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31,202 | Camera-based vehicle velocity estimation from monocular video | cs.CV | This paper documents the winning entry at the CVPR2017 vehicle velocity
estimation challenge. Velocity estimation is an emerging task in autonomous
driving which has not yet been thoroughly explored. The goal is to estimate the
relative velocity of a specific vehicle from a sequence of images. In this
paper, we present... | computer science |
31,203 | Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks | cs.CV | Recent work has shown that optical flow estimation can be formulated as an
end-to-end supervised learning problem, which yields estimates with a superior
accuracy-runtime tradeoff compared to alternative methodology. In this paper,
we make the network estimate its local uncertainty about the correctness of its
predicti... | computer science |
31,204 | Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields | cs.CV | Recently, in the community of Neural Style Transfer, several algorithms are
proposed to transfer an artistic style in real-time, which is known as Fast
Style Transfer. However, controlling the stroke size in stylized results still
remains an open challenge. To achieve controllable stroke sizes, several
attempts were ma... | computer science |
31,205 | Transport-Based Pattern Theory: A Signal Transformation Approach | cs.CV | In many scientific fields imaging is used to relate a certain physical
quantity to other dependent variables. Therefore, images can be considered as a
map from a real-world coordinate system to the non-negative measurements being
acquired. In this work we describe an approach for simultaneous modeling and
inference of ... | computer science |
31,206 | MoNet: Moments Embedding Network | cs.CV | Bilinear pooling has been recently proposed as a feature encoding layer,
which can be used after the convolutional layers of a deep network, to improve
performance in multiple vision tasks. Instead of conventional global average
pooling or fully connected layer, bilinear pooling gathers 2nd order
information in a trans... | computer science |
31,207 | Devon: Deformable Volume Network for Learning Optical Flow | cs.CV | We propose a lightweight neural network model, Deformable Volume Network
(Devon) for learning optical flow. Devon benefits from a multi-stage framework
to iteratively refine its prediction. Each stage is by itself a neural network
with an identical architecture. The optical flow between two stages is
propagated with a ... | computer science |
31,208 | Density-aware Single Image De-raining using a Multi-stream Dense Network | cs.CV | Single image rain streak removal is an extremely challenging problem due to
the presence of non-uniform rain densities in images. We present a novel
density-aware multi-stream densely connected convolutional neural network-based
algorithm, called DID-MDN, for joint rain density estimation and de-raining.
The proposed m... | computer science |
31,209 | Angle constrained path to cluster multiple manifolds | cs.CV | In this paper, we propose a method to cluster multiple intersected manifolds.
The algorithm chooses several landmark nodes randomly and then checks whether
there is an angle constrained path between each landmark node and every other
node in the neighborhood graph. When the points lie on different manifolds with
inters... | computer science |
31,210 | Conditional Adversarial Synthesis of 3D Facial Action Units | cs.CV | Employing deep learning-based approaches for fine-grained facial expression
analysis, such as those involving the estimation of Action Unit (AU)
intensities, is difficult due to the lack of a large-scale dataset of real
faces with sufficiently diverse AU labels for training. In this paper, we
consider how AU-level faci... | computer science |
31,211 | Binary Constrained Deep Hashing Network for Image Retrieval without
Human Intervention | cs.CV | Learning compact binary codes for image retrieval problem using deep neural
networks has attracted increasing attention recently. However, training deep
hashing networks is challenging due to the binary constraints on the hash
codes, the similarity preserving properties, and the requirement for a vast
amount of labelle... | computer science |
31,212 | Load Balanced GANs for Multi-view Face Image Synthesis | cs.CV | Multi-view face synthesis from a single image is an ill-posed problem and
often suffers from serious appearance distortion. Producing photo-realistic and
identity preserving multi-view results is still a not well defined synthesis
problem. This paper proposes Load Balanced Generative Adversarial Networks
(LB-GAN) to pr... | computer science |
31,213 | Spatial Morphing Kernel Regression For Feature Interpolation | cs.CV | In recent years, geotagged social media has become popular as a novel source
for geographic knowledge discovery. Ground-level images and videos provide a
different perspective than overhead imagery and can be applied to a range of
applications such as land use mapping, activity detection, pollution mapping,
etc. The sp... | computer science |
31,214 | Learning Image Conditioned Label Space for Multilabel Classification | cs.CV | This work addresses the task of multilabel image classification. Inspired by
the great success from deep convolutional neural networks (CNNs) for
single-label visual-semantic embedding, we exploit extending these models for
multilabel images. Specifically, we propose an image-dependent ranking model,
which returns a ra... | computer science |
