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30,402 | Human activity recognition from mobile inertial sensors using recurrence
plots | cs.CV | Inertial sensors are present in most mobile devices nowadays and such devices
are used by people during most of their daily activities. In this paper, we
present an approach for human activity recognition based on inertial sensors by
employing recurrence plots (RP) and visual descriptors. The pipeline of the
proposed a... | computer science |
30,403 | AI Oriented Large-Scale Video Management for Smart City: Technologies,
Standards and Beyond | cs.CV | Deep learning has achieved substantial success in a series of tasks in
computer vision. Intelligent video analysis, which can be broadly applied to
video surveillance in various smart city applications, can also be driven by
such powerful deep learning engines. To practically facilitate deep neural
network models in th... | computer science |
30,404 | Zone-based Keyword Spotting in Bangla and Devanagari Documents | cs.CV | In this paper we present a word spotting system in text lines for offline
Indic scripts such as Bangla (Bengali) and Devanagari. Recently, it was shown
that zone-wise recognition method improves the word recognition performance
than conventional full word recognition system in Indic scripts. Inspired with
this idea we ... | computer science |
30,405 | 4DFAB: A Large Scale 4D Facial Expression Database for Biometric
Applications | cs.CV | The progress we are currently witnessing in many computer vision
applications, including automatic face analysis, would not be made possible
without tremendous efforts in collecting and annotating large scale visual
databases. To this end, we propose 4DFAB, a new large scale database of dynamic
high-resolution 3D faces... | computer science |
30,406 | Adversarial Attribute-Image Person Re-identification | cs.CV | While attributes have been widely used for person re-identification (Re-ID)
that matches the same person images across disjoint camera views, they are used
either as extra features or for performing multi-task learning to assist the
image-image person matching task. However, how to find a set of person images
according... | computer science |
30,407 | Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using
Cascaded 3D Fully Convolutional Networks | cs.CV | We present a method to address the challenging problem of segmentation of
lumbar vertebrae from CT images acquired with varying fields of view. Our
method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting
of a localization FCN and a segmentation FCN. More specifically, in the first
step we train a ... | computer science |
30,408 | Joint Embedding and Classification for SAR Target Recognition | cs.CV | Deep learning can be an effective and efficient means to automatically detect
and classify targets in synthetic aperture radar (SAR) images, but it is
critical for trained neural networks to be robust to variations that exist
between training and test environments. The layers in a neural network can be
understood as su... | computer science |
30,409 | O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis | cs.CV | We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D
shape analysis. Built upon the octree representation of 3D shapes, our method
takes the average normal vectors of a 3D model sampled in the finest leaf
octants as input and performs 3D CNN operations on the octants occupied by the
3D shape surf... | computer science |
30,410 | Deep Learning for automatic sale receipt understanding | cs.CV | As a general rule, data analytics are now mandatory for companies. Scanned
document analysis brings additional challenges introduced by paper damages and
scanning quality.In an industrial context, this work focuses on the automatic
understanding of sale receipts which enable access to essential and accurate
consumption... | computer science |
30,411 | Empirically Analyzing the Effect of Dataset Biases on Deep Face
Recognition Systems | cs.CV | It is unknown what kind of biases modern in the wild face datasets have
because of their lack of annotation. A direct consequence of this is that total
recognition rates alone only provide limited insight about the generalization
ability of a Deep Convolutional Neural Networks (DCNNs). We propose to
empirically study t... | computer science |
30,412 | Keypoint-based object tracking and localization using networks of
low-power embedded smart cameras | cs.CV | Object tracking and localization is a complex task that typically requires
processing power beyond the capabilities of low-power embedded cameras. This
paper presents a new approach to real-time object tracking and localization
using multi-view binary keypoints descriptor. The proposed approach offers a
compromise betw... | computer science |
30,413 | Can CNNs Construct Highly Accurate Model Efficiently with Limited
Training Samples? | cs.CV | It is well known that metamodel or surrogate modeling techniques have been
widely applied in engineering problems due to their higher efficiency. However,
with the increase of the linearity and dimensions, it is difficult for the
present popular metamodeling techniques to construct reliable metamodel and
apply to more ... | computer science |
30,414 | Automatic Spine Segmentation using Convolutional Neural Network via
Redundant Generation of Class Labels for 3D Spine Modeling | cs.CV | There has been a significant increase from 2010 to 2016 in the number of
people suffering from spine problems. The automatic image segmentation of the
