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29,202 | 3D Morphable Models as Spatial Transformer Networks | cs.CV | In this paper, we show how a 3D Morphable Model (i.e. a statistical model of
the 3D shape of a class of objects such as faces) can be used to spatially
transform input data as a module (a 3DMM-STN) within a convolutional neural
network. This is an extension of the original spatial transformer network in
that we are abl... | computer science |
29,203 | Recent Advances in the Applications of Convolutional Neural Networks to
Medical Image Contour Detection | cs.CV | The fast growing deep learning technologies have become the main solution of
many machine learning problems for medical image analysis. Deep convolution
neural networks (CNNs), as one of the most important branch of the deep
learning family, have been widely investigated for various computer-aided
diagnosis tasks inclu... | computer science |
29,204 | Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake
Expression Prediction | cs.CV | Frame-level visual features are generally aggregated in time with the
techniques such as LSTM, Fisher Vectors, NetVLAD etc. to produce a robust
video-level representation. We here introduce a learnable aggregation technique
whose primary objective is to retain short-time temporal structure between
frame-level features ... | computer science |
29,205 | Automatic Myocardial Segmentation by Using A Deep Learning Network in
Cardiac MRI | cs.CV | Cardiac function is of paramount importance for both prognosis and treatment
of different pathologies such as mitral regurgitation, ischemia, dyssynchrony
and myocarditis. Cardiac behavior is determined by structural and functional
features. In both cases, the analysis of medical imaging studies requires to
detect and ... | computer science |
29,206 | Review on Computer Vision Techniques in Emergency Situation | cs.CV | In emergency situations, actions that save lives and limit the impact of
hazards are crucial. In order to act, situational awareness is needed to decide
what to do. Geolocalized photos and video of the situations as they evolve can
be crucial in better understanding them and making decisions faster. Cameras
are almost ... | computer science |
29,207 | FacePoseNet: Making a Case for Landmark-Free Face Alignment | cs.CV | We show how a simple convolutional neural network (CNN) can be trained to
accurately and robustly regress 6 degrees of freedom (6DoF) 3D head pose,
directly from image intensities. We further explain how this FacePoseNet (FPN)
can be used to align faces in 2D and 3D as an alternative to explicit facial
landmark detecti... | computer science |
29,208 | SPARCNN: SPAtially Related Convolutional Neural Networks | cs.CV | The ability to accurately detect and classify objects at varying pixel sizes
in cluttered scenes is crucial to many Navy applications. However, detection
performance of existing state-of the-art approaches such as convolutional
neural networks (CNNs) degrade and suffer when applied to such cluttered and
multi-object de... | computer science |
29,209 | Objective Classes for Micro-Facial Expression Recognition | cs.CV | Micro-expressions are brief spontaneous facial expressions that appear on a
face when a person conceals an emotion, making them different to normal facial
expressions in subtlety and duration. Currently, emotion classes within the
CASME II dataset are based on Action Units and self-reports, creating conflicts
during ma... | computer science |
29,210 | A Robust Indoor Scene Recognition Method based on Sparse Representation | cs.CV | In this paper, we present a robust method for scene recognition, which
leverages Convolutional Neural Networks (CNNs) features and Sparse Coding
setting by creating a new representation of indoor scenes. Although CNNs highly
benefited the fields of computer vision and pattern recognition, convolutional
layers adjust we... | computer science |
29,211 | Leaf Counting with Deep Convolutional and Deconvolutional Networks | cs.CV | In this paper, we investigate the problem of counting rosette leaves from an
RGB image, an important task in plant phenotyping. We propose a data-driven
approach for this task generalized over different plant species and imaging
setups. To accomplish this task, we use state-of-the-art deep learning
architectures: a dec... | computer science |
29,212 | Hierarchical Multi-scale Attention Networks for Action Recognition | cs.CV | Recurrent Neural Networks (RNNs) have been widely used in natural language
processing and computer vision. Among them, the Hierarchical Multi-scale RNN
(HM-RNN), a kind of multi-scale hierarchical RNN proposed recently, can learn
the hierarchical temporal structure from data automatically. In this paper, we
extend the ... | computer science |
29,213 | A wavelet frame coefficient total variational model for image
restoration | cs.CV | In this paper, we propose a vector total variation (VTV) of feature image
model for image restoration. The VTV imposes different smoothing powers on
different features (e.g. edges and cartoons) based on choosing various
regularization parameters. Thus, the model can simultaneously preserve edges
and remove noises. Next... | computer science |
29,214 | Learning Spatio-Temporal Features with 3D Residual Networks for Action
Recognition | cs.CV | Convolutional neural networks with spatio-temporal 3D kernels (3D CNNs) have
an ability to directly extract spatio-temporal features from videos for action
recognition. Although the 3D kernels tend to overfit because of a large number
of their parameters, the 3D CNNs are greatly improved by using recent huge
video data... | computer science |
29,215 | Gait Recognition from Motion Capture Data | cs.CV | Gait recognition from motion capture data, as a pattern classification
discipline, can be improved by the use of machine learning. This paper
contributes to the state-of-the-art with a statistical approach for extracting
robust gait features directly from raw data by a modification of Linear
Discriminant Analysis with ... | computer science |
29,216 | Evaluation of Deep Learning on an Abstract Image Classification Dataset | cs.CV | Convolutional Neural Networks have become state of the art methods for image
classification over the last couple of years. By now they perform better than
human subjects on many of the image classification datasets. Most of these
datasets are based on the notion of concrete classes (i.e. images are
classified by the ty... | computer science |
29,217 | Integral Curvature Representation and Matching Algorithms for
Identification of Dolphins and Whales | cs.CV | We address the problem of identifying individual cetaceans from images
showing the trailing edge of their fins. Given the trailing edge from an
unknown individual, we produce a ranking of known individuals from a database.