31,215 | Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells | cs.CV | We propose a new multiclass weighted loss function for instance segmentation
of cluttered cells. We are primarily motivated by the need of developmental
biologists to quantify and model the behavior of blood T-cells which might help
us in understanding their regulation mechanisms and ultimately help researchers
in thei... | computer science |
31,216 | DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level
Sign Language Translation | cs.CV | There is an undeniable communication barrier between deaf people and people
with normal hearing ability. Although innovations in sign language translation
technology aim to tear down this communication barrier, the majority of
existing sign language translation systems are either intrusive or constrained
by resolution ... | computer science |
31,217 | Deep Collaborative Weight-based Classification | cs.CV | One of the biggest problems in deep learning is its difficulty to retain
consistent robustness when transferring the model trained on one dataset to
another dataset. To conquer the problem, deep transfer learning was implemented
to execute various vision tasks by using a pre-trained deep model in a diverse
dataset. How... | computer science |
31,218 | Batch Normalization and the impact of batch structure on the behavior of
deep convolution networks | cs.CV | Batch normalization was introduced in 2015 to speed up training of deep
convolution networks by normalizing the activations across the current batch to
have zero mean and unity variance. The results presented here show an
interesting aspect of batch normalization, where controlling the shape of the
training batches can... | computer science |
31,219 | Building Efficient ConvNets using Redundant Feature Pruning | cs.CV | This paper presents an efficient technique to prune deep and/or wide
convolutional neural network models by eliminating redundant features (or
filters). Previous studies have shown that over-sized deep neural network
models tend to produce a lot of redundant features that are either shifted
version of one another or ar... | computer science |
31,220 | Learning Multiple Categories on Deep Convolution Networks | cs.CV | Deep convolution networks have proved very successful with big datasets such
as the 1000-classes ImageNet. Results show that the error rate increases slowly
as the size of the dataset increases. Experiments presented here may explain
why these networks are very effective in solving big recognition problems. If
the big ... | computer science |
31,221 | Lossless Compression of Angiogram Foreground with Visual Quality
Preservation of Background | cs.CV | By increasing the volume of telemedicine information, the need for medical
image compression has become more important. In angiographic images, a small
ratio of the entire image usually belongs to the vasculature that provides
crucial information for diagnosis. Other parts of the image are diagnostically
less important... | computer science |
31,222 | Generalizable Adversarial Examples Detection Based on Bi-model Decision
Mismatch | cs.CV | Deep neural networks (DNNs) have shown phenomenal success in a wide range of
applications. However, recent studies have discovered that they are vulnerable
to Adversarial Examples, i.e., original samples with added subtle
perturbations. Such perturbations are often too small and imperceptible to
humans, yet they can ea... | computer science |
31,223 | Left Ventricle Segmentation in Cardiac MR Images Using Fully
Convolutional Network | cs.CV | Medical image analysis, especially segmenting a specific organ, has an
important role in developing clinical decision support systems. In cardiac
magnetic resonance (MR) imaging, segmenting the left and right ventricles helps
physicians diagnose different heart abnormalities. There are challenges for
this task, includi... | computer science |
31,224 | Lossless Image Compression Algorithm for Wireless Capsule Endoscopy by
Content-Based Classification of Image Blocks | cs.CV | Recent advances in capsule endoscopy systems have introduced new methods and
capabilities. The capsule endoscopy system, by observing the entire digestive
tract, has significantly improved diagnosing gastrointestinal disorders and
diseases. The system has challenges such as the need to enhance the quality of
the transm... | computer science |
31,225 | Reversible Image Watermarking for Health Informatics Systems Using
Distortion Compensation in Wavelet Domain | cs.CV | Reversible image watermarking guaranties restoration of both original cover
and watermark logo from the watermarked image. Capacity and distortion of the
image under reversible watermarking are two important parameters. In this study
a reversible watermarking is investigated with focusing on increasing the
embedding ca... | computer science |
31,226 | Segmentation of Bleeding Regions in Wireless Capsule Endoscopy Images an
Approach for inside Capsule Video Summarization | cs.CV | Wireless capsule endoscopy (WCE) is an effective means of diagnosis of
gastrointestinal disorders. Detection of informative scenes by WCE could reduce
the length of transmitted videos and can help with the diagnosis. In this paper
we propose a simple and efficient method for segmentation of the bleeding
regions in WCE ... | computer science |
31,227 | Semantic Segmentation Refinement by Monte Carlo Region Growing of High
Confidence Detections | cs.CV | Despite recent improvements using fully convolutional networks, in general,