spine obtained from a computed tomography (CT) image is important for
diagnosing spine conditions and for performing surgery with computer-assisted
surgery systems. The ... | computer science |
30,415 | Color Face Recognition using High-Dimension Quaternion-based Adaptive
Representation | cs.CV | Recently, quaternion collaborative representation-based classification (QCRC)
and quaternion sparse representation-based classification (QSRC) have been
proposed for color face recognition. They can obtain correlation information
among different color channels. However, their performance is unstable in
different condit... | computer science |
30,416 | Vision Recognition using Discriminant Sparse Optimization Learning | cs.CV | To better select the correct training sample and obtain the robust
representation of the query sample, this paper proposes a discriminant-based
sparse optimization learning model. This learning model integrates discriminant
and sparsity together. Based on this model, we then propose a classifier called
locality-based d... | computer science |
30,417 | Open Evaluation Tool for Layout Analysis of Document Images | cs.CV | This paper presents an open tool for standardizing the evaluation process of
the layout analysis task of document images at pixel level. We introduce a new
evaluation tool that is both available as a standalone Java application and as
a RESTful web service. This evaluation tool is free and open-source in order to
be a ... | computer science |
30,418 | Constrained Manifold Learning for Hyperspectral Imagery Visualization | cs.CV | Displaying the large number of bands in a hyper- spectral image (HSI) on a
trichromatic monitor is important for HSI processing and analysis system. The
visualized image shall convey as much information as possible from the original
HSI and meanwhile facilitate image interpretation. However, most existing
methods displ... | computer science |
30,419 | Recognizing Gender from Human Facial Regions using Genetic Algorithm | cs.CV | Recently, recognition of gender from facial images has gained a lot of
importance. There exist a handful of research work that focus on feature
extraction to obtain gender specific information from facial images. However,
analyzing different facial regions and their fusion help in deciding the gender
of a person from f... | computer science |
30,420 | Accelerating Convolutional Neural Networks for Continuous Mobile Vision
via Cache Reuse | cs.CV | Convolutional Neural Network (CNN) is the state-of-the-art algorithm of many
mobile vision fields. It is also applied in many vision tasks such as face
detection and augmented reality on mobile devices. Though benefited from the
high accuracy achieved via deep CNN models, nowadays commercial mobile devices
are often sh... | computer science |
30,421 | IEOPF: An Active Contour Model for Image Segmentation with
Inhomogeneities Estimated by Orthogonal Primary Functions | cs.CV | Image segmentation is still an open problem especially when intensities of
the interested objects are overlapped due to the presence of intensity
inhomogeneity (also known as bias field). To segment images with intensity
inhomogeneities, a bias correction embedded level set model is proposed where
Inhomogeneities are E... | computer science |
30,422 | Automated Pruning for Deep Neural Network Compression | cs.CV | In this work we present a method to improve the pruning step of the current
state-of-the-art methodology to compress neural networks. The novelty of the
proposed pruning technique is in its differentiability, which allows pruning to
be performed during the backpropagation phase of the network training. This
enables an ... | computer science |
30,423 | Convolutional Recurrent Neural Networks for Dynamic MR Image
Reconstruction | cs.CV | Accelerating the data acquisition of dynamic magnetic resonance imaging (MRI)
leads to a challenging ill-posed inverse problem, which has received great
interest from both the signal processing and machine learning community over
the last decades. The key ingredient to the problem is how to exploit the
temporal correla... | computer science |
30,424 | Tech Report: A Fast Multiscale Spatial Regularization for Sparse
Hyperspectral Unmixing | cs.CV | Sparse hyperspectral unmixing from large spectral libraries has been
considered to circumvent limitations of endmember extraction algorithms in many
applications. This strategy often leads to ill-posed inverse problems, which
can benefit from spatial regularization strategies. While existing spatial
regularization meth... | computer science |
30,425 | R-FCN-3000 at 30fps: Decoupling Detection and Classification | cs.CV | We present R-FCN-3000, a large-scale real-time object detector in which
objectness detection and classification are decoupled. To obtain the detection
score for an RoI, we multiply the objectness score with the fine-grained
classification score. Our approach is a modification of the R-FCN architecture
in which position... | computer science |
30,426 | Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene | cs.CV | The goal of this paper is to take a single 2D image of a scene and recover
the 3D structure in terms of a small set of factors: a layout representing the
enclosing surfaces as well as a set of objects represented in terms of shape
and pose. We propose a convolutional neural network-based approach to predict
this repres... | computer science |
30,427 | Structured Set Matching Networks for One-Shot Part Labeling | cs.CV | Diagrams often depict complex phenomena and serve as a good test bed for
