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29,102 | Situation Recognition with Graph Neural Networks | cs.CV | We address the problem of recognizing situations in images. Given an image,
the task is to predict the most salient verb (action), and fill its semantic
roles such as who is performing the action, what is the source and target of
the action, etc. Different verbs have different roles (e.g. attacking has
weapon), and eac... | computer science |
29,103 | Image Augmentation using Radial Transform for Training Deep Neural
Networks | cs.CV | Deep learning models have a large number of free parameters that must be
estimated by efficient training of the models on a large number of training
data samples to increase their generalization performance. In real-world
applications, the data available to train these networks is often limited or
imbalanced. We propos... | computer science |
29,104 | Deep Edge-Aware Saliency Detection | cs.CV | There has been profound progress in visual saliency thanks to the deep
learning architectures, however, there still exist three major challenges that
hinder the detection performance for scenes with complex compositions, multiple
salient objects, and salient objects of diverse scales. In particular, output
maps of the ... | computer science |
29,105 | Dockerface: an easy to install and use Faster R-CNN face detector in a
Docker container | cs.CV | Face detection is a very important task and a necessary pre-processing step
for many applications such as facial landmark detection, pose estimation,
sentiment analysis and face recognition. Not only is face detection an
important pre-processing step in computer vision applications but also in
computational psychology,... | computer science |
29,106 | Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two
Perspective Frames | cs.CV | This paper proposes a new approach for monocular dense 3D reconstruction of a
complex dynamic scene from two perspective frames. By applying superpixel
over-segmentation to the image, we model a generically dynamic (hence
non-rigid) scene with a piecewise planar and rigid approximation. In this way,
we reduce the dynam... | computer science |
29,107 | Bringing Background into the Foreground: Making All Classes Equal in
Weakly-supervised Video Semantic Segmentation | cs.CV | Pixel-level annotations are expensive and time-consuming to obtain. Hence,
weak supervision using only image tags could have a significant impact in
semantic segmentation. Recent years have seen great progress in
weakly-supervised semantic segmentation, whether from a single image or from
videos. However, most existing... | computer science |
29,108 | Knock-Knock: Acoustic Object Recognition by using Stacked Denoising
Autoencoders | cs.CV | This paper presents a successful application of deep learning for object
recognition based on acoustic data. The shortcomings of previously employed
approaches where handcrafted features describing the acoustic data are being
used, include limiting the capability of the found representation to be widely
applicable and ... | computer science |
29,109 | Learning with Rethinking: Recurrently Improving Convolutional Neural
Networks through Feedback | cs.CV | Recent years have witnessed the great success of convolutional neural network
(CNN) based models in the field of computer vision. CNN is able to learn
hierarchically abstracted features from images in an end-to-end training
manner. However, most of the existing CNN models only learn features through a
feedforward struc... | computer science |
29,110 | Pathological Pulmonary Lobe Segmentation from CT Images using
Progressive Holistically Nested Neural Networks and Random Walker | cs.CV | Automatic pathological pulmonary lobe segmentation(PPLS) enables regional
analyses of lung disease, a clinically important capability. Due to often
incomplete lobe boundaries, PPLS is difficult even for experts, and most prior
art requires inference from contextual information. To address this, we propose
a novel PPLS ... | computer science |
29,111 | DesnowNet: Context-Aware Deep Network for Snow Removal | cs.CV | Existing learning-based atmospheric particle-removal approaches such as those
used for rainy and hazy images are designed with strong assumptions regarding
spatial frequency, trajectory, and translucency. However, the removal of snow
particles is more complicated because it possess the additional attributes of
particle... | computer science |
29,112 | Artistic style transfer for videos and spherical images | cs.CV | Manually re-drawing an image in a certain artistic style takes a professional
artist a long time. Doing this for a video sequence single-handedly is beyond
imagination. We present two computational approaches that transfer the style
from one image (for example, a painting) to a whole video sequence. In our
first approa... | computer science |
29,113 | Improved Regularization of Convolutional Neural Networks with Cutout | cs.CV | Convolutional neural networks are capable of learning powerful
representational spaces, which are necessary for tackling complex learning
tasks. However, due to the model capacity required to capture such
representations, they are often susceptible to overfitting and therefore
require proper regularization in order to ... | computer science |
29,114 | Segmentation-Aware Convolutional Networks Using Local Attention Masks | cs.CV | We introduce an approach to integrate segmentation information within a
convolutional neural network (CNN). This counter-acts the tendency of CNNs to
smooth information across regions and increases their spatial precision. To
obtain segmentation information, we set up a CNN to provide an embedding space
where region co... | computer science |
29,115 | A Novel data Pre-processing method for multi-dimensional and non-uniform
data | cs.CV | We are in the era of data analytics and data science which is on full bloom.