the segmentation produced by most state-of-the-art semantic segmentation
methods does not show satisfactory adherence to the object boundaries. We
propose a method to refine the segmentation results generated by such deep
learning models. Our m... | computer science |
31,228 | Liver Segmentation in Abdominal CT Images by Adaptive 3D Region Growing | cs.CV | Automatic liver segmentation plays an important role in computer-aided
diagnosis and treatment. Manual segmentation of organs is a difficult and
tedious task and so prone to human errors. In this paper, we propose an
adaptive 3D region growing with subject-specific conditions. For this aim we
use the intensity distribu... | computer science |
31,229 | Liver segmentation in CT images using three dimensional to two
dimensional fully convolutional network | cs.CV | The need for CT scan analysis is growing for pre-diagnosis and therapy of
abdominal organs. Automatic organ segmentation of abdominal CT scan can help
radiologists analyze the scans faster and segment organ images with fewer
errors. However, existing methods are not efficient enough to perform the
segmentation process ... | computer science |
31,230 | Low complexity convolutional neural network for vessel segmentation in
portable retinal diagnostic devices | cs.CV | Retinal vessel information is helpful in retinal disease screening and
diagnosis. Retinal vessel segmentation provides useful information about
vessels and can be used by physicians during intraocular surgery and retinal
diagnostic operations. Convolutional neural networks (CNNs) are powerful tools
for classification a... | computer science |
31,231 | Detecting Small, Densely Distributed Objects with Filter-Amplifier
Networks and Loss Boosting | cs.CV | Detecting small, densely distributed objects is a significant challenge:
small objects often contain less distinctive information compared to larger
ones, and finer-grained precision of bounding box boundaries are required. In
this paper, we propose two techniques for addressing this problem. First, we
estimate the lik... | computer science |
31,232 | Driver Hand Localization and Grasp Analysis: A Vision-based Real-time
Approach | cs.CV | Extracting hand regions and their grasp information from images robustly in
real-time is critical for occupants' safety and in-vehicular infotainment
applications. It must however, be noted that naturalistic driving scenes suffer
from rapidly changing illumination and occlusion. This is aggravated by the
fact that hand... | computer science |
31,233 | xView: Objects in Context in Overhead Imagery | cs.CV | We introduce a new large-scale dataset for the advancement of object
detection techniques and overhead object detection research. This satellite
imagery dataset enables research progress pertaining to four key computer
vision frontiers. We utilize a novel process for geospatial category detection
and bounding box annot... | computer science |
31,234 | End-to-end learning of keypoint detector and descriptor for pose
invariant 3D matching | cs.CV | Finding correspondences between images or 3D scans is at the heart of many
computer vision and image retrieval applications and is often enabled by
matching local keypoint descriptors. Various learning approaches have been
applied in the past to different stages of the matching pipeline, considering
detector, descripto... | computer science |
31,235 | Improved Techniques For Weakly-Supervised Object Localization | cs.CV | We propose an improved technique for weakly-supervised object localization.
Conventional methods have a limitation that they focus only on most
discriminative parts of the target objects. The recent study addressed this
issue and resolved this limitation by augmenting the training data for less
discriminative parts. To... | computer science |
31,236 | Glimpse Clouds: Human Activity Recognition from Unstructured Feature
Points | cs.CV | We propose a method for human activity recognition from RGB data that does
not rely on any pose information during test time and does not explicitly
calculate pose information internally. Instead, a visual attention module
learns to predict glimpse sequences in each frame. These glimpses correspond to
interest points i... | computer science |
31,237 | Video Person Re-identification by Temporal Residual Learning | cs.CV | In this paper, we propose a novel feature learning framework for video person
re-identification (re-ID). The proposed framework largely aims to exploit the
adequate temporal information of video sequences and tackle the poor spatial
alignment of moving pedestrians. More specifically, for exploiting the temporal
informa... | computer science |
31,238 | Graph-Based Blind Image Deblurring From a Single Photograph | cs.CV | Blind image deblurring, i.e., deblurring without knowledge of the blur
kernel, is a highly ill-posed problem. The problem can be solved in two parts:
i) estimate a blur kernel from the blurry image, and ii) given estimated blur
kernel, de-convolve blurry input to restore the target image. In this paper, we
propose a gr... | computer science |
31,239 | Where's YOUR focus: Personalized Attention | cs.CV | Human visual attention is subjective and biased according to the personal
preference of the viewer, however, current works of saliency detection are
general and objective, without counting the factor of the observer. This will
make the attention prediction for a particular person not accurate enough. In
this work, we p... | computer science |