visual and textual reasoning. However, understanding diagrams using natural
image understanding approaches requires large training datasets of diagrams,
which are very hard to obtain. Instead, this can be addressed as a matching
problem either bet... | computer science |
30,428 | Grounding Referring Expressions in Images by Variational Context | cs.CV | We focus on grounding (i.e., localizing or linking) referring expressions in
images, e.g., "largest elephant standing behind baby elephant". This is a
general yet challenging vision-language task since it does not only require the
localization of objects, but also the multimodal comprehension of context ---
visual attr... | computer science |
30,429 | Population-based Respiratory 4D Motion Atlas Construction and its
Application for VR Simulations of Liver Punctures | cs.CV | Virtual reality (VR) training simulators of liver needle insertion in the
hepatic area of breathing virtual patients currently need 4D data acquisitions
as a prerequisite. Here, first a population-based breathing virtual patient 4D
atlas can be built and second the requirement of a dose-relevant or expensive
acquisitio... | computer science |
30,430 | Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic
Sign Recognition | cs.CV | Recent advances in visual recognition show overarching success by virtue of
large amounts of supervised data. However,the acquisition of a large supervised
dataset is often challenging. This is also true for intelligent transportation
applications, i.e., traffic sign recognition. For example, a model trained with
data ... | computer science |
30,431 | The Best of Both Worlds: Learning Geometry-based 6D Object Pose
Estimation | cs.CV | We address the task of estimating the 6D pose of known rigid objects, from
RGB and RGB-D input images, in scenarios where the objects are heavily
occluded. Our main contribution is a new modular processing pipeline. The first
module localizes all known objects in the image via an existing instance
segmentation network.... | computer science |
30,432 | Zero-Shot Visual Recognition using Semantics-Preserving Adversarial
Embedding Network | cs.CV | We propose a novel framework called Semantics-Preserving Adversarial
Embedding Network (SP-AEN) for zero-shot visual recognition (ZSL), where test
images and their classes are both unseen during training. SP-AEN aims to tackle
the inherent problem --- semantic loss --- in the prevailing family of
embedding-based ZSL, w... | computer science |
30,433 | Blind Image Deblurring Using Row-Column Sparse Representations | cs.CV | Blind image deblurring is a particularly challenging inverse problem where
the blur kernel is unknown and must be estimated en route to recover the
deblurred image. The problem is of strong practical relevance since many
imaging devices such as cellphone cameras, must rely on deblurring algorithms
to yield satisfactory... | computer science |
30,434 | Learning Latent Super-Events to Detect Multiple Activities in Videos | cs.CV | In this paper, we introduce the concept of learning latent
\emph{super-events} from activity videos, and present how it benefits activity
detection in continuous videos. We define a super-event as a set of multiple
events occurring together in videos with a particular temporal organization; it
is the opposite concept o... | computer science |
30,435 | Learning to Forecast Videos of Human Activity with Multi-granularity
Models and Adaptive Rendering | cs.CV | We propose an approach for forecasting video of complex human activity
involving multiple people. Direct pixel-level prediction is too simple to
handle the appearance variability in complex activities. Hence, we develop
novel intermediate representations. An architecture combining a hierarchical
temporal model for pred... | computer science |
30,436 | What's in my closet?: Image classification using fuzzy logic | cs.CV | A fuzzy system was created in MATLAB to identify an item of clothing as a
dress, shirt, or pair of pants from a series of input images. The system was
initialized using a high-contrast vector-image of each item of clothing as the
state closest to a direct solution. Nine other user-input images (three of each
item) were... | computer science |
30,437 | Learning Semantic Concepts and Order for Image and Sentence Matching | cs.CV | Image and sentence matching has made great progress recently, but it remains
challenging due to the large visual-semantic discrepancy. This mainly arises
from that the representation of pixel-level image usually lacks of high-level
semantic information as in its matched sentence. In this work, we propose a
semantic-enh... | computer science |
30,438 | Saliency Preservation in Low-Resolution Grayscale Images | cs.CV | Visual salience detection originated over 500 million years ago and is one of
the most efficient mechanisms in nature. In contrast, state-of-the-art
computational saliency models are often complex and inefficient, primarily
because they process high-resolution color (HC) images. Insights into the
evolutionary origins o... | computer science |
30,439 | Unsupervised Multi-Domain Image Translation with Domain-Specific
Encoders/Decoders | cs.CV | Unsupervised Image-to-Image Translation achieves spectacularly advanced
developments nowadays. However, recent approaches mainly focus on one model
with two domains, which may face heavy burdens with large cost of $O(n^2)$
training time and model parameters, under such a requirement that $n$ domains
are freely transfer... | computer science |