The nicks and notches along the trailing edge define an individual's unique
signature. We define ... | computer science |
29,218 | Shape Registration with Directional Data | cs.CV | We propose several cost functions for registration of shapes encoded with
Euclidean and/or non-Euclidean information (unit vectors). Our framework is
assessed for estimation of both rigid and non-rigid transformations between the
target and model shapes corresponding to 2D contours and 3D surfaces. The
experimental res... | computer science |
29,219 | Accelerated Reconstruction of Perfusion-Weighted MRI Enforcing Jointly
Local and Nonlocal Spatio-temporal Constraints | cs.CV | Perfusion-weighted magnetic resonance imaging (MRI) is an imaging technique
that allows one to measure tissue perfusion in an organ of interest through the
injection of an intravascular paramagnetic contrast agent (CA). Due to a
preference for high temporal and spatial resolution in many applications, this
modality cou... | computer science |
29,220 | Semantic Foggy Scene Understanding with Synthetic Data | cs.CV | This work addresses the problem of semantic foggy scene understanding (SFSU).
Although extensive research has been performed on image dehazing and on
semantic scene understanding with weather-clear images, little attention has
been paid to SFSU. Due to the difficulty of collecting and annotating foggy
images, we choose... | computer science |
29,221 | The Parallel Algorithm for the 2-D Discrete Wavelet Transform | cs.CV | The discrete wavelet transform can be found at the heart of many
image-processing algorithms. Until now, the transform on general-purpose
processors (CPUs) was mostly computed using a separable lifting scheme. As the
lifting scheme consists of a small number of operations, it is preferred for
processing using single-co... | computer science |
29,222 | Multi-task Self-Supervised Visual Learning | cs.CV | We investigate methods for combining multiple self-supervised tasks--i.e.,
supervised tasks where data can be collected without manual labeling--in order
to train a single visual representation. First, we provide an apples-to-apples
comparison of four different self-supervised tasks using the very deep
ResNet-101 archi... | computer science |
29,223 | Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo
Cameras | cs.CV | We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for
highly accurate real-time visual odometry estimation of large-scale
environments from stereo cameras. It jointly optimizes for all the model
parameters within the active window, including the intrinsic/extrinsic camera
parameters of all keyfram... | computer science |
29,224 | RaspiReader: An Open Source Fingerprint Reader Facilitating Spoof
Detection | cs.CV | We present the design and prototype of an open source, optical fingerprint
reader, called RaspiReader, using ubiquitous components. RaspiReader, a
low-cost and easy to assemble reader, provides the fingerprint research
community a seamless and simple method for gaining more control over the
sensing component of fingerp... | computer science |
29,225 | Batch-Based Activity Recognition from Egocentric Photo-Streams | cs.CV | Activity recognition from long unstructured egocentric photo-streams has
several applications in assistive technology such as health monitoring and
frailty detection, just to name a few. However, one of its main technical
challenges is to deal with the low frame rate of wearable photo-cameras, which
causes abrupt appea... | computer science |
29,226 | Deep Learning for Target Classification from SAR Imagery: Data
Augmentation and Translation Invariance | cs.CV | This report deals with translation invariance of convolutional neural
networks (CNNs) for automatic target recognition (ATR) from synthetic aperture
radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR
ATR represents the robustness against misalignment of target chips extracted
from SAR images... | computer science |
29,227 | Robust Stereo Feature Descriptor for Visual Odometry | cs.CV | In this paper, we propose a simple way to utilize stereo camera data to
improve feature descriptors. Computer vision algorithms that use a stereo
camera require some calculations of 3D information. We leverage this
pre-calculated information to improve feature descriptor algorithms. We use the
3D feature information to... | computer science |
29,228 | 3D Binary Signatures | cs.CV | In this paper, we propose a novel binary descriptor for 3D point clouds. The
proposed descriptor termed as 3D Binary Signature (3DBS) is motivated from the
matching efficiency of the binary descriptors for 2D images. 3DBS describes
keypoints from point clouds with a binary vector resulting in extremely fast
matching. T... | computer science |
29,229 | Distributed Bundle Adjustment | cs.CV | Most methods for Bundle Adjustment (BA) in computer vision are either
centralized or operate incrementally. This leads to poor scaling and affects
the quality of solution as the number of images grows in large scale structure
from motion (SfM). Furthermore, they cannot be used in scenarios where image
acquisition and p... | computer science |
29,230 | Maximum A Posteriori Estimation of Distances Between Deep Features in
