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31,002 | Build a Compact Binary Neural Network through Bit-level Sensitivity and
Data Pruning | cs.CV | Convolutional neural network (CNN) has been widely used for vision-based
tasks. Due to the high computational complexity and memory storage requirement,
it is hard to directly deploy a full-precision CNN on embedded devices. The
hardware-friendly designs are needed for re-source-limited and
energy-constrained embed-ded... | computer science |
31,003 | Deep Learning Framework for Multi-class Breast Cancer Histology Image
Classification | cs.CV | In this work, we present a deep learning framework for multi-class breast
cancer image classification as our submission to the International Conference
on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer
Histology images (BACH). As these histology images are too large to fit into
GPU memory,... | computer science |
31,004 | Recent Advances in Efficient Computation of Deep Convolutional Neural
Networks | cs.CV | Deep neural networks have evolved remarkably over the past few years and they
are currently the fundamental tools of many intelligent systems. At the same
time, the computational complexity and resource consumption of these networks
also continue to increase. This will pose a significant challenge to the
deployment of ... | computer science |
31,005 | Learning the Synthesizability of Dynamic Texture Samples | cs.CV | A dynamic texture (DT) refers to a sequence of images that exhibit temporal
regularities and has many applications in computer vision and graphics. Given
an exemplar of dynamic texture, it is a dynamic but challenging task to
generate new samples with high quality that are perceptually similar to the
input exemplar, wh... | computer science |
31,006 | Ensembling Neural Networks for Digital Pathology Images Classification
and Segmentation | cs.CV | In the last years, neural networks have proven to be a powerful framework for
various image analysis problems. However, some application domains have
specific limitations. Notably, digital pathology is an example of such fields
due to tremendous image sizes and quite limited number of training examples
available. In th... | computer science |
31,007 | Image Posterization Using Fuzzy Logic and Bilateral Filter | cs.CV | Image posterization is converting images with a large number of tones into
synthetic images with distinct flat areas and a fewer number of tones. In this
technical report, we present the implementation and results of using fuzzy
logic in order to generate a posterized image in a simple and fast way. The
image filter is... | computer science |
31,008 | Museum Exhibit Identification Challenge for Domain Adaptation and Beyond | cs.CV | In this paper, we approach an open problem of artwork identification and
propose a new dataset dubbed Open Museum Identification Challenge (Open MIC).
It contains photos of exhibits captured in 10 distinct exhibition spaces of
several museums which showcase paintings, timepieces, sculptures, glassware,
relics, science ... | computer science |
31,009 | End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End
Learning | cs.CV | We introduce End2You -- the Imperial College London toolkit for multimodal
profiling by end-to-end deep learning. End2You is an open-source toolkit
implemented in Python and is based on Tensorflow. It provides capabilities to
train and evaluate models in an end-to-end manner, i.e., using raw input. It
supports input fr... | computer science |
31,010 | Searching for Representative Modes on Hypergraphs for Robust Geometric
Model Fitting | cs.CV | In this paper, we propose a simple and effective {geometric} model fitting
method to fit and segment multi-structure data even in the presence of severe
outliers. We cast the task of geometric model fitting as a representative
mode-seeking problem on hypergraphs. Specifically, a hypergraph is firstly
constructed, where... | computer science |
31,011 | Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid
Model | cs.CV | We introduce the first benchmark for a new problem --- recognizing human
action adverbs (HAA): "Adverbs Describing Human Actions" (ADHA). This is the
first step for computer vision to change over from pattern recognition to real
AI. We demonstrate some key features of ADHA: a semantically complete set of
adverbs descri... | computer science |
31,012 | Efficient Video Object Segmentation via Network Modulation | cs.CV | Video object segmentation targets at segmenting a specific object throughout
a video sequence, given only an annotated first frame. Recent deep learning
based approaches find it effective by fine-tuning a general-purpose
segmentation model on the annotated frame using hundreds of iterations of
gradient descent. Despite... | computer science |
31,013 | Image Synthesis in Multi-Contrast MRI with Conditional Generative
Adversarial Networks | cs.CV | Acquiring images of the same anatomy with multiple different contrasts
increases the diversity of diagnostic information available in an MR exam. Yet,
scan time limitations may prohibit acquisition of certain contrasts, and images
for some contrast may be corrupted by noise and artifacts. In such cases, the
ability to ... | computer science |
31,014 | Tracking Multiple Moving Objects Using Unscented Kalman Filtering
Techniques | cs.CV | It is an important task to reliably detect and track multiple moving objects
for video surveillance and monitoring. However, when occlusion occurs in
nonlinear motion scenarios, many existing methods often fail to continuously
track multiple moving objects of interest. In this paper we propose an
effective approach for... | computer science |
31,015 | Face Destylization | cs.CV | Numerous style transfer methods which produce artistic styles of portraits
have been proposed to date. However, the inverse problem of converting the
stylized portraits back into realistic faces is yet to be investigated
thoroughly. Reverting an artistic portrait to its original photo-realistic face
image has potential... | computer science |
31,016 | Accurate brain extraction using Active Shape Model and Convolutional
Neural Networks | cs.CV | Brain extraction or skull stripping is a fundamental procedure in most of
neuroimaging processing systems. The performance of this procedure has had a
critical impact on the success of neuroimaging analysis. After several years of
research and development, brain extraction still remains a challenging problem.