31,240 | Adversarial Learning for Semi-Supervised Semantic Segmentation | cs.CV | We propose a method for semi-supervised semantic segmentation using the
adversarial network. While most existing discriminators are trained to classify
input images as real or fake on the image level, we design a discriminator in a
fully convolutional manner to differentiate the predicted probability maps from
the grou... | computer science |
31,241 | Non-rigid Object Tracking via Deep Multi-scale Spatial-Temporal
Discriminative Saliency Maps | cs.CV | In this paper we propose an effective non-rigid object tracking method based
on spatial-temporal consistent saliency detection. In contrast to most existing
trackers that use a bounding box to specify the tracked target, the proposed
method can extract the accurate regions of the target as tracking output, which
achiev... | computer science |
31,242 | MagnifyMe: Aiding Cross Resolution Face Recognition via Identity Aware
Synthesis | cs.CV | Enhancing low resolution images via super-resolution or image synthesis for
cross-resolution face recognition has been well studied. Several image
processing and machine learning paradigms have been explored for addressing the
same. In this research, we propose Synthesis via Deep Sparse Representation
algorithm for syn... | computer science |
31,243 | Discriminative Label Consistent Domain Adaptation | cs.CV | Domain adaptation (DA) is transfer learning which aims to learn an effective
predictor on target data from source data despite data distribution mismatch
between source and target. We present in this paper a novel unsupervised DA
method for cross-domain visual recognition which simultaneously optimizes the
three terms ... | computer science |
31,244 | Classification of Breast Cancer Histology using Deep Learning | cs.CV | Breast Cancer is a major cause of death worldwide among women. Hematoxylin
and Eosin (H&E) stained breast tissue samples from biopsies are observed under
microscopes for the primary diagnosis of breast cancer. In this paper, we
propose a deep learning-based method for classification of H&E stained breast
tissue images ... | computer science |
31,245 | Harmonious Attention Network for Person Re-Identification | cs.CV | Existing person re-identification (re-id) methods either assume the
availability of well-aligned person bounding box images as model input or rely
on constrained attention selection mechanisms to calibrate misaligned images.
They are therefore sub-optimal for re-id matching in arbitrarily aligned person
images potentia... | computer science |
31,246 | ChatPainter: Improving Text to Image Generation using Dialogue | cs.CV | Synthesizing realistic images from text descriptions on a dataset like
Microsoft Common Objects in Context (MS COCO), where each image can contain
several objects, is a challenging task. Prior work has used text captions to
generate images. However, captions might not be informative enough to capture
the entire image a... | computer science |
31,247 | Sleep-deprived Fatigue Pattern Analysis using Large-Scale Selfies from
Social Med | cs.CV | The complexities of fatigue have drawn much attention from researchers across
various disciplines. Short-term fatigue may cause safety issue while driving;
thus, dynamic systems were designed to track driver fatigue. Long-term fatigue
could lead to chronic syndromes, and eventually affect individuals physical and
psych... | computer science |
31,248 | Real-Time End-to-End Action Detection with Two-Stream Networks | cs.CV | Two-stream networks have been very successful for solving the problem of
action detection. However, prior work using two-stream networks train both
streams separately, which prevents the network from exploiting regularities
between the two streams. Moreover, unlike the visual stream, the dominant forms
of optical flow ... | computer science |
31,249 | Missing Data Reconstruction in Remote Sensing image with a Unified
Spatial-Temporal-Spectral Deep Convolutional Neural Network | cs.CV | Because of the internal malfunction of satellite sensors and poor atmospheric
conditions such as thick cloud, the acquired remote sensing data often suffer
from missing information, i.e., the data usability is greatly reduced. In this
paper, a novel method of missing information reconstruction in remote sensing
images ... | computer science |
31,250 | Adaptive specular reflection detection and inpainting in colonoscopy
video frames | cs.CV | Colonoscopy video frames might be contaminated by bright spots with
unsaturated values known as specular reflection. Detection and removal of such
reflections could enhance the quality of colonoscopy images and facilitate
diagnosis procedure. In this paper we propose a novel two-phase method for this
purpose, consistin... | computer science |
31,251 | 6D Pose Estimation using an Improved Method based on Point Pair Features | cs.CV | The Point Pair Feature (Drost et al. 2010) has been one of the most
successful 6D pose estimation method among model-based approaches as an
efficient, integrated and compromise alternative to the traditional local and
global pipelines. During the last years, several variations of the algorithm
have been proposed. Among... | computer science |
31,252 | Deep Unsupervised Learning of Visual Similarities | cs.CV | Exemplar learning of visual similarities in an unsupervised manner is a
problem of paramount importance to Computer Vision. In this context, however,
the recent breakthrough in deep learning could not yet unfold its full
potential. With only a single positive sample, a great imbalance between one