30,440 | Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning | cs.CV | Modern neural image captioning systems typically adopt the encoder-decoder
framework consisting of two principal components: a convolutional neural
network (CNN) for image feature extraction and a recurrent neural network (RNN)
for caption generation. Inspired by the robustness analysis of CNN-based image
classifiers t... | computer science |
30,441 | Automatic Segmentation and Overall Survival Prediction in Gliomas using
Fully Convolutional Neural Network and Texture Analysis | cs.CV | In this paper, we use a fully convolutional neural network (FCNN) for the
segmentation of gliomas from Magnetic Resonance Images (MRI). A fully
automatic, voxel based classification was achieved by training a 23 layer deep
FCNN on 2-D slices extracted from patient volumes. The network was trained on
slices extracted fr... | computer science |
30,442 | Separating Reflection and Transmission Images in the Wild | cs.CV | The reflections caused by common semi-reflectors, such as glass windows, can
severely impact the performance of computer vision algorithms. State-of-the-art
works can successfully remove reflections on synthetic data and in controlled
scenarios. However, they are based on strong assumptions and fail to generalize
to re... | computer science |
30,443 | Detecting Curve Text in the Wild: New Dataset and New Solution | cs.CV | Scene text detection has been made great progress in recent years. The
detection manners are evolving from axis-aligned rectangle to rotated rectangle
and further to quadrangle. However, current datasets contain very little curve
text, which can be widely observed in scene images such as signboard, product
name and so ... | computer science |
30,444 | Beyond the Pixel-Wise Loss for Topology-Aware Delineation | cs.CV | Delineation of curvilinear structures is an important problem in Computer
Vision with multiple practical applications. With the advent of Deep Learning,
many current approaches on automatic delineation have focused on finding more
powerful deep architectures, but have continued using the habitual pixel-wise
losses such... | computer science |
30,445 | Stretching Domain Adaptation: How far is too far? | cs.CV | While deep learning has led to significant advances in visual recognition
over the past few years, such advances often require a lot of annotated data.
While unsupervised domain adaptation has emerged as an alternative approach
that doesn't require as much annotated data, prior evaluations of domain
adaptation have bee... | computer science |
30,446 | Joint 3D Proposal Generation and Object Detection from View Aggregation | cs.CV | We present AVOD, an Aggregate View Object Detection network for autonomous
driving scenarios. The proposed neural network architecture uses LIDAR point
clouds and RGB images to generate features that are shared by two subnetworks:
a region proposal network (RPN) and a second stage detector network. The
proposed RPN use... | computer science |
30,447 | From Lifestyle Vlogs to Everyday Interactions | cs.CV | A major stumbling block to progress in understanding basic human
interactions, such as getting out of bed or opening a refrigerator, is lack of
good training data. Most past efforts have gathered this data explicitly:
starting with a laundry list of action labels, and then querying search engines
for videos tagged with... | computer science |
30,448 | Burst Denoising with Kernel Prediction Networks | cs.CV | We present a technique for jointly denoising bursts of images taken from a
handheld camera. In particular, we propose a convolutional neural network
architecture for predicting spatially varying kernels that can both align and
denoise frames, a synthetic data generation approach based on a realistic noise
formation mod... | computer science |
30,449 | Top-down Flow Transformer Networks | cs.CV | We study the deformation fields of feature maps across convolutional network
layers under explicit top-down spatial transformations. We propose top-down
flow transformer (TFT) by focusing on three transformations: translation,
rotation, and scaling. Flow transformation generators, under controllable
parameters, are lea... | computer science |
30,450 | Deep Regionlets for Object Detection | cs.CV | A key challenge in generic object detection is being to handle large
variations in object scale, poses, viewpoints, especially part deformations
when determining the location for specified object categories. Recent advances
in deep neural networks have achieved promising results for object detection by
extending the tr... | computer science |
30,451 | Tomographic Reconstruction using Global Statistical Prior | cs.CV | Recent research in tomographic reconstruction is motivated by the need to
efficiently recover detailed anatomy from limited measurements. One of the ways
to compensate for the increasingly sparse sets of measurements is to exploit
the information from templates, i.e., prior data available in the form of
already reconst... | computer science |
30,452 | CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign
Recognition | cs.CV | In this paper, we investigate the robustness of traffic sign recognition
algorithms under challenging conditions. Existing datasets are limited in terms
of their size and challenging condition coverage, which motivated us to
generate the Challenging Unreal and Real Environments for Traffic Sign
Recognition (CURE-TSR) d... | computer science |
30,453 | Stacked Conditional Generative Adversarial Networks for Jointly Learning