Still-to-Video Face Recognition | cs.CV | The paper deals with the still-to-video face recognition for the small sample
size problem based on computation of distances between high-dimensional deep
bottleneck features. We present the novel statistical recognition method, in
which the still-to-video recognition task is casted into Maximum A Posteriori
estimation... | computer science |
29,231 | Synthesising Wider Field Images from Narrow-Field Retinal Video Acquired
Using a Low-Cost Direct Ophthalmoscope (Arclight) Attached to a Smartphone | cs.CV | Access to low cost retinal imaging devices in low and middle income countries
is limited, compromising progress in preventing needless blindness. The
Arclight is a recently developed low-cost solar powered direct ophthalmoscope
which can be attached to the camera of a smartphone to acquire retinal images
and video. How... | computer science |
29,232 | Stereo Matching With Color-Weighted Correlation, Hierarchical Belief
Propagation And Occlusion Handling | cs.CV | In this paper, we contrive a stereo matching algorithm with careful handling
of disparity, discontinuity and occlusion. This algorithm works a worldwide
matching stereo model which is based on minimization of energy. The global
energy comprises two terms, firstly the data term and secondly the smoothness
term. The data... | computer science |
29,233 | Facial Expression Recognition using Visual Saliency and Deep Learning | cs.CV | We have developed a convolutional neural network for the purpose of
recognizing facial expressions in human beings. We have fine-tuned the existing
convolutional neural network model trained on the visual recognition dataset
used in the ILSVRC2012 to two widely used facial expression datasets - CFEE and
RaFD, which whe... | computer science |
29,234 | Cross-view Asymmetric Metric Learning for Unsupervised Person
Re-identification | cs.CV | While metric learning is important for Person re-identification (RE-ID), a
significant problem in visual surveillance for cross-view pedestrian matching,
existing metric models for RE-ID are mostly based on supervised learning that
requires quantities of labeled samples in all pairs of camera views for
training. Howeve... | computer science |
29,235 | Part-to-whole Registration of Histology and MRI using Shape Elements | cs.CV | Image registration between histology and magnetic resonance imaging (MRI) is
a challenging task due to differences in structural content and contrast. Too
thick and wide specimens cannot be processed all at once and must be cut into
smaller pieces. This dramatically increases the complexity of the problem,
since each p... | computer science |
29,236 | One-Shot Concept Learning by Simulating Evolutionary Instinct
Development | cs.CV | Object recognition has become a crucial part of machine learning and computer
vision recently. The current approach to object recognition involves Deep
Learning and uses Convolutional Neural Networks to learn the pixel patterns of
the objects implicitly through backpropagation. However, CNNs require thousands
of exampl... | computer science |
29,237 | ChainerCV: a Library for Deep Learning in Computer Vision | cs.CV | Despite significant progress of deep learning in the field of computer
vision, there has not been a software library that covers these methods in a
unifying manner. We introduce ChainerCV, a software library that is intended to
fill this gap. ChainerCV supports numerous neural network models as well as
software compone... | computer science |
29,238 | An Optimized Union-Find Algorithm for Connected Components Labeling
Using GPUs | cs.CV | In this paper, we report an optimized union-find (UF) algorithm that can
label the connected components on a 2D image efficiently by employing the GPU
architecture. The proposed method contains three phases: UF-based local merge,
boundary analysis, and link. The coarse labeling in local merge reduces the
number atomic ... | computer science |
29,239 | A Probabilistic Quality Representation Approach to Deep Blind Image
Quality Prediction | cs.CV | Blind image quality assessment (BIQA) remains a very challenging problem due
to the unavailability of a reference image. Deep learning based BIQA methods
have been attracting increasing attention in recent years, yet it remains a
difficult task to train a robust deep BIQA model because of the very limited
number of tra... | computer science |
29,240 | Automatic Dataset Augmentation | cs.CV | Large scale image dataset and deep convolutional neural network (DCNN) are
two primary driving forces for the rapid progress made in generic object
recognition tasks in recent years. While lots of network architectures have
been continuously designed to pursue lower error rates, few efforts are devoted
to enlarge exist... | computer science |
29,241 | A Compromise Principle in Deep Monocular Depth Estimation | cs.CV | Monocular depth estimation, which plays a key role in understanding 3D scene
geometry, is fundamentally an ill-posed problem. Existing methods based on deep
convolutional neural networks (DCNNs) have examined this problem by learning
convolutional networks to estimate continuous depth maps from monocular images.