positive and many nega... | computer science |
31,253 | Indic Handwritten Script Identification using Offline-Online Multimodal
Deep Network | cs.CV | In this paper, we propose a novel approach of word-level Indic script
identification using only character-level data in training stage. The
advantages of using character level data for training have been outlined in
section I. Our method uses a multimodal deep network which takes both offline
and online modality of the... | computer science |
31,254 | An Approach to Vehicle Trajectory Prediction Using Automatically
Generated Traffic Maps | cs.CV | Trajectory and intention prediction of traffic participants is an important
task in automated driving and crucial for safe interaction with the
environment. In this paper, we present a new approach to vehicle trajectory
prediction based on automatically generated maps containing statistical
information about the behavi... | computer science |
31,255 | Interactive Image Manipulation with Natural Language Instruction
Commands | cs.CV | We propose an interactive image-manipulation system with natural language
instruction, which can generate a target image from a source image and an
instruction that describes the difference between the source and the target
image. The system makes it possible to modify a generated image interactively
and make natural l... | computer science |
31,256 | Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion
Segmentation | cs.CV | Accurate segmentation of breast lesions is a crucial step in evaluating the
characteristics of tumors. However, this is a challenging task, since breast
lesions have sophisticated shape, topological structure, and variation in the
intensity distribution. In this paper, we evaluated the performance of three
unsupervised... | computer science |
31,257 | A Weighted Sparse Sampling and Smoothing Frame Transition Approach for
Semantic Fast-Forward First-Person Videos | cs.CV | Thanks to the advances in the technology of low-cost digital cameras and the
popularity of the self-recording culture, the amount of visual data on the
Internet is going to the opposite side of the available time and patience of
the users. Thus, most of the uploaded videos are doomed to be forgotten and
unwatched in a ... | computer science |
31,258 | Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches | cs.CV | Face Aging has raised considerable attentions and interest from the computer
vision community in recent years. Numerous approaches ranging from purely image
processing techniques to deep learning structures have been proposed in
literature. In this paper, we aim to give a review of recent developments of
modern deep le... | computer science |
31,259 | No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous
Vehicles using Cameras & LiDARs | cs.CV | Online multi-object tracking (MOT) is extremely important for high-level
spatial reasoning and path planning for autonomous and highly-automated
vehicles. In this paper, we present a modular framework for tracking multiple
objects (vehicles), capable of accepting object proposals from different sensor
modalities (visio... | computer science |
31,260 | Tool Detection and Operative Skill Assessment in Surgical Videos Using
Region-Based Convolutional Neural Networks | cs.CV | Five billion people in the world lack access to quality surgical care.
Surgeon skill varies dramatically, and many surgical patients suffer
complications and avoidable harm. Improving surgical training and feedback
would help to reduce the rate of complications, half of which have been shown
to be preventable. To do th... | computer science |
31,261 | Superpixel based Class-Semantic Texton Occurrences for Natural Roadside
Vegetation Segmentation | cs.CV | Vegetation segmentation from roadside data is a field that has received
relatively little attention in present studies, but can be of great potentials
in a wide range of real-world applications, such as road safety assessment and
vegetation condition monitoring. In this paper, we present a novel approach
that generates... | computer science |
31,262 | Facial Expression Analysis under Partial Occlusion: A Survey | cs.CV | Automatic machine-based Facial Expression Analysis (FEA) has made substantial
progress in the past few decades driven by its importance for applications in
psychology, security, health, entertainment and human computer interaction. The
vast majority of completed FEA studies are based on non-occluded faces
collected in ... | computer science |
31,263 | Spatially Constrained Location Prior for Scene Parsing | cs.CV | Semantic context is an important and useful cue for scene parsing in
complicated natural images with a substantial amount of variations in objects
and the environment. This paper proposes Spatially Constrained Location Prior
(SCLP) for effective modelling of global and local semantic context in the
scene in terms of in... | computer science |
31,264 | Multispectral Image Intrinsic Decomposition via Low Rank Constraint | cs.CV | Multispectral images contain many clues of surface characteristics of the
objects, thus can be widely used in many computer vision tasks, e.g.,
recolorization and segmentation. However, due to the complex illumination and
the geometry structure of natural scenes, the spectra curves of a same surface
can look very diffe... | computer science |
31,265 | Constrained Image Generation Using Binarized Neural Networks with
Decision Procedures | cs.CV | We consider the problem of binary image generation with given properties.
