional neural network became prevalent, one of
the important discoveries is that a feature map from a convolutional network
can be extracted before going into the fully connected layer and can be used as
a saliency map for object detection. Furthermore, the model can use features
from eac... | computer science |
4,230 | Neural Stain-Style Transfer Learning using GAN for Histopathological
Images | cs.CV | Performance of data-driven network for tumor classification varies with
stain-style of histopathological images. This article proposes the stain-style
transfer (SST) model based on conditional generative adversarial networks
(GANs) which is to learn not only the certain color distribution but also the
corresponding his... | computer science |
4,231 | Left-Right Skip-DenseNets for Coarse-to-Fine Object Categorization | cs.CV | Inspired by the recent neuroscience studies on the left-right asymmetry of
the brains in the low and high spatial frequency processing, we introduce a
novel type of network -- the left-right skip-densenets for coarse-to-fine
object categorization. This network can enable both coarse and fine-grained
classification in a... | computer science |
4,232 | Exploiting Points and Lines in Regression Forests for RGB-D Camera
Relocalization | cs.CV | Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure and loop
closure detection. Recent random forests based methods exploit randomly sampled
pixel comparison features to predict 3D world locations for 2D image locations
to guid... | computer science |
4,233 | SIMILARnet: Simultaneous Intelligent Localization and Recognition
Network | cs.CV | Global Average Pooling (GAP) [4] has been used previously to generate class
activation for image classification tasks. The motivation behind SIMILARnet
comes from the fact that the convolutional filters possess position information
of the essential features and hence, combination of the feature maps could help
us locat... | computer science |
4,234 | Saliency Prediction for Mobile User Interfaces | cs.CV | We introduce models for saliency prediction for mobile user interfaces. A
mobile interface may include elements like buttons, text, etc. in addition to
natural images which enable performing a variety of tasks. Saliency in natural
images is a well studied area. However, given the difference in what
constitutes a mobile... | computer science |
4,235 | Arrhythmia Classification from the Abductive Interpretation of Short
Single-Lead ECG Records | cs.AI | In this work we propose a new method for the rhythm classification of short
single-lead ECG records, using a set of high-level and clinically meaningful
features provided by the abductive interpretation of the records. These
features include morphological and rhythm-related features that are used to
build two classifie... | computer science |
4,236 | Saliency-based Sequential Image Attention with Multiset Prediction | cs.CV | Humans process visual scenes selectively and sequentially using attention.
Central to models of human visual attention is the saliency map. We propose a
hierarchical visual architecture that operates on a saliency map and uses a
novel attention mechanism to sequentially focus on salient regions and take
additional glim... | computer science |
4,237 | Fast Predictive Simple Geodesic Regression | cs.CV | Deformable image registration and regression are important tasks in medical
image analysis. However, they are computationally expensive, especially when
analyzing large-scale datasets that contain thousands of images. Hence, cluster
computing is typically used, making the approaches dependent on such
computational infr... | computer science |
4,238 | End-to-end 3D shape inverse rendering of different classes of objects
from a single input image | cs.CV | In this paper a semi-supervised deep framework is proposed for the problem of
3D shape inverse rendering from a single 2D input image. The main structure of
proposed framework consists of unsupervised pre-trained components which
significantly reduce the need to labeled data for training the whole framework.