Howeve... | computer science |
29,242 | DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation | cs.CV | DeepPrior is a simple approach based on Deep Learning that predicts the joint
3D locations of a hand given a depth map. Since its publication early 2015, it
has been outperformed by several impressive works. Here we show that with
simple improvements: adding ResNet layers, data augmentation, and better
initial hand loc... | computer science |
29,243 | Automatic Discovery and Geotagging of Objects from Street View Imagery | cs.CV | Many applications such as autonomous navigation, urban planning and asset
monitoring, rely on the availability of accurate information about objects and
their geolocations. In this paper we propose to automatically detect and
compute the GPS coordinates of recurring stationary objects of interest using
street view imag... | computer science |
29,244 | Performance Guaranteed Network Acceleration via High-Order Residual
Quantization | cs.CV | Input binarization has shown to be an effective way for network acceleration.
However, previous binarization scheme could be regarded as simple pixel-wise
thresholding operations (i.e., order-one approximation) and suffers a big
accuracy loss. In this paper, we propose a highorder binarization scheme, which
achieves mo... | computer science |
29,245 | Setting an attention region for convolutional neural networks using
region selective features, for recognition of materials within glass vessels | cs.CV | Convolutional neural networks have emerged as the leading method for the
classification and segmentation of images. In some cases, it is desirable to
focus the attention of the net on a specific region in the image; one such case
is the recognition of the contents of transparent vessels, where the vessel
region in the ... | computer science |
29,246 | Curriculum Learning for Multi-Task Classification of Visual Attributes | cs.CV | Visual attributes, from simple objects (e.g., backpacks, hats) to
soft-biometrics (e.g., gender, height, clothing) have proven to be a powerful
representational approach for many applications such as image description and
human identification. In this paper, we introduce a novel method to combine the
advantages of both... | computer science |
29,247 | Autoencoder with recurrent neural networks for video forgery detection | cs.CV | Video forgery detection is becoming an important issue in recent years,
because modern editing software provide powerful and easy-to-use tools to
manipulate videos. In this paper we propose to perform detection by means of
deep learning, with an architecture based on autoencoders and recurrent neural
networks. A traini... | computer science |
29,248 | 4D Multi-atlas Label Fusion using Longitudinal Images | cs.CV | Longitudinal reproducibility is an essential concern in automated medical
image segmentation, yet has proven to be an elusive objective as manual brain
structure tracings have shown more than 10% variability. To improve
reproducibility, lon-gitudinal segmentation (4D) approaches have been
investigated to reconcile tem-... | computer science |
29,249 | Semantic Texture for Robust Dense Tracking | cs.CV | We argue that robust dense SLAM systems can make valuable use of the layers
of features coming from a standard CNN as a pyramid of `semantic texture' which
is suitable for dense alignment while being much more robust to nuisance
factors such as lighting than raw RGB values. We use a straightforward
Lucas-Kanade formula... | computer science |
29,250 | Reasoning about Fine-grained Attribute Phrases using Reference Games | cs.CV | We present a framework for learning to describe fine-grained visual
differences between instances using attribute phrases. Attribute phrases
capture distinguishing aspects of an object (e.g., "propeller on the nose" or
"door near the wing" for airplanes) in a compositional manner. Instances within
a category can be des... | computer science |
29,251 | Driving Style Analysis Using Primitive Driving Patterns With Bayesian
Nonparametric Approaches | cs.CV | Analysis and recognition of driving styles are profoundly important to
intelligent transportation and vehicle calibration. This paper presents a novel
driving style analysis framework using the primitive driving patterns learned
from naturalistic driving data. In order to achieve this, first, a Bayesian
nonparametric l... | computer science |
29,252 | Deep Learning for Medical Image Analysis | cs.CV | This report describes my research activities in the Hasso Plattner Institute
and summarizes my Ph.D. plan and several novels, end-to-end trainable
approaches for analyzing medical images using deep learning algorithm. In this
report, as an example, we explore different novel methods based on deep
learning for brain abn... | computer science |
29,253 | A simple expression for the map of Asplund's distances with the
multiplicative Logarithmic Image Processing (LIP) law | cs.CV | We introduce a simple expression for the map of Asplund's distances with the
multiplicative Logarithmic Image Processing (LIP) law. It is a difference
between a morphological dilation and a morphological erosion with an additive
structuring function which corresponds to a morphological gradient. | computer science |
29,254 | Learning a 3D descriptor for cross-source point cloud registration from
synthetic data | cs.CV | As the development of 3D sensors, registration of 3D data (e.g. point cloud)
coming from different kind of sensor is dispensable and shows great demanding.