This problem arises in a number of practical applications, including generation
of artificial porous medium for an electrode of lithium-ion batteries, for
composed materials, etc. A generated image represents a porous medium and, as
such, it is s... | computer science |
31,266 | Residual Dense Network for Image Super-Resolution | cs.CV | A very deep convolutional neural network (CNN) has recently achieved great
success for image super-resolution (SR) and offered hierarchical features as
well. However, most deep CNN based SR models do not make full use of the
hierarchical features from the original low-resolution (LR) images, thereby
achieving relativel... | computer science |
31,267 | Single Image Super-Resolution via Cascaded Multi-Scale Cross Network | cs.CV | The deep convolutional neural networks have achieved significant improvements
in accuracy and speed for single image super-resolution. However, as the depth
of network grows, the information flow is weakened and the training becomes
harder and harder. On the other hand, most of the models adopt a single-stream
structur... | computer science |
31,268 | A Twofold Siamese Network for Real-Time Object Tracking | cs.CV | Observing that Semantic features learned in an image classification task and
Appearance features learned in a similarity matching task complement each
other, we build a twofold Siamese network, named SA-Siam, for real-time object
tracking. SA-Siam is composed of a semantic branch and an appearance branch.
Each branch i... | computer science |
31,269 | Free-breathing cardiac MRI using bandlimited manifold modelling | cs.CV | We introduce a novel bandlimited manifold framework and an algorithm to
recover freebreathing and ungated cardiac MR images from highly undersampled
measurements. The image frames in the free breathing and ungated dataset are
assumed to be points on a bandlimited manifold. We introduce a novel kernel
low-rank algorithm... | computer science |
31,270 | A Dataset To Evaluate The Representations Learned By Video Prediction
Models | cs.CV | We present a parameterized synthetic dataset called Moving Symbols to support
the objective study of video prediction networks. Using several instantiations
of the dataset in which variation is explicitly controlled, we highlight issues
in an existing state-of-the-art approach and propose the use of a performance
metri... | computer science |
31,271 | Detecting Comma-shaped Clouds for Severe Weather Forecasting using Shape
and Motion | cs.CV | Meteorologists use shapes and movements of clouds in satellite images as
indicators of several major types of severe storms. Satellite imaginary data
are in increasingly higher resolution, both spatially and temporally, making it
impossible for humans to fully leverage the data in their forecast. Automatic
satellite im... | computer science |
31,272 | Multi-Oriented Scene Text Detection via Corner Localization and Region
Segmentation | cs.CV | Previous deep learning based state-of-the-art scene text detection methods
can be roughly classified into two categories. The first category treats scene
text as a type of general objects and follows general object detection paradigm
to localize scene text by regressing the text box locations, but troubled by
the arbit... | computer science |
31,273 | Seeing Small Faces from Robust Anchor's Perspective | cs.CV | This paper introduces a novel anchor design to support anchor-based face
detection for superior scale-invariant performance, especially on tiny faces.