There is abundance of all kinds of data for example biometrics based data,
satellite images data, chip-seq data, social network data, sensor based data
etc. from a variety of sources. This data abundance is the result of the fact
that storage ... | computer science |
29,116 | Convolutional Neural Networks for Non-iterative Reconstruction of
Compressively Sensed Images | cs.CV | Traditional algorithms for compressive sensing recovery are computationally
expensive and are ineffective at low measurement rates. In this work, we
propose a data driven non-iterative algorithm to overcome the shortcomings of
earlier iterative algorithms. Our solution, ReconNet, is a deep neural network,
whose paramet... | computer science |
29,117 | Sequence-to-Label Script Identification for Multilingual OCR | cs.CV | We describe a novel line-level script identification method. Previous work
repurposed an OCR model generating per-character script codes, counted to
obtain line-level script identification. This has two shortcomings. First, as a
sequence-to-sequence model it is more complex than necessary for the
sequence-to-label prob... | computer science |
29,118 | Acoustic Feature Learning via Deep Variational Canonical Correlation
Analysis | cs.CV | We study the problem of acoustic feature learning in the setting where we
have access to another (non-acoustic) modality for feature learning but not at
test time. We use deep variational canonical correlation analysis (VCCA), a
recently proposed deep generative method for multi-view representation
learning. We also ex... | computer science |
29,119 | DeepRebirth: Accelerating Deep Neural Network Execution on Mobile
Devices | cs.CV | Deploying deep neural networks on mobile devices is a challenging task.
Current model compression methods such as matrix decomposition effectively
reduce the deployed model size, but still cannot satisfy real-time processing
requirement. This paper first discovers that the major obstacle is the
excessive execution time... | computer science |
29,120 | An Improved Neural Segmentation Method Based on U-NET | cs.CV | Neural segmentation has a great impact on the smooth implementation of local
anesthesia surgery. At present, the network for the segmentation includes U-NET
[1] and SegNet [2]. U-NET network has short training time and less training
parameters, but the depth is not deep enough. SegNet network has deeper
structure, but ... | computer science |
29,121 | Efficiently Tracking Homogeneous Regions in Multichannel Images | cs.CV | We present a method for tracking Maximally Stable Homogeneous Regions (MSHR)
in images with an arbitrary number of channels. MSHR are conceptionally very
similar to Maximally Stable Extremal Regions (MSER) and Maximally Stable Color
Regions (MSCR), but can also be applied to hyperspectral and color images while
remaini... | computer science |
29,122 | Language Identification Using Deep Convolutional Recurrent Neural
Networks | cs.CV | Language Identification (LID) systems are used to classify the spoken
language from a given audio sample and are typically the first step for many
spoken language processing tasks, such as Automatic Speech Recognition (ASR)
systems. Without automatic language detection, speech utterances cannot be
parsed correctly and ... | computer science |
29,123 | GSLAM: Initialization-robust Monocular Visual SLAM via Global
Structure-from-Motion | cs.CV | Many monocular visual SLAM algorithms are derived from incremental
structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM
method which integrates recent advances made in global SfM. In particular, we
present two main contributions to visual SLAM. First, we solve the visual
odometry problem by a ... | computer science |
29,124 | A deep architecture for unified aesthetic prediction | cs.CV | Image aesthetics has become an important criterion for visual content
curation on social media sites and media content repositories. Previous work on
aesthetic prediction models in the computer vision community has focused on
aesthetic score prediction or binary image labeling. However, raw aesthetic
annotations are in... | computer science |
29,125 | Random Erasing Data Augmentation | cs.CV | In this paper, we introduce Random Erasing, a new data augmentation method
for training the convolutional neural network (CNN). In training, Random
Erasing randomly selects a rectangle region in an image and erases its pixels
with random values. In this process, training images with various levels of
occlusion are gene... | computer science |