Shadow Detection and Shadow Removal | cs.CV | Understanding shadows from a single image spontaneously derives into two
types of task in previous studies, containing shadow detection and shadow
removal. In this paper, we present a multi-task perspective, which is not
embraced by any existing work, to jointly learn both detection and removal in
an end-to-end fashion... | computer science |
30,454 | TV-GAN: Generative Adversarial Network Based Thermal to Visible Face
Recognition | cs.CV | This work tackles the face recognition task on images captured using thermal
camera sensors which can operate in the non-light environment. While it can
greatly increase the scope and benefits of the current security surveillance
systems, performing such a task using thermal images is a challenging problem
compared to ... | computer science |
30,455 | Broadcasting Convolutional Network | cs.CV | While convolutional neural networks (CNNs) are widely used for handling
spatio-temporal scenes, there exist limitations in reasoning relations among
spatial features caused by their inherent structures, which have been issued
consistently in many studies. In this paper, we propose Broadcasting
Convolutional Networks (B... | computer science |
30,456 | Maximum Classifier Discrepancy for Unsupervised Domain Adaptation | cs.CV | In this work, we present a method for unsupervised domain adaptation (UDA),
where we aim to transfer knowledge from a label-rich domain (i.e., a source
domain) to an unlabeled domain (i.e., a target domain). Many adversarial
learning methods have been proposed for this task. These methods train domain
classifier networ... | computer science |
30,457 | Consistent Multiple Graph Matching with Multi-layer Random Walks
Synchronization | cs.CV | We address the correspondence search problem among multiple graphs with
complex properties while considering the matching consistency. We describe each
pair of graphs by combining multiple attributes, then jointly match them in a
unified framework. The main contribution of this paper is twofold. First, we
formulate the... | computer science |
30,458 | In-Place Activated BatchNorm for Memory-Optimized Training of DNNs | cs.CV | In this work we present In-Place Activated Batch Normalization (InPlace-ABN)
- a novel approach to drastically reduce the training memory footprint of
modern deep neural networks in a computationally efficient way. Our solution
substitutes the conventionally used succession of BatchNorm + Activation layers
with a singl... | computer science |
30,459 | Disentangled Person Image Generation | cs.CV | Generating novel, yet realistic, images of persons is a challenging task due
to the complex interplay between the different image factors, such as the
foreground, background and pose information. In this work, we aim at generating
such images based on a novel, two-stage reconstruction pipeline that learns a
disentangle... | computer science |
30,460 | Creating Capsule Wardrobes from Fashion Images | cs.CV | We propose to automatically create capsule wardrobes. Given an inventory of
candidate garments and accessories, the algorithm must assemble a minimal set
of items that provides maximal mix-and-match outfits. We pose the task as a
subset selection problem. To permit efficient subset selection over the space
of all outfi... | computer science |
30,461 | Incremental Learning in Deep Convolutional Neural Networks Using Partial
Network Sharing | cs.CV | Deep convolutional neural network (DCNN) based supervised learning is a
widely practiced approach for large-scale image classification. However,
retraining these large networks to accommodate new, previously unseen data
demands high computational time and energy requirements. Also, previously seen
training samples may ... | computer science |
30,462 | On the Duality Between Retinex and Image Dehazing | cs.CV | Image dehazing deals with the removal of undesired loss of visibility in
outdoor images due to the presence of fog. Retinex is a color vision model
mimicking the ability of the Human Visual System to robustly discount varying
illuminations when observing a scene under different spectral lighting
conditions. Retinex has... | computer science |
30,463 | Super-FAN: Integrated facial landmark localization and super-resolution
of real-world low resolution faces in arbitrary poses with GANs | cs.CV | This paper addresses two challenging tasks: improving the quality of
real-world low resolution face images via super-resolution and accurately
locating the facial landmarks on such poor resolution images. To this end, we
make the following 5 contributions: (a) we propose Super-FAN: the very first
end-to-end system that... | computer science |
30,464 | Hybrid eye center localization using cascaded regression and
hand-crafted model fitting | cs.CV | We propose a new cascaded regressor for eye center detection. Previous
methods start from a face or an eye detector and use either advanced features
or powerful regressors for eye center localization, but not both. Instead, we
detect the eyes more accurately using an existing facial feature alignment
method. We improve... | computer science |
30,465 | Stacked Denoising Autoencoders and Transfer Learning for Immunogold
Particles Detection and Recognition | cs.CV | In this paper we present a system for the detection of immunogold particles
and a Transfer Learning (TL) framework for the recognition of these immunogold
particles. Immunogold particles are part of a high-magnification method for the