However, point cloud registration between different sensors is challenging
because of the variant of density, missing data, different viewpoint, noise and
outliers,... | computer science |
29,255 | Deep Structure for end-to-end inverse rendering | cs.CV | Inverse rendering in a 3D format denoted to recovering the 3D properties of a
scene given 2D input image(s) and is typically done using 3D Morphable Model
(3DMM) based methods from single view images. These models formulate each face
as a weighted combination of some basis vectors extracted from the training
data. In t... | computer science |
29,256 | A Machine Learning Approach For Identifying Patients with Mild Traumatic
Brain Injury Using Diffusion MRI Modeling | cs.CV | While diffusion MRI has been extremely promising in the study of MTBI,
identifying patients with recent MTBI remains a challenge. The literature is
mixed with regard to localizing injury in these patients, however, gray matter
such as the thalamus and white matter including the corpus callosum and frontal
deep white ma... | computer science |
29,257 | Pix2face: Direct 3D Face Model Estimation | cs.CV | An efficient, fully automatic method for 3D face shape and pose estimation in
unconstrained 2D imagery is presented. The proposed method jointly estimates a
dense set of 3D landmarks and facial geometry using a single pass of a modified
version of the popular "U-Net" neural network architecture. Additionally, we
propos... | computer science |
29,258 | Adaptive SVM+: Learning with Privileged Information for Domain
Adaptation | cs.CV | Incorporating additional knowledge in the learning process can be beneficial
for several computer vision and machine learning tasks. Whether privileged
information originates from a source domain that is adapted to a target domain,
or as additional features available at training time only, using such
privileged (i.e., ... | computer science |
29,259 | Simultaneously Color-Depth Super-Resolution with Conditional Generative
Adversarial Network | cs.CV | Recently, Generative Adversarial Network (GAN) has been found wide
applications in style transfer, image-to-image translation and image
super-resolution. In this paper, a color-depth conditional GAN is proposed to
concurrently resolve the problems of depth super-resolution and color
super-resolution in 3D videos. First... | computer science |
29,260 | Photorealistic Facial Expression Synthesis by the Conditional Difference
Adversarial Autoencoder | cs.CV | Photorealistic facial expression synthesis from single face image can be
widely applied to face recognition, data augmentation for emotion recognition
or entertainment. This problem is challenging, in part due to a paucity of
labeled facial expression data, making it difficult for algorithms to
disambiguate changes due... | computer science |
29,261 | A Greedy Part Assignment Algorithm for Real-time Multi-person 2D Pose
Estimation | cs.CV | Human pose-estimation in a multi-person image involves detection of various
body parts and grouping them into individual person clusters. While the former
task is challenging due to mutual occlusions, the combinatorial complexity of
the latter task is very high. We propose a greedy part assignment algorithm
that exploi... | computer science |
29,262 | Joint Maximum Purity Forest with Application to Image Super-Resolution | cs.CV | In this paper, we propose a novel random-forest scheme, namely Joint Maximum
Purity Forest (JMPF), for classification, clustering, and regression tasks. In
the JMPF scheme, the original feature space is transformed into a compactly
pre-clustered feature space, via a trained rotation matrix. The rotation matrix
is obtai... | computer science |
29,263 | Cascade Residual Learning: A Two-stage Convolutional Neural Network for
Stereo Matching | cs.CV | Leveraging on the recent developments in convolutional neural networks
(CNNs), matching dense correspondence from a stereo pair has been cast as a
learning problem, with performance exceeding traditional approaches. However,
it remains challenging to generate high-quality disparities for the inherently
ill-posed region... | computer science |
29,264 | ScatterNet Hybrid Deep Learning (SHDL) Network For Object Classification | cs.CV | The paper proposes the ScatterNet Hybrid Deep Learning (SHDL) network that
extracts invariant and discriminative image representations for object
recognition. SHDL framework is constructed with a multi-layer ScatterNet
front-end, an unsupervised learning middle, and a supervised learning back-end
module. Each layer of ... | computer science |
29,265 | Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural
Networks - Preliminary Results | cs.CV | Radiology reports are an important means of communication between
radiologists and other physicians. These reports express a radiologist's
interpretation of a medical imaging examination and are critical in
establishing a diagnosis and formulating a treatment plan. In this paper, we
propose a Bi-directional convolution... | computer science |
29,266 | Two-stream Flow-guided Convolutional Attention Networks for Action
Recognition | cs.CV | This paper proposes a two-stream flow-guided convolutional attention networks
for action recognition in videos. The central idea is that optical flows, when
properly compensated for the camera motion, can be used to guide attention to
the human foreground. We thus develop cross-link layers from the temporal
network (tr... | computer science |
29,267 | Texture and Structure Incorporated ScatterNet Hybrid Deep Learning
Network (TS-SHDL) For Brain Matter Segmentation | cs.CV | Automation of brain matter segmentation from MR images is a challenging task
due to the irregular boundaries between the grey and white matter regions. In
addition, the presence of intensity inhomogeneity in the MR images further
complicates the problem. In this paper, we propose a texture and vesselness
incorporated v... | computer science |
29,268 | Disguised Face Identification (DFI) with Facial KeyPoints using Spatial
Fusion Convolutional Network | cs.CV | Disguised face identification (DFI) is an extremely challenging problem due
to the numerous variations that can be introduced using different disguises.
This paper introduces a deep learning framework to first detect 14 facial
key-points which are then utilized to perform disguised face identification.
Since the traini... | computer science |
29,269 | Adversarial nets with perceptual losses for text-to-image synthesis | cs.CV | Recent approaches in generative adversarial networks (GANs) can automatically
synthesize realistic images from descriptive text. Despite the overall fair
quality, the generated images often expose visible flaws that lack structural
definition for an object of interest. In this paper, we aim to extend state of
the art f... | computer science |
29,270 | Learning Invariant Riemannian Geometric Representations Using Deep Nets | cs.CV | Non-Euclidean constraints are inherent in many kinds of data in computer
vision and machine learning, typically as a result of specific invariance
requirements that need to be respected during high-level inference. Often,
these geometric constraints can be expressed in the language of Riemannian
geometry, where convent... | computer science |
29,271 | Action Classification and Highlighting in Videos | cs.CV | Inspired by recent advances in neural machine translation, that jointly align
and translate using encoder-decoder networks equipped with attention, we
propose an attentionbased LSTM model for human activity recognition. Our model
jointly learns to classify actions and highlight frames associated with the
action, by att... | computer science |
29,272 | Learning a Generative Adversarial Network for High Resolution Artwork
Synthesis | cs.CV | Artwork is a mode of creative expression and this paper is particularly
interested in investigating if machine can learn and synthetically create
artwork that are usually non- figurative and structured abstract. To this end,
we propose an extension to the Generative Adversarial Network (GAN), namely as
the ArtGAN to sy... | computer science |
29,273 | Video Summarization with Attention-Based Encoder-Decoder Networks | cs.CV | This paper addresses the problem of supervised video summarization by
formulating it as a sequence-to-sequence learning problem, where the input is a
sequence of original video frames, the output is a keyshot sequence. Our key
idea is to learn a deep summarization network with attention mechanism to mimic
the way of se... | computer science |
29,274 | Fast Landmark Localization with 3D Component Reconstruction and CNN for
Cross-Pose Recognition | cs.CV | Two approaches are proposed for cross-pose face recognition, one is based on
the 3D reconstruction of facial components and the other is based on the deep
Convolutional Neural Network (CNN). Unlike most 3D approaches that consider
holistic faces, the proposed approach considers 3D facial components. It
segments a 2D ga... | computer science |
29,275 | ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17) | cs.CV | Chinese is the most widely used language in the world. Algorithms that read
Chinese text in natural images facilitate applications of various kinds.