29,126 | Multi-View Stereo with Single-View Semantic Mesh Refinement | cs.CV | While 3D reconstruction is a well-established and widely explored research
topic, semantic 3D reconstruction has only recently witnessed an increasing
share of attention from the Computer Vision community. Semantic annotations
allow in fact to enforce strong class-dependent priors, as planarity for ground
and walls, wh... | computer science |
29,127 | Stacked Deconvolutional Network for Semantic Segmentation | cs.CV | Recent progress in semantic segmentation has been driven by improving the
spatial resolution under Fully Convolutional Networks (FCNs). To address this
problem, we propose a Stacked Deconvolutional Network (SDN) for semantic
segmentation. In SDN, multiple shallow deconvolutional networks, which are
called as SDN units,... | computer science |
29,128 | Free Space Estimation using Occupancy Grids and Dynamic Object Detection | cs.CV | In this paper we present an approach to estimate Free Space from a Stereo
image pair using stochastic occupancy grids. We do this in the domain of
autonomous driving on the famous benchmark dataset KITTI. Later based on the
generated occupancy grid we match 2 image sequences to compute the top view
representation of th... | computer science |
29,129 | Salt-n-pepper noise filtering using Cellular Automata | cs.CV | Cellular Automata (CA) have been considered one of the most pronounced
parallel computational tools in the recent era of nature and bio-inspired
computing. Taking advantage of their local connectivity, the simplicity of
their design and their inherent parallelism, CA can be effectively applied to
many image processing ... | computer science |
29,130 | Deep Neural Network Capacity | cs.CV | In recent years, deep neural network exhibits its powerful superiority on
information discrimination in many computer vision applications. However, the
capacity of deep neural network architecture is still a mystery to the
researchers. Intuitively, larger capacity of neural network can always deposit
more information t... | computer science |
29,131 | ConvNet Architecture Search for Spatiotemporal Feature Learning | cs.CV | Learning image representations with ConvNets by pre-training on ImageNet has
proven useful across many visual understanding tasks including object
detection, semantic segmentation, and image captioning. Although any image
representation can be applied to video frames, a dedicated spatiotemporal
representation is still ... | computer science |
29,132 | Importance of Image Enhancement Techniques in Color Image Segmentation:
A Comprehensive and Comparative Study | cs.CV | Color image segmentation is a very emerging research topic in the area of
color image analysis and pattern recognition. Many state-of-the-art algorithms
have been developed for this purpose. But, often the segmentation results of
these algorithms seem to be suffering from miss-classifications and
over-segmentation. The... | computer science |
29,133 | Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark
Performances and Survey | cs.CV | Hyperspectral unmixing (HU) is a very useful and increasingly popular
preprocessing step for a wide range of hyperspectral applications. However, the
HU research has been constrained a lot by three factors: (a) the number of
hyperspectral images (especially the ones with ground truths) are very limited;
(b) the ground ... | computer science |
29,134 | Deep Scene Text Detection with Connected Component Proposals | cs.CV | A growing demand for natural-scene text detection has been witnessed by the
computer vision community since text information plays a significant role in
scene understanding and image indexing. Deep neural networks are being used due
to their strong capabilities of pixel-wise classification or word localization,
similar... | computer science |
29,135 | Pixel-Level Matching for Video Object Segmentation using Convolutional
Neural Networks | cs.CV | We propose a novel video object segmentation algorithm based on pixel-level
matching using Convolutional Neural Networks (CNN). Our network aims to
distinguish the target area from the background on the basis of the pixel-level
similarity between two object units. The proposed network represents a target
object using f... | computer science |
29,136 | High Efficient Reconstruction of Single-shot T2 Mapping from
OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network | cs.CV | Purpose: An end-to-end deep convolutional neural network (CNN) based on deep
residual network (ResNet) was proposed to efficiently reconstruct reliable T2
mapping from single-shot OverLapping-Echo Detachment (OLED) planar imaging.