selective localization of biological molecules at the subcellular level only
visible ... | computer science |
30,466 | Self-supervised Multi-level Face Model Learning for Monocular
Reconstruction at over 250 Hz | cs.CV | The reconstruction of dense 3D models of face geometry and appearance from a
single image is highly challenging and ill-posed. To constrain the problem,
many approaches rely on strong priors, such as parametric face models learned
from limited 3D scan data. However, prior models restrict generalization of the
true dive... | computer science |
30,467 | MoDL: Model Based Deep Learning Architecture for Inverse Problems | cs.CV | We introduce a model-based image reconstruction framework with a convolution
neural network (CNN) based regularization prior. While CNN based image recovery
methods are ideally suited for inverse problems with a convolutional structure,
deep learning architectures for problems that do not have a convolutional
structure... | computer science |
30,468 | Learned Perceptual Image Enhancement | cs.CV | Learning a typical image enhancement pipeline involves minimization of a loss
function between enhanced and reference images. While L1 and L2 losses are
perhaps the most widely used functions for this purpose, they do not
necessarily lead to perceptually compelling results. In this paper, we show
that adding a learned ... | computer science |
30,469 | Multi-Scale Video Frame-Synthesis Network with Transitive Consistency
Loss | cs.CV | Traditional approaches to interpolate/extrapolate frames in a video sequence
require accurate pixel correspondences between images, e.g., using optical
flow. Their results stem on the accuracy of optical flow estimation, and could
generate heavy artifacts when flow estimation failed. Recently methods using
auto-encoder... | computer science |
30,470 | Network Analysis for Explanation | cs.CV | Safety critical systems strongly require the quality aspects of artificial
intelligence including explainability. In this paper, we analyzed a trained
network to extract features which mainly contribute the inference. Based on the
analysis, we developed a simple solution to generate explanations of the
inference proces... | computer science |
30,471 | Deep Image Smoothing based on Texture and Structure Guidance | cs.CV | Image smoothing is a fundamental task in computer vision, which aims to
retain salient structures and remove insignificant textures. In this paper, we
tackle the natural deficiency of existing methods, that they cannot properly
distinguish textures and structures with similar low-level appearance. While
deep learning a... | computer science |
30,472 | Chaining Identity Mapping Modules for Image Denoising | cs.CV | We propose to learn a fully-convolutional network model that consists of a
Chain of Identity Mapping Modules (CIMM) for image denoising. The CIMM
structure possesses two distinctive features that are important for the noise
removal task. Firstly, each residual unit employs identity mappings as the skip
connections and ... | computer science |
30,473 | Dense Optical Flow based Change Detection Network Robust to Difference
of Camera Viewpoints | cs.CV | This paper presents a novel method for detecting scene changes from a pair of
images with a difference of camera viewpoints using a dense optical flow based
change detection network. In the case that camera poses of input images are
fixed or known, such as with surveillance and satellite cameras, the pixel
corresponden... | computer science |
30,474 | Compact Hash Code Learning with Binary Deep Neural Network | cs.CV | In this work, we firstly propose deep network models and learning algorithms
for learning binary hash codes given image representations under both
unsupervised and supervised manners. Then, by leveraging the powerful capacity
of convolutional neural networks, we propose an end-to-end architecture which
jointly learns t... | computer science |
30,475 | Shape from Shading through Shape Evolution | cs.CV | In this paper, we address the shape-from-shading problem by training deep
networks with synthetic images. Unlike conventional approaches that combine
deep learning and synthetic imagery, we propose an approach that does not need
any external shape dataset to render synthetic images. Our approach consists of
two synergi... | computer science |
30,476 | Learning 2D Gabor Filters by Infinite Kernel Learning Regression | cs.CV | Gabor functions have wide-spread applications in image processing and
computer vision. In this paper, we prove that 2D Gabor functions are
translation-invariant positive-definite kernels and propose a novel formulation
for the problem of image representation with Gabor functions based on infinite
kernel learning regres... | computer science |
30,477 | Defense against Adversarial Attacks Using High-Level Representation
Guided Denoiser | cs.CV | Neural networks are vulnerable to adversarial examples. This phenomenon poses
a threat to their applications in security-sensitive systems. It is thus
important to develop effective defending methods to strengthen the robustness
of neural networks to adversarial attacks. Many techniques have been proposed,
but only a f... | computer science |
30,478 | Direct and Real-Time Cardiovascular Risk Prediction | cs.CV | Coronary artery calcium (CAC) burden quantified in low-dose chest CT is a
predictor of cardiovascular events. We propose an automatic method for CAC
quantification, circumventing intermediate segmentation of CAC. The method
determines a bounding box around the heart using a ConvNet for localization.