using labe... | computer science |
4,239 | 3D Reconstruction of Incomplete Archaeological Objects Using a
Generative Adversarial Network | cs.CV | We introduce a data-driven approach to aid the repairing and conservation of
archaeological objects: ORGAN, an object reconstruction generative adversarial
network (GAN). By using an encoder-decoder 3D deep neural network on a GAN
architecture, and combining two loss objectives: a completion loss and an
Improved Wasser... | computer science |
4,240 | Dependent landmark drift: robust point set registration based on the
Gaussian mixture model with a statistical shape model | cs.AI | The goal of point set registration is to find point-by-point correspondences
between point sets, each of which characterizes the shape of an object. Because
local preservation of object geometry is assumed, prevalent algorithms in the
area can often elegantly solve the problems without using geometric information
speci... | computer science |
4,241 | Parameter Reference Loss for Unsupervised Domain Adaptation | cs.CV | The success of deep learning in computer vision is mainly attributed to an
abundance of data. However, collecting large-scale data is not always possible,
especially for the supervised labels. Unsupervised domain adaptation (UDA) aims
to utilize labeled data from a source domain to learn a model that generalizes
to a t... | computer science |
4,242 | A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry | cs.CV | Demographic studies suggest that changes in the retinal vasculature geometry,
especially in vessel width, are associated with the incidence or progression of
eye-related or systemic diseases. To date, the main information source for
width estimation from fundus images has been the intensity profile between
vessel edges... | computer science |
4,243 | Improving Smiling Detection with Race and Gender Diversity | cs.CV | Recent progress in deep learning has been accompanied by a growing concern
for whether models are fair for users, with equally good performance across
different demographics. In computer vision research, such questions are
relevant to face detection and the related task of face attribute detection,
among others. We mea... | computer science |
4,244 | Discriminant Projection Representation-based Classification for Vision
Recognition | cs.CV | Representation-based classification methods such as sparse
representation-based classification (SRC) and linear regression classification
(LRC) have attracted a lot of attentions. In order to obtain the better
representation, a novel method called projection representation-based
classification (PRC) is proposed for ima... | computer science |
4,245 | Dilated FCN for Multi-Agent 2D/3D Medical Image Registration | cs.CV | 2D/3D image registration to align a 3D volume and 2D X-ray images is a
challenging problem due to its ill-posed nature and various artifacts presented
in 2D X-ray images. In this paper, we propose a multi-agent system with an auto
attention mechanism for robust and efficient 2D/3D image registration.
Specifically, an i... | computer science |
4,246 | A Novel Brain Decoding Method: a Correlation Network Framework for
Revealing Brain Connections | cs.CV | Brain decoding is a hot spot in cognitive science, which focuses on
reconstructing perceptual images from brain activities. Analyzing the
correlations of collected data from human brain activities and representing
activity patterns are two problems in brain decoding based on functional
magnetic resonance imaging (fMRI)... | computer science |
4,247 | Investigating the Impact of Data Volume and Domain Similarity on
Transfer Learning Applications | cs.CV | Transfer Learning helps to build a system to recognize and apply knowledge
and experience learned in previous tasks (source task) to new tasks or new
domains (target task), which share some commonality. The two important factors
that impact the performance of transfer learning models are: (a) the size of
the target dat... | computer science |
4,248 | Deception Detection in Videos | cs.AI | We present a system for covert automated deception detection in real-life
courtroom trial videos. We study the importance of different modalities like
vision, audio and text for this task. On the vision side, our system uses
classifiers trained on low level video features which predict human
micro-expressions. We show ... | computer science |
4,249 | Learning Binary Residual Representations for Domain-specific Video
Streaming | cs.CV | We study domain-specific video streaming. Specifically, we target a streaming
setting where the videos to be streamed from a server to a client are all in
the same domain and they have to be compressed to a small size for low-latency
transmission. Several popular video streaming services, such as the video game
streami... | computer science |
4,250 | CSGNet: Neural Shape Parser for Constructive Solid Geometry | cs.CV | We present a neural architecture that takes as input a 2D or 3D shape and
induces a program to generate it. The in- structions in our program are based
on constructive solid geometry principles, i.e., a set of boolean operations on
shape primitives defined recursively. Bottom-up techniques for this task that
rely on pr... | computer science |
4,251 | SLAC: A Sparsely Labeled Dataset for Action Classification and
Localization | cs.CV | This paper describes a procedure for the creation of large-scale video
datasets for action classification and localization from unconstrained,
realistic web data. The scalability of the proposed procedure is demonstrated
by building a novel video benchmark, named SLAC (Sparsely Labeled ACtions),
consisting of over 520K... | computer science |
4,252 | Moments in Time Dataset: one million videos for event understanding | cs.CV | We present the Moments in Time Dataset, a large-scale human-annotated
collection of one million short videos corresponding to dynamic events
unfolding within three seconds. Modeling the spatial-audio-temporal dynamics
even for actions occurring in 3 second videos poses many challenges: meaningful
events do not include ... | computer science |
4,253 | Visualization of Hyperspectral Images Using Moving Least Squares | cs.CV | Displaying the large number of bands in a hyper spectral image on a
trichromatic monitor has been an active research topic. The visualized image
shall convey as much information as possible form the original data and
facilitate image interpretation. Most existing methods display HSIs in false
colors which contradict wi... | computer science |
4,254 | The Shape of Art History in the Eyes of the Machine | cs.AI | How does the machine classify styles in art? And how does it relate to art
historians' methods for analyzing style? Several studies have shown the ability
of the machine to learn and predict style categories, such as Renaissance,
Baroque, Impressionism, etc., from images of paintings. This implies that the
machine can ... | computer science |
4,255 | Game of Sketches: Deep Recurrent Models of Pictionary-style Word
Guessing | cs.CV | The ability of intelligent agents to play games in human-like fashion is
popularly considered a benchmark of progress in Artificial Intelligence.