Despite the large potential value, datasets and competitions in the past
primarily focus on English, which bares very different characteristics than
Chinese. This report i... | computer science |
29,276 | ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose
Estimation | cs.CV | To be robust to illumination changes when detecting objects in images, the
current trend is to train a Deep Network with training images captured under
many different lighting conditions. Unfortunately, creating such a training set
is very cumbersome, or sometimes even impossible, for some applications such as
3D pose ... | computer science |
29,277 | Automatic Semantic Style Transfer using Deep Convolutional Neural
Networks and Soft Masks | cs.CV | This paper presents an automatic image synthesis method to transfer the style
of an example image to a content image. When standard neural style transfer
approaches are used, the textures and colours in different semantic regions of
the style image are often applied inappropriately to the content image,
ignoring its se... | computer science |
29,278 | Neural Class-Specific Regression for face verification | cs.CV | Face verification is a problem approached in the literature mainly using
nonlinear class-specific subspace learning techniques. While it has been shown
that kernel-based Class-Specific Discriminant Analysis is able to provide
excellent performance in small- and medium-scale face verification problems,
its application i... | computer science |
29,279 | On Boosting, Tug of War, and Lexicographic Programming | cs.CV | Despite the large amount of research effort dedicated to adapting boosting
for imbalanced classification, boosting methods are yet to be satisfactorily
immune to class imbalance, especially for multi-class problems, due to the
long-standing reliance on expensive cost set tuning. We show that the
assignment of weights t... | computer science |
29,280 | Quantifying Facial Age by Posterior of Age Comparisons | cs.CV | We introduce a novel approach for annotating large quantity of in-the-wild
facial images with high-quality posterior age distribution as labels. Each
posterior provides a probability distribution of estimated ages for a face. Our
approach is motivated by observations that it is easier to distinguish who is
the older of... | computer science |
29,281 | Sparse-then-Dense Alignment based 3D Map Reconstruction Method for
Endoscopic Capsule Robots | cs.CV | Since the development of capsule endoscopcy technology, substantial progress
were made in converting passive capsule endoscopes to robotic active capsule
endoscopes which can be controlled by the doctor. However, robotic capsule
endoscopy still has some challenges. In particular, the use of such devices to
generate a p... | computer science |
29,282 | Inferring Human Activities Using Robust Privileged Probabilistic
Learning | cs.CV | Classification models may often suffer from "structure imbalance" between
training and testing data that may occur due to the deficient data collection
process. This imbalance can be represented by the learning using privileged
information (LUPI) paradigm. In this paper, we present a supervised
probabilistic classifica... | computer science |
29,283 | 3D Visual Perception for Self-Driving Cars using a Multi-Camera System:
Calibration, Mapping, Localization, and Obstacle Detection | cs.CV | Cameras are a crucial exteroceptive sensor for self-driving cars as they are
low-cost and small, provide appearance information about the environment, and
work in various weather conditions. They can be used for multiple purposes such
as visual navigation and obstacle detection. We can use a surround multi-camera
syste... | computer science |
29,284 | Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs
using Deep Learning | cs.CV | Traditionally, medical discoveries are made by observing associations and
then designing experiments to test these hypotheses. However, observing and
quantifying associations in images can be difficult because of the wide variety
of features, patterns, colors, values, shapes in real data. In this paper, we
use deep lea... | computer science |
29,285 | Multi-task Dictionary Learning based Convolutional Neural Network for
Computer aided Diagnosis with Longitudinal Images | cs.CV | Algorithmic image-based diagnosis and prognosis of neurodegenerative diseases
on longitudinal data has drawn great interest from computer vision researchers.
The current state-of-the-art models for many image classification tasks are
based on the Convolutional Neural Networks (CNN). However, a key challenge in
applying... | computer science |
29,286 | Learning Inference Models for Computer Vision | cs.CV | Computer vision can be understood as the ability to perform inference on
image data. Breakthroughs in computer vision technology are often marked by
advances in inference techniques. This thesis proposes novel inference schemes
and demonstrates applications in computer vision. We propose inference
techniques for both g... | computer science |
29,287 | Exact Blur Measure Outperforms Conventional Learned Features for Depth
Finding | cs.CV | Image analysis methods that are based on exact blur values are faced with the
computational complexities due to blur measurement error. This atmosphere
encourages scholars to look for handcrafted and learned features for finding
depth from a single image. This paper introduces a novel exact realization for
blur measure... | computer science |
29,288 | Single Shot Text Detector with Regional Attention | cs.CV | We present a novel single-shot text detector that directly outputs word-level
bounding boxes in a natural image. We propose an attention mechanism which
roughly identifies text regions via an automatically learned attentional map.