Subsequently, a ded... | computer science |
30,479 | A Frequency Domain Neural Network for Fast Image Super-resolution | cs.CV | In this paper, we present a frequency domain neural network for image
super-resolution. The network employs the convolution theorem so as to cast
convolutions in the spatial domain as products in the frequency domain.
Moreover, the non-linearity in deep nets, often achieved by a rectifier unit,
is here cast as a convol... | computer science |
30,480 | An Integrated Platform for Live 3D Human Reconstruction and Motion
Capturing | cs.CV | The latest developments in 3D capturing, processing, and rendering provide
means to unlock novel 3D application pathways. The main elements of an
integrated platform, which target tele-immersion and future 3D applications,
are described in this paper, addressing the tasks of real-time capturing,
robust 3D human shape/a... | computer science |
30,481 | Image Inpainting for High-Resolution Textures using CNN Texture
Synthesis | cs.CV | Deep neural networks have been successfully applied to problems such as image
segmentation, image super-resolution, coloration and image inpainting. In this
work we propose the use of convolutional neural networks (CNN) for image
inpainting of large regions in high-resolution textures. Due to limited
computational reso... | computer science |
30,482 | Weaving Multi-scale Context for Single Shot Detector | cs.CV | Aggregating context information from multiple scales has been proved to be
effective for improving accuracy of Single Shot Detectors (SSDs) on object
detection. However, existing multi-scale context fusion techniques are
computationally expensive, which unfavorably diminishes the advantageous speed
of SSD. In this work... | computer science |
30,483 | Combining Deep Universal Features, Semantic Attributes, and Hierarchical
Classification for Zero-Shot Learning | cs.CV | We address zero-shot (ZS) learning, building upon prior work in hierarchical
classification by combining it with approaches based on semantic attribute
estimation. For both non-novel and novel image classes we compare multiple
formulations of the problem, starting with deep universal features in each
case. We investiga... | computer science |
30,484 | Minimal Solvers for Monocular Rolling Shutter Compensation under
Ackermann Motion | cs.CV | Modern automotive vehicles are often equipped with a budget commercial
rolling shutter camera. These devices often produce distorted images due to the
inter-row delay of the camera while capturing the image. Recent methods for
monocular rolling shutter motion compensation utilize blur kernel and the
straightness proper... | computer science |
30,485 | Class Rectification Hard Mining for Imbalanced Deep Learning | cs.CV | Recognising detailed facial or clothing attributes in images of people is a
challenging task for computer vision, especially when the training data are
both in very large scale and extremely imbalanced among different attribute
classes. To address this problem, we formulate a novel scheme for batch
incremental hard sam... | computer science |
30,486 | Basic Thresholding Classification | cs.CV | In this thesis, we propose a light-weight sparsity-based algorithm, basic
thresholding classifier (BTC), for classification applications (such as face
identification, hyper-spectral image classification, etc.) which is capable of
identifying test samples extremely rapidly and performing high classification
accuracy. Or... | computer science |
30,487 | Transformational Sparse Coding | cs.CV | A fundamental problem faced by object recognition systems is that objects and
their features can appear in different locations, scales and orientations.
Current deep learning methods attempt to achieve invariance to local
translations via pooling, discarding the locations of features in the process.