Methods: The training dataset was obtained from simulations carried out on
SPROM software... | computer science |
29,137 | Energy-based Models for Video Anomaly Detection | cs.CV | Automated detection of abnormalities in data has been studied in research
area in recent years because of its diverse applications in practice including
video surveillance, industrial damage detection and network intrusion
detection. However, building an effective anomaly detection system is a
non-trivial task since it... | computer science |
29,138 | Deep Neural Network with l2-norm Unit for Brain Lesions Detection | cs.CV | Automated brain lesions detection is an important and very challenging
clinical diagnostic task because the lesions have different sizes, shapes,
contrasts, and locations. Deep Learning recently has shown promising progress
in many application fields, which motivates us to apply this technology for
such important probl... | computer science |
29,139 | Conditional Adversarial Network for Semantic Segmentation of Brain Tumor | cs.CV | Automated medical image analysis has a significant value in diagnosis and
treatment of lesions. Brain tumors segmentation has a special importance and
difficulty due to the difference in appearances and shapes of the different
tumor regions in magnetic resonance images. Additionally, the data sets are
heterogeneous and... | computer science |
29,140 | FaceBoxes: A CPU Real-time Face Detector with High Accuracy | cs.CV | Although tremendous strides have been made in face detection, one of the
remaining open challenges is to achieve real-time speed on the CPU as well as
maintain high performance, since effective models for face detection tend to be
computationally prohibitive. To address this challenge, we propose a novel face
detector,... | computer science |
29,141 | S$^3$FD: Single Shot Scale-invariant Face Detector | cs.CV | This paper presents a real-time face detector, named Single Shot
Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various
scales of faces with a single deep neural network, especially for small faces.
Specifically, we try to solve the common problem that anchor-based detectors
deteriorate dramatica... | computer science |
29,142 | Robust Registration and Geometry Estimation from Unstructured Facial
Scans | cs.CV | Commercial off the shelf (COTS) 3D scanners are capable of generating point
clouds covering visible portions of a face with sub-millimeter accuracy at
close range, but lack the coverage and specialized anatomic registration
provided by more expensive 3D facial scanners. We demonstrate an effective
pipeline for joint al... | computer science |
29,143 | MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion
Estimation | cs.CV | Optical flow estimation is one of the most studied problems in computer
vision, yet recent benchmark datasets continue to reveal problem areas of
today's approaches. Occlusions have remained one of the key challenges. In this
paper, we propose a symmetric optical flow method to address the well-known
chicken-and-egg re... | computer science |
29,144 | Learning a Multi-View Stereo Machine | cs.CV | We present a learnt system for multi-view stereopsis. In contrast to recent
learning based methods for 3D reconstruction, we leverage the underlying 3D
geometry of the problem through feature projection and unprojection along
viewing rays. By formulating these operations in a differentiable manner, we
are able to learn... | computer science |
29,145 | Deformable Modeling for Human Body Acquired from Depth Sensors | cs.CV | This paper presents a novel approach to reconstruct complete 3D deformable
models over time by a single depth camera. These are the steps employed for
deforming objects from single depth camera. The partial surfaces reconstructed
from various times of capture are assembled together to form a complete 3D
surface. A mesh... | computer science |
29,146 | Simultaneous Detection and Quantification of Retinal Fluid with Deep
Learning | cs.CV | We propose a new deep learning approach for automatic detection and
segmentation of fluid within retinal OCT images. The proposed framework
utilizes both ResNet and Encoder-Decoder neural network architectures. When
training the network, we apply a novel data augmentation method called myopic
warping together with stan... | computer science |
29,147 | Eigen Evolution Pooling for Human Action Recognition | cs.CV | We introduce Eigen Evolution Pooling, an efficient method to aggregate a
sequence of feature vectors. Eigen evolution pooling is designed to produce
compact feature representations for a sequence of feature vectors, while
maximally preserving as much information about the sequence as possible,
especially the temporal e... | computer science |
29,148 | Dilated Deep Residual Network for Image Denoising | cs.CV | Variations of deep neural networks such as convolutional neural network (CNN)
have been successfully applied to image denoising. The goal is to automatically
learn a mapping from a noisy image to a clean image given training data
consisting of pairs of noisy and clean images. Most existing CNN models for
image denoisin... | computer science |
29,149 | Towards Interpretable Deep Neural Networks by Leveraging Adversarial
Examples | cs.CV | Deep neural networks (DNNs) have demonstrated impressive performance on a
wide array of tasks, but they are usually considered opaque since internal
structure and learned parameters are not interpretable. In this paper, we
re-examine the internal representations of DNNs using adversarial images, which
are generated by ... | computer science |
29,150 | Towards the Automatic Anime Characters Creation with Generative