To achieve this, we explicitly address the problem that anchor-based detectors
drop performance drastically on faces with tiny sizes, e.g. less than 16x16
pixels. In thi... | computer science |
31,274 | Attention-Aware Generative Adversarial Networks (ATA-GANs) | cs.CV | In this work, we present a novel approach for training Generative Adversarial
Networks (GANs). Using the attention maps produced by a Teacher- Network we are
able to improve the quality of the generated images as well as perform weakly
object localization on the generated images. To this end, we generate images of
HEp-... | computer science |
31,275 | Adversarially Learned One-Class Classifier for Novelty Detection | cs.CV | Novelty detection is the process of identifying the observation(s) that
differ in some respect from the training observations (the target class). In
reality, the novelty class is often absent during training, poorly sampled or
not well defined. Therefore, one-class classifiers can efficiently model such
problems. Howev... | computer science |
31,276 | PBGen: Partial Binarization of Deconvolution-Based Generators for Edge
Intelligence | cs.CV | This work explores the binarization of the deconvolution-based generator in a
GAN for memory saving and speedup of image construction. Our study suggests
that different from convolutional neural networks (including the discriminator)
where all layers can be binarized, only some of the layers in the generator can
be bin... | computer science |
31,277 | Photographic Text-to-Image Synthesis with a Hierarchically-nested
Adversarial Network | cs.CV | This paper presents a novel method to deal with the challenging task of
generating photographic images conditioned on semantic image descriptions. Our
method introduces accompanying hierarchical-nested adversarial objectives
inside the network hierarchies, which regularize mid-level representations and
assist generator... | computer science |
31,278 | Depth Masked Discriminative Correlation Filter | cs.CV | Depth information provides a strong cue for occlusion detection and handling,
but has been largely omitted in generic object tracking until recently due to
lack of suitable benchmark datasets and applications. In this work, we propose
a Depth Masked Discriminative Correlation Filter (DM-DCF) which adopts novel
depth se... | computer science |
31,279 | 2D/3D Pose Estimation and Action Recognition using Multitask Deep
Learning | cs.CV | Action recognition and human pose estimation are closely related but both
problems are generally handled as distinct tasks in the literature. In this
work, we propose a multitask framework for jointly 2D and 3D pose estimation
from still images and human action recognition from video sequences. We show
that a single ar... | computer science |
31,280 | Using Curvilinear Features in Focus for Registering a Single Image to a
3D Object | cs.CV | In the context of 2D/3D registration, this paper introduces an approach that
allows to match features detected in two different modalities: photographs and
3D models, by using a common 2D reprensentation. More precisely, 2D images are
matched with a set of depth images, representing the 3D model. After
introducing the ... | computer science |
31,281 | Classification of breast cancer histology images using transfer learning | cs.CV | Breast cancer is one of the leading causes of mortality in women. Early
detection and treatment are imperative for improving survival rates, which have
steadily increased in recent years as a result of more sophisticated
computer-aided-diagnosis (CAD) systems. A critical component of breast cancer
diagnosis relies on h... | computer science |
31,282 | A Robust Real-Time Automatic License Plate Recognition based on the YOLO
Detector | cs.CV | Automatic License Plate Recognition (ALPR) has been a frequent topic of
research due to many practical applications. However, many of the current
solutions are still not robust in real-world situations, commonly depending on
many constraints. This paper presents a robust and efficient ALPR system based
on the state-of-... | computer science |
31,283 | i3PosNet: Instrument Pose Estimation from X-Ray | cs.CV | Performing delicate Minimally Invasive Surgeries (MIS) forces surgeons to
accurately assess the position and orientation (pose) of surgical instruments.
In current practice, this pose information is provided by conventional tracking
systems (optical and electro-magnetic). Two challenges render these systems
inadequate ... | computer science |
31,284 | A Resilient Image Matching Method with an Affine Invariant Feature
Detector and Descriptor | cs.CV | Image feature matching is to seek, localize and identify the similarities
across the images. The matched local features between different images can
indicate the similarities of their content. Resilience of image feature
matching to large view point changes is challenging for a lot of applications
such as 3D object rec... | computer science |
31,285 | Translating and Segmenting Multimodal Medical Volumes with Cycle- and
Shape-Consistency Generative Adversarial Network | cs.CV | Synthesized medical images have several important applications, e.g., as an
intermedium in cross-modality image registration and as supplementary training
samples to boost the generalization capability of a classifier. Especially,
synthesized computed tomography (CT) data can provide X-ray attenuation map for
radiation... | computer science |
31,286 | Semantic segmentation of trajectories with agent models | cs.CV | In many cases, such as trajectories clustering and classification, we often
divide a trajectory into segments as preprocessing. In this paper, we propose a
trajectory semantic segmentation method based on learned behavior models. In
the proposed method, we learn some behavior models from video sequences. Next,
using le... | computer science |
31,287 | Directional Statistics-based Deep Metric Learning for Image
Classification and Retrieval | cs.CV | Deep distance metric learning (DDML), which is proposed to learn image
similarity metrics in an end-to-end manner based on the convolution neural
network, has achieved encouraging results in many computer vision