In this p... | computer science |
31,017 | Face recognition for monitoring operator shift in railways | cs.CV | Train Pilot is a very tedious and stressful job. Pilots must be vigilant at
all times and its easy for them to lose track of time of shift. In countries
like USA the pilots are mandated by law to adhere to 8 hour shifts. If they
exceed 8 hours of shift the railroads may be penalized for over-tiring their
drivers. The p... | computer science |
31,018 | Zero-Shot Kernel Learning | cs.CV | In this paper, we address an open problem of zero-shot learning. Its
principle is based on learning a mapping that associates feature vectors
extracted from i.e. images and attribute vectors that describe objects and/or
scenes of interest. In turns, this allows classifying unseen object classes
and/or scenes by matchin... | computer science |
31,019 | Data Augmentation of Railway Images for Track Inspection | cs.CV | Regular maintenance of all the assets is pivotal for proper functioning of
railway. Manual maintenance can be very cumbersome and leave room for errors.
Track anomalies like vegetation overgrowth, sun kinks affect the track
construct and result in unequal load transfer, imbalanced lateral forces on
tracks which causes ... | computer science |
31,020 | Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional
Neural Networks | cs.CV | Remote sensing is extensively used in cartography. As transportation networks
expand, extracting roads automatically from satellite images is crucial to keep
maps up-to-date. Synthetic Aperture Radar (SAR) satellites can provide high
resolution topographical maps. However roads are difficult to identify in SAR
images a... | computer science |
31,021 | Mixed-Resolution Image Representation and Compression with Convolutional
Neural Networks | cs.CV | In this paper, we propose a end-to-end mixed-resolution image compression
framework with convolutional neural networks. Firstly, given one input image,
feature description neural network (FDNN) is used to generate a new
representation of this image, so that this representation can be more
efficiently compressed by stan... | computer science |
31,022 | Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds | cs.CV | Deep learning approaches have made tremendous progress in the field of
semantic segmentation over the past few years. However, most current approaches
operate in the 2D image space. Direct semantic segmentation of unstructured 3D
point clouds is still an open research problem. The recently proposed PointNet
architectur... | computer science |
31,023 | 3D non-rigid registration using color: Color Coherent Point Drift | cs.CV | Research into object deformations using computer vision techniques has been
under intense study in recent years. A widely used technique is 3D non-rigid
registration to estimate the transformation between two instances of a
deforming structure. Despite many previous developments on this topic, it
remains a challenging ... | computer science |
31,024 | Background subtraction using the factored 3-way restricted Boltzmann
machines | cs.CV | In this paper, we proposed a method for reconstructing the 3D model based on
continuous sensory input. The robot can draw on extremely large data from the
real world using various sensors. However, the sensory inputs are usually too
noisy and high-dimensional data. It is very difficult and time consuming for
robot to p... | computer science |
31,025 | Adviser Networks: Learning What Question to Ask for Human-In-The-Loop
Viewpoint Estimation | cs.CV | Humans have an unparalleled visual intelligence and can overcome visual
ambiguities that machines currently cannot. Recent works have shown that
incorporating guidance from humans during inference for monocular
viewpoint-estimation can help overcome difficult cases in which the