Adversarial Networks | cs.CV | Automatic generation of facial images has been well studied after the
Generative Adversarial Network (GAN) came out. There exists some attempts
applying the GAN model to the problem of generating facial images of anime
characters, but none of the existing work gives a promising result. In this
work, we explore the trai... | computer science |
29,151 | Mesh-based 3D Textured Urban Mapping | cs.CV | In the era of autonomous driving, urban mapping represents a core step to let
vehicles interact with the urban context. Successful mapping algorithms have
been proposed in the last decade building the map leveraging on data from a
single sensor. The focus of the system presented in this paper is twofold: the
joint esti... | computer science |
29,152 | Spotting Separator Points at Line Terminals in Compressed Document
Images for Text-line Segmentation | cs.CV | Line separators are used to segregate text-lines from one another in document
image analysis. Finding the separator points at every line terminal in a
document image would enable text-line segmentation. In particular, identifying
the separators in handwritten text could be a thrilling exercise. Obviously it
would be ch... | computer science |
29,153 | Self-explanatory Deep Salient Object Detection | cs.CV | Salient object detection has seen remarkable progress driven by deep learning
techniques. However, most of deep learning based salient object detection
methods are black-box in nature and lacking in interpretability. This paper
proposes the first self-explanatory saliency detection network that explicitly
exploits low-... | computer science |
29,154 | Winqi: A System for 6D Localization and SLAM Augmentation Using
Wideangle Optics and Coded Light Beacons | cs.CV | Simultaneous Localization and Mapping (SLAM) systems use commodity
visible/near visible digital sensors coupled with processing units that detect,
recognize and track image points in a camera stream. These systems are cheap,
fast and make use of readily available camera technologies. However, SLAM
systems suffer from i... | computer science |
29,155 | 3D Pose Regression using Convolutional Neural Networks | cs.CV | 3D pose estimation is a key component of many important computer vision tasks
such as autonomous navigation and 3D scene understanding. Most state-of-the-art
approaches to 3D pose estimation solve this problem as a pose-classification
problem in which the pose space is discretized into bins and a CNN classifier
is used... | computer science |
29,156 | Discovery of Visual Semantics by Unsupervised and Self-Supervised
Representation Learning | cs.CV | The success of deep learning in computer vision is rooted in the ability of
deep networks to scale up model complexity as demanded by challenging visual
tasks. As complexity is increased, so is the need for large amounts of labeled
data to train the model. This is associated with a costly human annotation
effort. To ad... | computer science |
29,157 | Visual Forecasting by Imitating Dynamics in Natural Sequences | cs.CV | We introduce a general framework for visual forecasting, which directly
imitates visual sequences without additional supervision. As a result, our
model can be applied at several semantic levels and does not require any domain
knowledge or handcrafted features. We achieve this by formulating visual
forecasting as an in... | computer science |
29,158 | High Voltage Insulator Surface Evaluation Using Image Processing | cs.CV | High voltage insulators are widely deployed in power systems to isolate the
live- and dead-part of overhead lines as well as to support the power line
conductors mechanically. Permanent, secure and safe operation of power
transmission lines require that the high voltage insulators are inspected and
monitor, regularly. ... | computer science |
29,159 | UE4Sim: A Photo-Realistic Simulator for Computer Vision Applications | cs.CV | We present a photo-realistic training and evaluation simulator (UE4Sim) with
extensive applications across various fields of computer vision. Built on top
of the Unreal Engine, the simulator integrates full featured physics based
cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse
urban and subu... | computer science |
29,160 | Teaching UAVs to Race Using UE4Sim | cs.CV | Automating the navigation of unmanned aerial vehicles (UAVs) in diverse
scenarios has gained much attention in the recent years. However, teaching UAVs
to fly in challenging environments remains an unsolved problem, mainly due to
the lack of data for training. In this paper, we develop a photo-realistic
simulator that ... | computer science |
29,161 | Computer-aided diagnosis of lung nodule using gradient tree boosting and
Bayesian optimization | cs.CV | We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule
classification focusing on (i) usefulness of gradient tree boosting (XGBoost)
and (ii) effectiveness of parameter optimization using Bayesian optimization
(Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung
cancers and 37 b... | computer science |
29,162 | Incremental Import Vector Machines for Classifying Hyperspectral Data | cs.CV | In this paper we propose an incremental learning strategy for import vector
machines (IVM), which is a sparse kernel logistic regression approach. We use
the procedure for the concept of self-training for sequential classification of
hyperspectral data. The strategy comprises the inclusion of new training
samples to in... | computer science |
29,163 | Applying Data Augmentation to Handwritten Arabic Numeral Recognition
Using Deep Learning Neural Networks | cs.CV | Handwritten character recognition has been the center of research and a