tasks.$L2$-normalization in the embedding space has been used to improve the
performance of several DDML met... | computer science |
31,288 | Single-View Food Portion Estimation: Learning Image-to-Energy Mappings
Using Generative Adversarial Networks | cs.CV | Due to the growing concern of chronic diseases and other health problems
related to diet, there is a need to develop accurate methods to estimate an
individual's food and energy intake. Measuring accurate dietary intake is an
open research problem. In particular, accurate food portion estimation is
challenging since th... | computer science |
31,289 | Recurrent Residual Module for Fast Inference in Videos | cs.CV | Deep convolutional neural networks (CNNs) have made impressive progress in
many video recognition tasks such as video pose estimation and video object
detection. However, CNN inference on video is computationally expensive due to
processing dense frames individually. In this work, we propose a framework
called Recurren... | computer science |
31,290 | ReHAR: Robust and Efficient Human Activity Recognition | cs.CV | Designing a scheme that can achieve a good performance in predicting single
person activities and group activities is a challenging task. In this paper, we
propose a novel robust and efficient human activity recognition scheme called
ReHAR, which can be used to handle single person activities and group
activities predi... | computer science |
31,291 | Mixed Supervised Object Detection with Robust Objectness Transfer | cs.CV | In this paper, we consider the problem of leveraging existing fully labeled
categories to improve the weakly supervised detection (WSD) of new object
categories, which we refer to as mixed supervised detection (MSD). Different
from previous MSD methods that directly transfer the pre-trained object
detectors from existi... | computer science |
31,292 | Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition | cs.CV | Variations of human body skeletons may be considered as dynamic graphs, which
are generic data representation for numerous real-world applications. In this
paper, we propose a spatio-temporal graph convolution (STGC) approach for
assembling the successes of local convolutional filtering and sequence learning
ability of... | computer science |
31,293 | Real-World Repetition Estimation by Div, Grad and Curl | cs.CV | We consider the problem of estimating repetition in video, such as performing
push-ups, cutting a melon or playing violin. Existing work shows good results
under the assumption of static and stationary periodicity. As realistic video
is rarely perfectly static and stationary, the often preferred Fourier-based
measureme... | computer science |
31,294 | Fusion of Multispectral Data Through Illumination-aware Deep Neural
Networks for Pedestrian Detection | cs.CV | Multispectral pedestrian detection has received extensive attention in recent
years as a promising solution to facilitate robust human target detection for
around-the-clock applications (e.g. security surveillance and autonomous
driving). In this paper, we demonstrate illumination information encoded in
multispectral i... | computer science |
31,295 | Neural Stereoscopic Image Style Transfer | cs.CV | Neural style transfer is an emerging technique which is able to endow
daily-life images with attractive artistic styles. Previous work has succeeded
in applying convolutional neural network (CNN) to style transfer for monocular
images or videos. However, style transfer for stereoscopic images is still a
missing piece. ... | computer science |
31,296 | 3D Object Super-Resolution | cs.CV | We consider the problem of scaling deep generative shape models to
high-resolution. To this end, we introduce a novel method for the fast
up-sampling of 3D objects in voxel space by super-resolution on the six
orthographic depth projections. We demonstrate the training of object-specific
super-resolution CNNs for depth... | computer science |
31,297 | Deep Learning Architectures for Face Recognition in Video Surveillance | cs.CV | Face recognition (FR) systems for video surveillance (VS) applications
attempt to accurately detect the presence of target individuals over a
distributed network of cameras. In video-based FR systems, facial models of
target individuals are designed a priori during enrollment using a limited
number of reference still i... | computer science |
31,298 | Simultaneous Traffic Sign Detection and Boundary Estimation using
Convolutional Neural Network | cs.CV | We propose a novel traffic sign detection system that simultaneously
estimates the location and precise boundary of traffic signs using
convolutional neural network (CNN). Estimating the precise boundary of traffic
signs is important in navigation systems for intelligent vehicles where traffic
signs can be used as 3D l... | computer science |
31,299 | Generating High Quality Visible Images from SAR Images Using CNNs | cs.CV | We propose a novel approach for generating high quality visible-like images
from Synthetic Aperture Radar (SAR) images using Deep Convolutional Generative
Adversarial Network (GAN) architectures. The proposed approach is based on a
cascaded network of convolutional neural nets (CNNs) for despeckling and image
colorizat... | computer science |
31,300 | Improving OCR Accuracy on Early Printed Books by combining Pretraining,
Voting, and Active Learning | cs.CV | We combine three methods which significantly improve the OCR accuracy of OCR
models trained on early printed books: (1) The pretraining method utilizes the
information stored in already existing models trained on a variety of typesets
(mixed models) instead of starting the training from scratch. (2) Performing
cross fo... | computer science |
31,301 | CSRNet: Dilated Convolutional Neural Networks for Understanding the
Highly Congested Scenes | cs.CV | We propose a network for Congested Scene Recognition called CSRNet to provide
a data-driven and deep learning method that can understand highly congested
scenes and perform accurate count estimation as well as present high-quality
density maps. The proposed CSRNet is composed of two major components: a
convolutional ne... | computer science |
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