Similarly, performance on multi-disciplinary tasks such as Visual Question
Answering (VQA) is considered a marker for gauging progress in Computer Vision.
In our work, we bri... | computer science |
4,256 | Pose Flow: Efficient Online Pose Tracking | cs.CV | Multi-person articulated pose tracking in complex unconstrained videos is an
important and challenging problem. In this paper, going along the road of
top-down approaches, we propose a decent and efficient pose tracker based on
pose flows. First, we design an online optimization framework to build
association of cross-... | computer science |
4,257 | FixaTons: A collection of Human Fixations Datasets and Metrics for
Scanpath Similarity | cs.AI | In the last three decades, human visual attention has been a topic of great
interest in various disciplines. In computer vision, many models have been
proposed to predict the distribution of human fixations on a visual input.
Recently, thanks to the creation of large collections of data, machine learning
algorithms hav... | computer science |
4,258 | PPFNet: Global Context Aware Local Features for Robust 3D Point Matching | cs.CV | We present PPFNet - Point Pair Feature NETwork for deeply learning a globally
informed 3D local feature descriptor to find correspondences in unorganized
point clouds. PPFNet learns local descriptors on pure geometry and is highly
aware of the global context, an important cue in deep learning. Our 3D
representation is ... | computer science |
4,259 | Slice Sampling Particle Belief Propagation | cs.CV | Inference in continuous label Markov random fields is a challenging task. We
use particle belief propagation (PBP) for solving the inference problem in
continuous label space. Sampling particles from the belief distribution is
typically done by using Metropolis-Hastings Markov chain Monte Carlo methods
which involves s... | computer science |
4,260 | Generative Adversarial Networks and Probabilistic Graph Models for
Hyperspectral Image Classification | cs.CV | High spectral dimensionality and the shortage of annotations make
hyperspectral image (HSI) classification a challenging problem. Recent studies
suggest that convolutional neural networks can learn discriminative spatial
features, which play a paramount role in HSI interpretation. However, most of
these methods ignore ... | computer science |
4,261 | Abductive reasoning as the basis to reproduce expert criteria in ECG
Atrial Fibrillation identification | cs.AI | Objective: This work aims at providing a new method for the automatic
detection of atrial fibrillation, other arrhythmia and noise on short single
lead ECG signals, emphasizing the importance of the interpretability of the
classification results.
Approach: A morphological and rhythm description of the cardiac behavio... | computer science |
4,262 | Exact and Consistent Interpretation for Piecewise Linear Neural
Networks: A Closed Form Solution | cs.CV | Strong intelligent machines powered by deep neural networks are increasingly
deployed as black boxes to make decisions in risk-sensitive domains, such as
finance and medical. To reduce potential risk and build trust with users, it is
critical to interpret how such machines make their decisions. Existing works
interpret... | computer science |
4,263 | Divide, Denoise, and Defend against Adversarial Attacks | cs.CV | Deep neural networks, although shown to be a successful class of machine
learning algorithms, are known to be extremely unstable to adversarial
perturbations. Improving the robustness of neural networks against these
attacks is important, especially for security-critical applications. To defend
against such attacks, we... | computer science |
4,264 | Scale Optimization for Full-Image-CNN Vehicle Detection | cs.CV | Many state-of-the-art general object detection methods make use of shared
full-image convolutional features (as in Faster R-CNN). This achieves a
reasonable test-phase computation time while enjoys the discriminative power
provided by large Convolutional Neural Network (CNN) models. Such designs excel
on benchmarks whi... | computer science |
4,265 | Density Weighted Connectivity of Grass Pixels in Image Frames for
Biomass Estimation | cs.CV | Accurate estimation of the biomass of roadside grasses plays a significant
role in applications such as fire-prone region identification. Current
solutions heavily depend on field surveys, remote sensing measurements and
image processing using reference markers, which often demand big investments of
time, effort and co... | computer science |
4,266 | Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for
Improved Automated Lesion Detection | cs.CV | In this work we present a novel system for generation of virtual PET images
using CT scans. We combine a fully convolutional network (FCN) with a
conditional generative adversarial network (GAN) to generate simulated PET data
from given input CT data. The synthesized PET can be used for false-positive
reduction in lesi... | computer science |
4,267 | Facial Expression Recognition Based on Complexity Perception
Classification Algorithm | cs.CV | Facial expression recognition (FER) has always been a challenging issue in
computer vision. The different expressions of emotion and uncontrolled
environmental factors lead to inconsistencies in the complexity of FER and