Other approaches ex... | computer science |
30,488 | IQA: Visual Question Answering in Interactive Environments | cs.CV | We introduce Interactive Question Answering (IQA), the task of answering
questions that require an autonomous agent to interact with a dynamic visual
environment. IQA presents the agent with a scene and a question, like: "Are
there any apples in the fridge?" The agent must navigate around the scene,
acquire visual unde... | computer science |
30,489 | Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing
Imagery | cs.CV | Fine-grained object recognition that aims to identify the type of an object
among a large number of subcategories is an emerging application with the
increasing resolution that exposes new details in image data. Traditional fully
supervised algorithms fail to handle this problem where there is low
between-class varianc... | computer science |
30,490 | MapNet: Geometry-Aware Learning of Maps for Camera Localization | cs.CV | Maps are a key component in image-based camera localization and visual SLAM
systems: they are used to establish geometric constraints between images,
correct drift in relative pose estimation, and relocalize cameras after lost
tracking. The exact definitions of maps, however, are often
application-specific and hand-cra... | computer science |
30,491 | Visual aesthetic analysis using deep neural network: model and
techniques to increase accuracy without transfer learning | cs.CV | We train a deep Convolutional Neural Network (CNN) from scratch for visual
aesthetic analysis in images and discuss techniques we adopt to improve the
accuracy. We avoid the prevalent best transfer learning approaches of using
pretrained weights to perform the task and train a model from scratch to get
accuracy of 78.7... | computer science |
30,492 | Deep Koalarization: Image Colorization using CNNs and
Inception-ResNet-v2 | cs.CV | We review some of the most recent approaches to colorize gray-scale images
using deep learning methods. Inspired by these, we propose a model which
combines a deep Convolutional Neural Network trained from scratch with
high-level features extracted from the Inception-ResNet-v2 pre-trained model.
Thanks to its fully con... | computer science |
30,493 | CycleGAN Face-off | cs.CV | Face-off is an interesting case of style transfer where the facial
expressions and attributes of one person could be fully transformed to another
face. We are interested in the unsupervised training process which only
requires two sequences of unaligned video frames from each person and learns
what shared attributes to... | computer science |
30,494 | SPP-Net: Deep Absolute Pose Regression with Synthetic Views | cs.CV | Image based localization is one of the important problems in computer vision
due to its wide applicability in robotics, augmented reality, and autonomous
systems. There is a rich set of methods described in the literature how to
geometrically register a 2D image w.r.t.\ a 3D model. Recently, methods based
on deep (and ... | computer science |
30,495 | Single-Shot Multi-Person 3D Body Pose Estimation From Monocular RGB
Input | cs.CV | We propose a new efficient single-shot method for multi-person 3D pose
estimation in general scenes from a monocular RGB camera. Our fully
convolutional DNN-based approach jointly infers 2D and 3D joint locations on
the basis of an extended 3D location map supported by body part associations.
This new formulation enabl... | computer science |
30,496 | Geometry Guided Adversarial Facial Expression Synthesis | cs.CV | Facial expression synthesis has drawn much attention in the field of computer
graphics and pattern recognition. It has been widely used in face animation and
recognition. However, it is still challenging due to the high-level semantic
presence of large and non-linear face geometry variations. This paper proposes
a Geom... | computer science |
30,497 | 3D Facial Expression Reconstruction using Cascaded Regression | cs.CV | This paper proposes a novel model fitting algorithm for 3D facial expression
reconstruction from a single image. Face expression reconstruction from a
single image is a challenging task in computer vision. Most state-of-the-art
methods fit the input image to a 3D Morphable Model (3DMM). These methods need
to solve a st... | computer science |
30,498 | Dynamics Transfer GAN: Generating Video by Transferring Arbitrary
Temporal Dynamics from a Source Video to a Single Target Image | cs.CV | In this paper, we propose Dynamics Transfer GAN; a new method for generating
video sequences based on generative adversarial learning. The spatial
constructs of a generated video sequence are acquired from the target image.
The dynamics of the generated video sequence are imported from a source video
sequence, with arb... | computer science |
30,499 | FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder
Network for face detection | cs.CV | Because of affected by weather conditions, camera pose and range, etc.
Objects are usually small, blur, occluded and diverse pose in the images
gathered from outdoor surveillance cameras or access control system. It is
challenging and important to detect faces precisely for face recognition system
in the field of publi... | computer science |
30,500 | The Effectiveness of Data Augmentation for Detection of Gastrointestinal
Diseases from Endoscopical Images | cs.CV | The lack, due to privacy concerns, of large public databases of medical
pathologies is a well-known and major problem, substantially hindering the
application of deep learning techniques in this field. In this article, we
investigate the possibility to supply to the deficiency in the number of data
by means of data aug... | computer science |
30,501 | Can We Teach Computers to Understand Art? Domain Adaptation for
Enhancing Deep Networks Capacity to De-Abstract Art | cs.CV | Humans comprehend a natural scene at a single glance; painters and other
visual artists, through their abstract representations, stressed this capacity
to the limit. The performance of computer vision solutions matched that of
humans in many problems of visual recognition. In this paper we address the
problem of recogn... | computer science |
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