computer-alone would have otherwise faile... | computer science |
31,026 | Compressive Light Field Reconstructions using Deep Learning | cs.CV | Light field imaging is limited in its computational processing demands of
high sampling for both spatial and angular dimensions. Single-shot light field
cameras sacrifice spatial resolution to sample angular viewpoints, typically by
multiplexing incoming rays onto a 2D sensor array. While this resolution can be
recover... | computer science |
31,027 | Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view
Solution | cs.CV | Lifting is a common manual material handling task performed in the
workplaces. It is considered as one of the main risk factors for Work-related
Musculoskeletal Disorders. To improve work place safety, it is necessary to
assess musculoskeletal and biomechanical risk exposures associated with these
tasks, which requires... | computer science |
31,028 | Scale-recurrent Network for Deep Image Deblurring | cs.CV | In single image deblurring, the "coarse-to-fine" scheme, i.e. gradually
restoring the sharp image on different resolutions in a pyramid, is very
successful in both traditional optimization-based methods and recent
neural-network-based approaches. In this paper, we investigate this strategy
and propose a Scale-recurrent... | computer science |
31,029 | Brute-Force Facial Landmark Analysis With A 140,000-Way Classifier | cs.CV | We propose a simple approach to visual alignment, focusing on the
illustrative task of facial landmark estimation. While most prior work treats
this as a regression problem, we instead formulate it as a discrete $K$-way
classification task, where a classifier is trained to return one of $K$
discrete alignments. One cru... | computer science |
31,030 | Rollable Latent Space for SAR Target Recognition of Un-seen Views | cs.CV | This paper proposes rollable latent space (RLS) for synthetic aperture radar
(SAR) target recognition of un-seen views. Scarce labeled data and limited
viewing direction are critical issues in SAR target recognition.The RLS is a
designed space in which rolling of latent features corresponds to 3D rotation
of an object.... | computer science |
31,031 | Geometry-Contrastive Generative Adversarial Network for Facial
Expression Synthesis | cs.CV | In this paper, we propose a geometry-contrastive generative adversarial
network GC-GAN for generating facial expression images conditioned on geometry
information. Specifically, given an input face and a target expression
designated by a set of facial landmarks, an identity-preserving face can be
generated guided by th... | computer science |
31,032 | Fast Piecewise-Affine Motion Estimation Without Segmentation | cs.CV | Current algorithmic approaches for piecewise affine motion estimation are
based on alternating motion segmentation and estimation. We propose a new
method to estimate piecewise affine motion fields directly without intermediate
segmentation. To this end, we reformulate the problem by imposing piecewise
constancy of the... | computer science |
31,033 | Every Smile is Unique: Landmark-Guided Diverse Smile Generation | cs.CV | Each smile is unique: one person surely smiles in different ways (e.g.,
closing/opening the eyes or mouth). Given one input image of a neutral face,
can we generate multiple smile videos with distinctive characteristics? To
tackle this one-to-many video generation problem, we propose a novel deep
learning architecture ... | computer science |
31,034 | Learning Image Representations by Completing Damaged Jigsaw Puzzles | cs.CV | In this paper, we explore methods of complicating self-supervised tasks for
representation learning. That is, we do severe damage to data and encourage a
network to recover them. First, we complicate each of three powerful
self-supervised task candidates: jigsaw puzzle, inpainting, and colorization.