variability of between expression categories, which is often overlooked in most
facial expression r... | computer science |
4,268 | Tongue image constitution recognition based on Complexity Perception
method | cs.CV | Background and Object: In China, body constitution is highly related to
physiological and pathological functions of human body and determines the
tendency of the disease, which is of great importance for treatment in clinical
medicine. Tongue diagnosis, as a key part of Traditional Chinese Medicine
inspection, is an im... | computer science |
4,269 | Multi-Instance Dynamic Ordinal Random Fields for Weakly-supervised
Facial Behavior Analysis | cs.CV | We propose a Multi-Instance-Learning (MIL) approach for weakly-supervised
learning problems, where a training set is formed by bags (sets of feature
vectors or instances) and only labels at bag-level are provided. Specifically,
we consider the Multi-Instance Dynamic-Ordinal-Regression (MI-DOR) setting,
where the instan... | computer science |
4,270 | Cooperative Starting Movement Detection of Cyclists Using Convolutional
Neural Networks and a Boosted Stacking Ensemble | cs.CV | In future, vehicles and other traffic participants will be interconnected and
equipped with various types of sensors, allowing for cooperation on different
levels, such as situation prediction or intention detection. In this article we
present a cooperative approach for starting movement detection of cyclists
using a b... | computer science |
4,271 | Learning to recognize Abnormalities in Chest X-Rays with Location-Aware
Dense Networks | cs.CV | Chest X-ray is the most common medical imaging exam used to assess multiple
pathologies. Automated algorithms and tools have the potential to support the
reading workflow, improve efficiency, and reduce reading errors. With the
availability of large scale data sets, several methods have been proposed to
classify pathol... | computer science |
4,272 | Knowledge-based Recurrent Attentive Neural Network for Traffic Sign
Detection | cs.CV | Accurate Traffic Sign Detection (TSD) can help drivers make better decision
according to the traffic regulations. TSD, regarded as a typical small object
detection problem in some way, is fundamental in the field of self-driving and
advanced driver assistance systems. However, small object detection is still an
open qu... | computer science |
4,273 | IntPhys: A Framework and Benchmark for Visual Intuitive Physics
Reasoning | cs.AI | In order to reach human performance on complex visual tasks, artificial
systems need to incorporate a significant amount of understanding of the world
in terms of macroscopic objects, movements, forces, etc. Inspired by work on
intuitive physics in infants, we propose an evaluation framework which
diagnoses how much a ... | computer science |
4,274 | Evolutionary design of photometric systems and its application to Gaia | cs.NE | Designing a photometric system to best fulfil a set of scientific goals is a
complex task, demanding a compromise between conflicting requirements and
subject to various constraints. A specific example is the determination of
stellar astrophysical parameters (APs) - effective temperature, metallicity
etc. - across a wi... | computer science |
4,275 | Dimensionally Constrained Symbolic Regression | stat.ML | We describe dimensionally constrained symbolic regression which has been
developed for mass measurement in certain classes of events in high-energy
physics (HEP). With symbolic regression, we can derive equations that are well
known in HEP. However, in problems with large number of variables, we find that
by constraini... | computer science |
4,276 | An estimation of distribution algorithm with adaptive Gibbs sampling for
unconstrained global optimization | cs.NE | In this paper is proposed a new heuristic approach belonging to the field of
evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a
probability model and a set of solutions is sampled from the model which
characterizes the distribution of such solutions. The main framework of the
proposed method is an... | computer science |
4,277 | A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A
Derivation from Multidimensional Scaling of Streaming Data | cs.NE | Neural network models of early sensory processing typically reduce the
dimensionality of streaming input data. Such networks learn the principal
subspace, in the sense of principal component analysis (PCA), by adjusting
synaptic weights according to activity-dependent learning rules. When derived
from a principled cost... | computer science |
4,278 | A Hebbian/Anti-Hebbian Network Derived from Online Non-Negative Matrix
Factorization Can Cluster and Discover Sparse Features | cs.NE | Despite our extensive knowledge of biophysical properties of neurons, there
is no commonly accepted algorithmic theory of neuronal function. Here we
explore the hypothesis that single-layer neuronal networks perform online
symmetric nonnegative matrix factorization (SNMF) of the similarity matrix of
the streamed data. ... | computer science |
4,279 | A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning
Derived from Symmetric Matrix Factorization | cs.NE | Olshausen and Field (OF) proposed that neural computations in the primary
visual cortex (V1) can be partially modeled by sparse dictionary learning. By