benchmark problem in the sector of pattern recognition and artificial
intelligence, and it continues to be a challenging research topic. Due to its
enormous application many works have been done in this field focusing on
different languages. Arabic... | computer science |
29,164 | Shapelet-based Sparse Representation for Landcover Classification of
Hyperspectral Images | cs.CV | This paper presents a sparse representation-based classification approach
with a novel dictionary construction procedure. By using the constructed
dictionary sophisticated prior knowledge about the spatial nature of the image
can be integrated. The approach is based on the assumption that each image
patch can be factor... | computer science |
29,165 | An Efficient Single Chord-based Accumulation Technique (SCA) to Detect
More Reliable Corners | cs.CV | Corner detection is a vital operation in numerous computer vision
applications. The Chord-to-Point Distance Accumulation (CPDA) detector is
recognized as the contour-based corner detector producing the lowest
localization error while localizing corners in an image. However, in our
experiment part, we demonstrate that C... | computer science |
29,166 | Attentive Semantic Video Generation using Captions | cs.CV | This paper proposes a network architecture to perform variable length
semantic video generation using captions. We adopt a new perspective towards
video generation where we allow the captions to be combined with the long-term
and short-term dependencies between video frames and thus generate a video in
an incremental m... | computer science |
29,167 | Joint Multi-view Face Alignment in the Wild | cs.CV | The de facto algorithm for facial landmark estimation involves running a face
detector with a subsequent deformable model fitting on the bounding box. This
encompasses two basic problems: i) the detection and deformable fitting steps
are performed independently, while the detector might not provide best-suited
initiali... | computer science |
29,168 | Distantly Supervised Road Segmentation | cs.CV | We present an approach for road segmentation that only requires image-level
annotations at training time. We leverage distant supervision, which allows us
to train our model using images that are different from the target domain.
Using large publicly available image databases as distant supervisors, we
develop a simple... | computer science |
29,169 | e-Counterfeit: a mobile-server platform for document counterfeit
detection | cs.CV | This paper presents a novel application to detect counterfeit identity
documents forged by a scan-printing operation. Texture analysis approaches are
proposed to extract validation features from security background that is
usually printed in documents as IDs or banknotes. The main contribution of this
work is the end-t... | computer science |
29,170 | Revisiting knowledge transfer for training object class detectors | cs.CV | We propose to revisit knowledge transfer for training object detectors on
target classes from weakly supervised training images, helped by a set of
source classes with bounding-box annotations. We present a unified knowledge
transfer framework based on training a single neural network multi-class object
detector over a... | computer science |
29,171 | Segmentation of retinal cysts from Optical Coherence Tomography volumes
via selective enhancement | cs.CV | Automated and accurate segmentation of cystoid structures in Optical
Coherence Tomography (OCT) is of interest in the early detection of retinal
diseases. It is, however, a challenging task. We propose a novel method for
localizing cysts in 3D OCT volumes. The proposed work is biologically inspired
and based on selecti... | computer science |
29,172 | Recognizing Involuntary Actions from 3D Skeleton Data Using Body States | cs.CV | Human action recognition has been one of the most active fields of research
in computer vision for last years. Two dimensional action recognition methods
are facing serious challenges such as occlusion and missing the third dimension
of data. Development of depth sensors has made it feasible to track positions
of human... | computer science |
29,173 | Employing Weak Annotations for Medical Image Analysis Problems | cs.CV | To efficiently establish training databases for machine learning methods,
collaborative and crowdsourcing platforms have been investigated to
collectively tackle the annotation effort. However, when this concept is ported
to the medical imaging domain, reading expertise will have a direct impact on
the annotation accur... | computer science |
29,174 | Learning Spread-out Local Feature Descriptors | cs.CV | We propose a simple, yet powerful regularization technique that can be used
to significantly improve both the pairwise and triplet losses in learning local
feature descriptors. The idea is that in order to fully utilize the expressive
power of the descriptor space, good local feature descriptors should be
sufficiently ... | computer science |
29,175 | STNet: Selective Tuning of Convolutional Networks for Object
Localization | cs.CV | Visual attention modeling has recently gained momentum in developing visual
hierarchies provided by Convolutional Neural Networks. Despite recent successes
of feedforward processing on the abstraction of concepts form raw images, the
inherent nature of feedback processing has remained computationally
controversial. Ins... | computer science |
29,176 | PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its
Application in Saliency Detection | cs.CV | Context plays an important role in many computer vision tasks. Previous
models usually construct contextual information from the whole context region.