In addition, we int... | computer science |
31,035 | Attribute-Guided Network for Cross-Modal Zero-Shot Hashing | cs.CV | Zero-Shot Hashing aims at learning a hashing model that is trained only by
instances from seen categories but can generate well to those of unseen
categories. Typically, it is achieved by utilizing a semantic embedding space
to transfer knowledge from seen domain to unseen domain. Existing efforts
mainly focus on singl... | computer science |
31,036 | Multimodal Image Captioning for Marketing Analysis | cs.CV | Automatically captioning images with natural language sentences is an
important research topic. State of the art models are able to produce
human-like sentences. These models typically describe the depicted scene as a
whole and do not target specific objects of interest or emotional relationships
between these objects ... | computer science |
31,037 | Orthogonally Regularized Deep Networks For Image Super-resolution | cs.CV | Deep learning methods, in particular trained Convolutional Neural Networks
(CNNs) have recently been shown to produce compelling state-of-the-art results
for single image Super-Resolution (SR). Invariably, a CNN is learned to map the
low resolution (LR) image to its corresponding high resolution (HR) version in
the spa... | computer science |
31,038 | Multi-Temporal Land Cover Classification with Sequential Recurrent
Encoders | cs.CV | Earth observation (EO) sensors deliver data with daily or weekly temporal
resolution. Most land use and land cover (LULC) approaches, however, expect
cloud-free and mono-temporal observations. The increasing temporal capabilities
of today's sensors enables the use of temporal, along with spectral and spatial
features. ... | computer science |
31,039 | A Log-Euclidean and Total Variation based Variational Framework for
Computational Sonography | cs.CV | We propose a spatial compounding technique and variational framework to
improve 3D ultrasound image quality by compositing multiple ultrasound volumes
acquired from different probe orientations. In the composite volume, instead of
intensity values, we estimate a tensor at every voxel. The resultant tensor
image encapsu... | computer science |
31,040 | Structural Recurrent Neural Network (SRNN) for Group Activity Analysis | cs.CV | A group of persons can be analyzed at various semantic levels such as
individual actions, their interactions, and the activity of the entire group.
In this paper, we propose a structural recurrent neural network (SRNN) that
uses a series of interconnected RNNs to jointly capture the actions of
individuals, their intera... | computer science |
31,041 | Face Detection Using Improved Faster RCNN | cs.CV | Faster RCNN has achieved great success for generic object detection including
PASCAL object detection and MS COCO object detection. In this report, we
propose a detailed designed Faster RCNN method named FDNet1.0 for face
detection. Several techniques were employed including multi-scale training,
multi-scale testing, l... | computer science |
31,042 | A Systematic Analysis for State-of-the-Art 3D Lung Nodule Proposals
Generation | cs.CV | Lung nodule proposals generation is the primary step of lung nodule detection
and has received much attention in recent years . In this paper, we first
construct a model of 3-dimension Convolutional Neural Network (3D CNN) to
generate lung nodule proposals, which can achieve the state-of-the-art
performance. Then, we a... | computer science |
31,043 | Applications of a Graph Theoretic Based Clustering Framework in Computer
Vision and Pattern Recognition | cs.CV | Recently, several clustering algorithms have been used to solve variety of
problems from different discipline. This dissertation aims to address different
challenging tasks in computer vision and pattern recognition by casting the
problems as a clustering problem. We proposed novel approaches to solve
multi-target trac... | computer science |
31,044 | 2D-Densely Connected Convolution Neural Networks for automatic Liver and
Tumor Segmentation | cs.CV | In this paper we propose a fully automatic 2-stage cascaded approach for
segmentation of liver and its tumors in CT (Computed Tomography) images using
densely connected fully convolutional neural network (DenseNet). We
independently train liver and tumor segmentation models and cascade them for a
combined segmentation ... | computer science |
31,045 | Enhanced Image Classification With Data Augmentation Using Position
Coordinates | cs.CV | In this paper we propose the use of image pixel position coordinate system to
improve image classification accuracy in various applications. Specifically, we
hypothesize that the use of pixel coordinates will lead to (a) Resolution
invariant performance. Here, by resolution we mean the spacing between the
pixels rather... | computer science |
31,046 | Smile detection in the wild based on transfer learning | cs.CV | Smile detection from unconstrained facial images is a specialized and
challenging problem. As one of the most informative expressions, smiles convey
basic underlying emotions, such as happiness and satisfaction, which lead to
multiple applications, e.g., human behavior analysis and interactive
controlling. Compared to ... | computer science |
31,047 | A High-Performance HOG Extractor on FPGA | cs.CV | Pedestrian detection is one of the key problems in emerging self-driving car
industry. And HOG algorithm has proven to provide good accuracy for pedestrian
detection. There are plenty of research works have been done in accelerating
HOG algorithm on FPGA because of its low-power and high-throughput
characteristics. In ... | computer science |
31,048 | SocialML: machine learning for social media video creators | cs.CV | In the recent years, social media have become one of the main places where
creative content is being published and consumed by billions of users. Contrary
to traditional media, social media allow the publishers to receive almost
instantaneous feedback regarding their creative work at an unprecedented scale.