minimizing the regularized representation error they derived an online
algorithm, which learns Gabor-filter receptive fields from a natural image
ensemble in agreement ... | computer science |
4,280 | A State Space Approach for Piecewise-Linear Recurrent Neural Networks
for Reconstructing Nonlinear Dynamics from Neural Measurements | cs.NE | The computational properties of neural systems are often thought to be
implemented in terms of their network dynamics. Hence, recovering the system
dynamics from experimentally observed neuronal time series, like multiple
single-unit (MSU) recordings or neuroimaging data, is an important step toward
understanding its c... | computer science |
4,281 | Unsupervised Learning through Prediction in a Model of Cortex | cs.NE | We propose a primitive called PJOIN, for "predictive join," which combines
and extends the operations JOIN and LINK, which Valiant proposed as the basis
of a computational theory of cortex. We show that PJOIN can be implemented in
Valiant's model. We also show that, using PJOIN, certain reasonably complex
learning and ... | computer science |
4,282 | Solving the G-problems in less than 500 iterations: Improved efficient
constrained optimization by surrogate modeling and adaptive parameter control | math.OC | Constrained optimization of high-dimensional numerical problems plays an
important role in many scientific and industrial applications. Function
evaluations in many industrial applications are severely limited and no
analytical information about objective function and constraint functions is
available. For such expensi... | computer science |
4,283 | Data-Driven Dynamic Decision Models | stat.ML | This article outlines a method for automatically generating models of dynamic
decision-making that both have strong predictive power and are interpretable in
human terms. This is useful for designing empirically grounded agent-based
simulations and for gaining direct insight into observed dynamic processes. We
use an e... | computer science |
4,284 | Finding Approximate Local Minima Faster than Gradient Descent | math.OC | We design a non-convex second-order optimization algorithm that is guaranteed
to return an approximate local minimum in time which scales linearly in the
underlying dimension and the number of training examples. The time complexity
of our algorithm to find an approximate local minimum is even faster than that
of gradie... | computer science |
4,285 | Dynamic Mortality Risk Predictions in Pediatric Critical Care Using
Recurrent Neural Networks | stat.ML | Viewing the trajectory of a patient as a dynamical system, a recurrent neural
network was developed to learn the course of patient encounters in the
Pediatric Intensive Care Unit (PICU) of a major tertiary care center. Data
extracted from Electronic Medical Records (EMR) of about 12000 patients who
were admitted to the... | computer science |
4,286 | Robustness from structure: Inference with hierarchical spiking networks
on analog neuromorphic hardware | cs.NE | How spiking networks are able to perform probabilistic inference is an
intriguing question, not only for understanding information processing in the
brain, but also for transferring these computational principles to neuromorphic
silicon circuits. A number of computationally powerful spiking network models
have been pro... | computer science |
4,287 | Pattern representation and recognition with accelerated analog
neuromorphic systems | cs.NE | Despite being originally inspired by the central nervous system, artificial
neural networks have diverged from their biological archetypes as they have
been remodeled to fit particular tasks. In this paper, we review several
possibilites to reverse map these architectures to biologically more realistic
spiking networks... | computer science |
4,288 | SIM-CE: An Advanced Simulink Platform for Studying the Brain of
Caenorhabditis elegans | cs.NE | We introduce SIM-CE, an advanced, user-friendly modeling and simulation
environment in Simulink for performing multi-scale behavioral analysis of the
nervous system of Caenorhabditis elegans (C. elegans). SIM-CE contains an
implementation of the mathematical models of C. elegans's neurons and synapses,
in Simulink, whi... | computer science |
4,289 | A Probabilistic Linear Genetic Programming with Stochastic Context-Free
Grammar for solving Symbolic Regression problems | cs.NE | Traditional Linear Genetic Programming (LGP) algorithms are based only on the
selection mechanism to guide the search. Genetic operators combine or mutate
random portions of the individuals, without knowing if the result will lead to
a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP)
methods... | computer science |
4,290 | A Digital Neuromorphic Architecture Efficiently Facilitating Complex
Synaptic Response Functions Applied to Liquid State Machines | cs.NE | Information in neural networks is represented as weighted connections, or
synapses, between neurons. This poses a problem as the primary computational
bottleneck for neural networks is the vector-matrix multiply when inputs are
multiplied by the neural network weights. Conventional processing architectures
are not well... | computer science |
4,291 | Bayesian Belief Updating of Spatiotemporal Seizure Dynamics | stat.ML | Epileptic seizure activity shows complicated dynamics in both space and time.