However, not all context locations are helpful and some of them may be
detrimental to the final task. To solve this problem, we propose a novel
pixel-wise contextual att... | computer science |
29,177 | Sharpness-aware Low dose CT denoising using conditional generative
adversarial network | cs.CV | Low Dose Computed Tomography (LDCT) has offered tremendous benefits in
radiation restricted applications, but the quantum noise as resulted by the
insufficient number of photons could potentially harm the diagnostic
performance. Current image-based denoising methods tend to produce a blur
effect on the final reconstruc... | computer science |
29,178 | Sparsity Invariant CNNs | cs.CV | In this paper, we consider convolutional neural networks operating on sparse
inputs with an application to depth upsampling from sparse laser scan data.
First, we show that traditional convolutional networks perform poorly when
applied to sparse data even when the location of missing data is provided to
the network. To... | computer science |
29,179 | ProbFlow: Joint Optical Flow and Uncertainty Estimation | cs.CV | Optical flow estimation remains challenging due to untextured areas, motion
boundaries, occlusions, and more. Thus, the estimated flow is not equally
reliable across the image. To that end, post-hoc confidence measures have been
introduced to assess the per-pixel reliability of the flow. We overcome the
artificial sepa... | computer science |
29,180 | Color and Gradient Features for Text Segmentation from Video Frames | cs.CV | Text segmentation in a video is drawing attention of researchers in the field
of image processing, pattern recognition and document image analysis because it
helps in annotating and labeling video events accurately. We propose a novel
idea of generating an enhanced frame from the R, G, and B channels of an input
frame ... | computer science |
29,181 | Contrast and visual saliency similarity induced index for image quality
assessment | cs.CV | Perceptual image quality assessment (IQA) defines/utilizes a computational
model to assess the image quality in consistent with human opinions. A good IQA
model should consider both the effectiveness and efficiency, while most
previous IQA models are hard to reach simultaneously. So we attempt to make
another effort to... | computer science |
29,182 | Activity Recognition based on a Magnitude-Orientation Stream Network | cs.CV | The temporal component of videos provides an important clue for activity
recognition, as a number of activities can be reliably recognized based on the
motion information. In view of that, this work proposes a novel temporal stream
for two-stream convolutional networks based on images computed from the optical
flow mag... | computer science |
29,183 | On Image Classification: Correlation v.s. Causality | cs.CV | Image classification is one of the fundamental problems in computer vision.
Owing to the availability of large image datasets like ImageNet and YFCC100M, a
plethora of research has been conducted to do high precision image
classification and many remarkable achievements have been made. The success of
most existing meth... | computer science |
29,184 | CNN Fixations: An unraveling approach to visualize the discriminative
image regions | cs.CV | Deep convolutional neural networks (CNN) have revolutionized various fields
of vision research and have seen unprecedented adoption for multiple tasks such
as classification, detection, captioning, etc. However, they offer little
transparency into their inner workings and are often treated as black boxes
that deliver e... | computer science |
29,185 | A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition | cs.CV | This paper presents a novel hierarchical spatiotemporal orientation
representation for spacetime image analysis. It is designed to combine the
benefits of the multilayer architecture of ConvNets and a more controlled
approach to spacetime analysis. A distinguishing aspect of the approach is that
unlike most contemporar... | computer science |
29,186 | What does 2D geometric information really tell us about 3D face shape? | cs.CV | A face image contains geometric cues in the form of configurational
information and contours that can be used to estimate 3D face shape. While it
is clear that 3D reconstruction from 2D points is highly ambiguous if no
further constraints are enforced, one might expect that the face-space
constraint solves this problem... | computer science |
29,187 | WordSup: Exploiting Word Annotations for Character based Text Detection | cs.CV | Imagery texts are usually organized as a hierarchy of several visual
elements, i.e. characters, words, text lines and text blocks. Among these
elements, character is the most basic one for various languages such as
Western, Chinese, Japanese, mathematical expression and etc. It is natural and
convenient to construct a ... | computer science |
29,188 | Representation Learning by Learning to Count | cs.CV | We introduce a novel method for representation learning that uses an
artificial supervision signal based on counting visual primitives. This
supervision signal is obtained from an equivariance relation, which does not
require any manual annotation. We relate transformations of images to
transformations of the represent... | computer science |
29,189 | Reflection Separation and Deblurring of Plenoptic Images | cs.CV | In this paper, we address the problem of reflection removal and deblurring
from a single image captured by a plenoptic camera. We develop a two-stage