This is a p... | computer science |
31,049 | Automatic Pavement Crack Detection Based on Structured Prediction with
the Convolutional Neural Network | cs.CV | Automated pavement crack detection is a challenging task that has been
researched for decades due to the complicated pavement conditions in real
world. In this paper, a supervised method based on deep learning is proposed,
which has the capability of dealing with different pavement conditions.
Specifically, a convoluti... | computer science |
31,050 | Describing Semantic Representations of Brain Activity Evoked by Visual
Stimuli | cs.CV | Quantitative modeling of human brain activity based on language
representations has been actively studied in systems neuroscience. However,
previous studies examined word-level representation, and little is known about
whether we could recover structured sentences from brain activity. This study
attempts to generate na... | computer science |
31,051 | A Multiresolution Deep Learning Framework for Automated Annotation of
Reflectance Confocal Microscopy Images | cs.CV | Morphological tissue patterns in RCM images are critical in diagnosis of
melanocytic lesions. We present a multiresolution deep learning framework that
can automatically annotate RCM images for these diagnostic patterns with high
sensitivity and specificity | computer science |
31,052 | Feature Based Framework to Detect Diseases, Tumor, and Bleeding in
Wireless Capsule Endoscopy | cs.CV | Studying animal locomotion improves our understanding of motor control and
aids in the treatment of motor impairment. Mice are a premier model of human
disease and are the model system of choice for much of basic neuroscience. High
frame rates (250 Hz) are needed to quantify the kinematics of these running
rodents. Man... | computer science |
31,053 | A comprehensive review of 3D point cloud descriptors | cs.CV | The introduction of inexpensive 3D data acquisition devices has promisingly
facilitated the wide availability and popularity of 3D point cloud, which
attracts more attention on the effective extraction of novel 3D point cloud
descriptors for accurate and efficient of 3D computer vision tasks. However,
how to de- velop ... | computer science |
31,054 | Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder | cs.CV | Existing video hash functions are built on three isolated stages: frame
pooling, relaxed learning, and binarization, which have not adequately explored
the temporal order of video frames in a joint binary optimization model,
resulting in severe information loss. In this paper, we propose a novel
unsupervised video hash... | computer science |
31,055 | MiMatrix: A Massively Distributed Deep Learning Framework on a Petascale
High-density Heterogeneous Cluster | cs.CV | In this paper, we present a co-designed petascale high-density GPU cluster to
expedite distributed deep learning training with synchronous Stochastic
Gradient Descent~(SSGD). This architecture of our heterogeneous cluster is
inspired by Harvard architecture. Regarding to different roles in the system,
nodes are configu... | computer science |
31,056 | Outlier Detection for Robust Multi-dimensional Scaling | cs.CV | Multi-dimensional scaling (MDS) plays a central role in data-exploration,
dimensionality reduction and visualization. State-of-the-art MDS algorithms are
not robust to outliers, yielding significant errors in the embedding even when
only a handful of outliers are present. In this paper, we introduce a technique
to dete... | computer science |
31,057 | SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images | cs.CV | Large-scale image data such as digital whole-slide histology images pose a
challenging task at annotation software solutions. Today, a number of good
solutions with varying scopes exist. For cell annotation, however, we find that
many do not match the prerequisites for fast annotations. Especially in the
field of mitos... | computer science |
31,058 | ShakeDrop regularization | cs.CV | This paper proposes a powerful regularization method named ShakeDrop
regularization. ShakeDrop is inspired by Shake-Shake regularization that
decreases error rates by disturbing learning. While Shake-Shake can be applied
to only ResNeXt which has multiple branches, ShakeDrop can be applied to not
only ResNeXt but also ... | computer science |
31,059 | Super-resolution of spatiotemporal event-stream image captured by the
asynchronous temporal contrast vision sensor | cs.CV | Super-resolution (SR) is a useful technology to generate a high-resolution