To understand the evolution and propagation of seizures spatially extended sets
of data need to be analysed. We have previously described an efficient
filtering scheme using variational Laplace that can be used in the Dynamic
Causal Modelling... | computer science |
4,292 | Spatio-Temporal Backpropagation for Training High-performance Spiking
Neural Networks | cs.NE | Compared with artificial neural networks (ANNs), spiking neural networks
(SNNs) are promising to explore the brain-like behaviors since the spikes could
encode more spatio-temporal information. Although pre-training from ANN or
direct training based on backpropagation (BP) makes the supervised training of
SNNs possible... | computer science |
4,293 | When Neurons Fail | stat.ML | We view a neural network as a distributed system of which neurons can fail
independently, and we evaluate its robustness in the absence of any (recovery)
learning phase. We give tight bounds on the number of neurons that can fail
without harming the result of a computation. To determine our bounds, we
leverage the fact... | computer science |
4,294 | Modeling Label Ambiguity for Neural List-Wise Learning to Rank | cs.IR | List-wise learning to rank methods are considered to be the state-of-the-art.
One of the major problems with these methods is that the ambiguous nature of
relevance labels in learning to rank data is ignored. Ambiguity of relevance
labels refers to the phenomenon that multiple documents may be assigned the
same relevan... | computer science |
4,295 | Training Spiking Neural Networks for Cognitive Tasks: A Versatile
Framework Compatible to Various Temporal Codes | cs.NE | Conventional modeling approaches have found limitations in matching the
increasingly detailed neural network structures and dynamics recorded in
experiments to the diverse brain functionalities. On another approach, studies
have demonstrated to train spiking neural networks for simple functions using
supervised learnin... | computer science |
4,296 | Cortical microcircuits as gated-recurrent neural networks | cs.NE | Cortical circuits exhibit intricate recurrent architectures that are
remarkably similar across different brain areas. Such stereotyped structure
suggests the existence of common computational principles. However, such
principles have remained largely elusive. Inspired by gated-memory networks,
namely long short-term me... | computer science |
4,297 | A relativistic extension of Hopfield neural networks via the mechanical
analogy | cs.NE | We propose a modification of the cost function of the Hopfield model whose
salient features shine in its Taylor expansion and result in more than pairwise
interactions with alternate signs, suggesting a unified framework for handling
both with deep learning and network pruning. In our analysis, we heavily rely
on the H... | computer science |
4,298 | Generative Models for Stochastic Processes Using Convolutional Neural
Networks | stat.ML | The present paper aims to demonstrate the usage of Convolutional Neural
Networks as a generative model for stochastic processes, enabling researchers
from a wide range of fields (such as quantitative finance and physics) to
develop a general tool for forecasts and simulations without the need to
identify/assume a speci... | computer science |
4,299 | On the information in spike timing: neural codes derived from
polychronous groups | cs.NE | There is growing evidence regarding the importance of spike timing in neural
information processing, with even a small number of spikes carrying
information, but computational models lag significantly behind those for rate
coding. Experimental evidence on neuronal behavior is consistent with the
dynamical and state dep... | computer science |
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