approach to recover the scene depth and high resolution textures of the
reflected and transmitted layers. For depth estimation in the presence of
reflections, we train a ... | computer science |
29,190 | Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based
Visual Odometry Approach for Endoscopic Capsule Robots | cs.CV | Ingestible wireless capsule endoscopy is an emerging minimally invasive
diagnostic technology for inspection of the GI tract and diagnosis of a wide
range of diseases and pathologies. Medical device companies and many research
groups have recently made substantial progresses in converting passive capsule
endoscopes to ... | computer science |
29,191 | Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and
Camera Self-Calibration | cs.CV | Tracking of multiple objects is an important application in AI City geared
towards solving salient problems related to safety and congestion in an urban
environment. Frequent occlusion in traffic surveillance has been a major
problem in this research field. In this challenge, we propose a model-based
vehicle localizati... | computer science |
29,192 | Pose Estimation using Local Structure-Specific Shape and Appearance
Context | cs.CV | We address the problem of estimating the alignment pose between two models
using structure-specific local descriptors. Our descriptors are generated using
a combination of 2D image data and 3D contextual shape data, resulting in a set
of semi-local descriptors containing rich appearance and shape information for
both e... | computer science |
29,193 | In search of inliers: 3d correspondence by local and global voting | cs.CV | We present a method for finding correspondence between 3D models. From an
initial set of feature correspondences, our method uses a fast voting scheme to
separate the inliers from the outliers. The novelty of our method lies in the
use of a combination of local and global constraints to determine if a vote
should be ca... | computer science |
29,194 | Exploiting Convolution Filter Patterns for Transfer Learning | cs.CV | In this paper, we introduce a new regularization technique for transfer
learning. The aim of the proposed approach is to capture statistical
relationships among convolution filters learned from a well-trained network and
transfer this knowledge to another network. Since convolution filters of the
prevalent deep Convolu... | computer science |
29,195 | Incremental Learning of Object Detectors without Catastrophic Forgetting | cs.CV | Despite their success for object detection, convolutional neural networks are
ill-equipped for incremental learning, i.e., adapting the original model
trained on a set of classes to additionally detect objects of new classes, in
the absence of the initial training data. They suffer from "catastrophic
forgetting" - an a... | computer science |
29,196 | The Unconstrained Ear Recognition Challenge | cs.CV | In this paper we present the results of the Unconstrained Ear Recognition
Challenge (UERC), a group benchmarking effort centered around the problem of
person recognition from ear images captured in uncontrolled conditions. The
goal of the challenge was to assess the performance of existing ear recognition
techniques on... | computer science |
29,197 | Statistical Selection of CNN-Based Audiovisual Features for
Instantaneous Estimation of Human Emotional States | cs.CV | Automatic prediction of continuous-level emotional state requires selection
of suitable affective features to develop a regression system based on
supervised machine learning. This paper investigates the performance of
features statistically learned using convolutional neural networks for
instantaneously predicting the... | computer science |
29,198 | CNN-Based Prediction of Frame-Level Shot Importance for Video
Summarization | cs.CV | In the Internet, ubiquitous presence of redundant, unedited, raw videos has
made video summarization an important problem. Traditional methods of video
summarization employ a heuristic set of hand-crafted features, which in many
cases fail to capture subtle abstraction of a scene. This paper presents a deep
learning me... | computer science |
29,199 | Fast single image super-resolution based on sigmoid transformation | cs.CV | Single image super-resolution aims to generate a high-resolution image from a
single low-resolution image, which is of great significance in extensive
applications. As an ill-posed problem, numerous methods have been proposed to
reconstruct the missing image details based on exemplars or priors. In this
paper, we propo... | computer science |
29,200 | Single Reference Image based Scene Relighting via Material Guided
Filtering | cs.CV | Image relighting is to change the illumination of an image to a target
illumination effect without known the original scene geometry, material
information and illumination condition. We propose a novel outdoor scene
relighting method, which needs only a single reference image and is based on
material constrained layer ... | computer science |
29,201 | Predicting Aesthetic Score Distribution through Cumulative
Jensen-Shannon Divergence | cs.CV | Aesthetic quality prediction is a challenging task in the computer vision
community because of the complex interplay with semantic contents and
photographic technologies. Recent studies on the powerful deep learning based
aesthetic quality assessment usually use a binary high-low label or a numerical
score to represent... | computer science |
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