(HR) visual output from the low-resolution (LR) visual inputs overcoming the
physical limitations of the cameras. However, SR has not been applied to
enhance the resolution of spatiotemporal event-stream images captured by the
frame-free dynamic... | computer science |
31,060 | Revisiting the Inverted Indices for Billion-Scale Approximate Nearest
Neighbors | cs.CV | This work addresses the problem of billion-scale nearest neighbor search. The
state-of-the-art retrieval systems for billion-scale databases are currently
based on the inverted multi-index, the recently proposed generalization of the
inverted index structure. The multi-index provides a very fine-grained
partition of th... | computer science |
31,061 | Pixel-Level Alignment of Facial Images for High Accuracy Recognition
Using Ensemble of Patches | cs.CV | The variation of pose, illumination and expression makes face recognition
still a challenging problem. As a pre-processing in holistic approaches, faces
are usually aligned by eyes. The proposed method tries to perform a pixel
alignment rather than eye-alignment by mapping the geometry of faces to a
reference face whil... | computer science |
31,062 | SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial
Network | cs.CV | Cross-modal hashing aims to map heterogeneous multimedia data into a common
Hamming space, which can realize fast and flexible retrieval across different
modalities. Supervised cross-modal hashing methods have achieved considerable
progress by incorporating semantic side information. However, they mainly have
two limit... | computer science |
31,063 | Fair comparison of skin detection approaches on publicly available
datasets | cs.CV | Skin detection is the process of discriminating skin and non-skin regions in
a digital image and it is widely used in several applications ranging from hand
gesture analysis to tracking body parts and face detection. Skin detection is a
challenging problem which has drawn extensive attention from the research
community... | computer science |
31,064 | Bitewing Radiography Semantic Segmentation Base on Conditional
Generative Adversarial Nets | cs.CV | Currently, Segmentation of bitewing radiograpy images is a very challenging
task. The focus of the study is to segment it into caries, enamel, dentin,
pulp, crowns, restoration and root canal treatments. The main method of
semantic segmentation of bitewing radiograpy images at this stage is the
U-shaped deep convolutio... | computer science |
31,065 | Unsupervised Typography Transfer | cs.CV | Traditional methods in Chinese typography synthesis view characters as an
assembly of radicals and strokes, but they rely on manual definition of the key
points, which is still time-costing. Some recent work on computer vision
proposes a brand new approach: to treat every Chinese character as an
independent and insepar... | computer science |
31,066 | Generating Triples with Adversarial Networks for Scene Graph
Construction | cs.CV | Driven by successes in deep learning, computer vision research has begun to
move beyond object detection and image classification to more sophisticated
tasks like image captioning or visual question answering. Motivating such
endeavors is the desire for models to capture not only objects present in an
image, but more f... | computer science |
31,067 | Digital Watermarking for Deep Neural Networks | cs.CV | Although deep neural networks have made tremendous progress in the area of
multimedia representation, training neural models requires a large amount of
data and time. It is well-known that utilizing trained models as initial
weights often achieves lower training error than neural networks that are not
pre-trained. A fi... | computer science |
31,068 | An Unsupervised Learning Model for Deformable Medical Image Registration | cs.CV | We present an efficient learning-based algorithm for deformable, pairwise 3D
medical image registration. Current registration methods optimize an energy
function independently for each pair of images, which can be time-consuming for
large data. We define registration as a parametric function, and optimize its
parameter... | computer science |
31,069 | Deep Versus Wide Convolutional Neural Networks for Object Recognition on
Neuromorphic System | cs.CV | In the last decade, special purpose computing systems, such as Neuromorphic
computing, have become very popular in the field of computer vision and machine
learning for classification tasks. In 2015, IBM's released the TrueNorth
Neuromorphic system, kick-starting a new era of Neuromorphic computing.