This substantially suppresses background interference in the convolutional
features, whic... | computer science |
29,289 | Context Based Visual Content Verification | cs.CV | In this paper the intermediary visual content verification method based on
multi-level co-occurrences is studied. The co-occurrence statistics are in
general used to determine relational properties between objects based on
information collected from data. As such these measures are heavily subject to
relative number of... | computer science |
29,290 | Reasoning with shapes: profiting cognitive susceptibilities to infer
linear mapping transformations between shapes | cs.CV | Visual information plays an indispensable role in our daily interactions with
environment. Such information is manipulated for a wide range of purposes
spanning from basic object and material perception to complex gesture
interpretations. There have been novel studies in cognitive science for
in-depth understanding of ... | computer science |
29,291 | Effective Use of Dilated Convolutions for Segmenting Small Object
Instances in Remote Sensing Imagery | cs.CV | Thanks to recent advances in CNNs, solid improvements have been made in
semantic segmentation of high resolution remote sensing imagery. However, most
of the previous works have not fully taken into account the specific
difficulties that exist in remote sensing tasks. One of such difficulties is
that objects are small ... | computer science |
29,292 | Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration | cs.CV | Hyperspectral imaging, providing abundant spatial and spectral information
simultaneously, has attracted a lot of interest in recent years. Unfortunately,
due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to
various degradations, such noises (random noise, HSI denoising), blurs
(Gaussian and ... | computer science |
29,293 | DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land
Segmentation | cs.CV | Semantic segmentation is a fundamental research in remote sensing image
processing. Because of the complex maritime environment, the sea-land
segmentation is a challenging task. Although the neural network has achieved
excellent performance in semantic segmentation in the last years, there are a
few of works using CNN ... | computer science |
29,294 | Too Far to See? Not Really! --- Pedestrian Detection with Scale-aware
Localization Policy | cs.CV | A major bottleneck of pedestrian detection lies on the sharp performance
deterioration in the presence of small-size pedestrians that are relatively far
from the camera. Motivated by the observation that pedestrians of disparate
spatial scales exhibit distinct visual appearances, we propose in this paper an
active pede... | computer science |
29,295 | Adversarial Networks for Spatial Context-Aware Spectral Image
Reconstruction from RGB | cs.CV | Hyperspectral signal reconstruction aims at recovering the original spectral
input that produced a certain trichromatic (RGB) response from a capturing
device or observer. Given the heavily underconstrained, non-linear nature of
the problem, traditional techniques leverage different statistical properties
of the spectr... | computer science |
29,296 | A Comprehensive Survey of Deep Learning in Remote Sensing: Theories,
Tools and Challenges for the Community | cs.CV | In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inev... | computer science |
29,297 | Visual-textual Attention Driven Fine-grained Representation Learning | cs.CV | Fine-grained image classification is to recognize hundreds of subcategories
belonging to the same basic-level category, which is a highly challenging task
due to the quite subtle visual distinctions among similar subcategories. Most
existing methods generally learn part detectors to discover discriminative
regions for ... | computer science |
29,298 | Automatic Brain Tumor Segmentation using Cascaded Anisotropic
Convolutional Neural Networks | cs.CV | A cascade of fully convolutional neural networks is proposed to segment
multi-modal Magnetic Resonance (MR) images with brain tumor into background and
three hierarchical regions: whole tumor, tumor core and enhancing tumor core.
The cascade is designed to decompose the multi-class segmentation problem into
a sequence ... | computer science |
29,299 | End-to-End Multi-View Lipreading | cs.CV | Non-frontal lip views contain useful information which can be used to enhance
the performance of frontal view lipreading. However, the vast majority of
recent lipreading works, including the deep learning approaches which
significantly outperform traditional approaches, have focused on frontal mouth
images. As a conseq... | computer science |
29,300 | Unsupervised learning through one-shot image-based shape reconstruction | cs.CV | Objects are three-dimensional entities, but visual observations are largely
2D. Inferring 3D properties from individual 2D views is thus a generically
useful skill that is critical to object perception. We ask the question: can we
learn useful image representations by explicitly training a system to infer 3D
shape from... | computer science |
29,301 | Learning to Look Around: Intelligently Exploring Unseen Environments for
Unknown Tasks | cs.CV | It is common to implicitly assume access to intelligently captured inputs
(e.g., photos from a human photographer), yet autonomously capturing good
observations is itself a major challenge. We address the problem of learning to
look around: if a visual agent has the ability to voluntarily acquire new views
to observe i... | computer science |
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