Alternatively, Deep... | computer science |
31,070 | Encoder-Decoder with Atrous Separable Convolution for Semantic Image
Segmentation | cs.CV | Spatial pyramid pooling module or encode-decoder structure are used in deep
neural networks for semantic segmentation task. The former networks are able to
encode multi-scale contextual information by probing the incoming features with
filters or pooling operations at multiple rates and multiple effective
fields-of-vie... | computer science |
31,071 | Effective Quantization Approaches for Recurrent Neural Networks | cs.CV | Deep learning, and in particular Recurrent Neural Networks (RNN) have shown
superior accuracy in a large variety of tasks including machine translation,
language understanding, and movie frame generation. However, these deep
learning approaches are very expensive in terms of computation. In most cases,
Graphic Processi... | computer science |
31,072 | Going Deeper in Spiking Neural Networks: VGG and Residual Architectures | cs.CV | Over the past few years, Spiking Neural Networks (SNNs) have become popular
as a possible pathway to enable low-power event-driven neuromorphic hardware.
However, their application in machine learning have largely been limited to
very shallow neural network architectures for simple problems. In this paper,
we propose a... | computer science |
31,073 | Spatially adaptive image compression using a tiled deep network | cs.CV | Deep neural networks represent a powerful class of function approximators
that can learn to compress and reconstruct images. Existing image compression
algorithms based on neural networks learn quantized representations with a
constant spatial bit rate across each image. While entropy coding introduces
some spatial var... | computer science |
31,074 | SCK: A sparse coding based key-point detector | cs.CV | All current popular hand-crafted key-point detectors such as Harris corner,
MSER, SIFT, SURF... rely on some specific pre-designed structures for the
detection of corners, blobs, or junctions in an image. In this paper, a novel
sparse coding based key-point detector which requires no particular
pre-designed structures ... | computer science |
31,075 | A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels | cs.CV | The recent success of deep neural networks is powered in part by large-scale
well-labeled training data. However, it is a daunting task to laboriously
annotate an ImageNet-like dateset. On the contrary, it is fairly convenient,
fast, and cheap to collect training images from the Web along with their noisy
labels. This ... | computer science |
31,076 | Driver Gaze Zone Estimation using Convolutional Neural Networks: A
General Framework and Ablative Analysis | cs.CV | Driver gaze has been shown to be an excellent surrogate for driver attention
in intelligent vehicles. With the recent surge of highly autonomous vehicles,
driver gaze can be useful for determining the handoff time to a human driver.
While there has been significant improvement in personalized driver gaze zone
estimatio... | computer science |
31,077 | Deep Image Super Resolution via Natural Image Priors | cs.CV | Single image super-resolution (SR) via deep learning has recently gained
significant attention in the literature. Convolutional neural networks (CNNs)
are typically learned to represent the mapping between low-resolution (LR) and
high-resolution (HR) images/patches with the help of training examples. Most
existing deep... | computer science |
31,078 | From Hashing to CNNs: Training BinaryWeight Networks via Hashing | cs.CV | Deep convolutional neural networks (CNNs) have shown appealing performance on
various computer vision tasks in recent years. This motivates people to deploy
CNNs to realworld applications. However, most of state-of-art CNNs require
large memory and computational resources, which hinders the deployment on
mobile devices... | computer science |
31,079 | Saliency-Enhanced Robust Visual Tracking | cs.CV | Discrete correlation filter (DCF) based trackers have shown considerable
success in visual object tracking. These trackers often make use of low to mid
level features such as histogram of gradients (HoG) and mid-layer activations
from convolution neural networks (CNNs). We argue that including semantically
higher level... | computer science |
31,080 | Peekaboo - Where are the Objects? Structure Adjusting Superpixels | cs.CV | This paper addresses the search for a fast and meaningful image segmentation
in the context of $k$-means clustering. The proposed method builds on a
widely-used local version of Lloyd's algorithm, called Simple Linear Iterative
Clustering (SLIC). We propose an algorithm which extends SLIC to dynamically
adjust the loca... | computer science |
31,081 | Archetypal Analysis for Sparse Representation-based Hyperspectral
Sub-pixel Quantification | cs.CV | The estimation of land cover fractions from remote sensing images is a
frequently used indicator of the environmental quality. This paper focuses on
the quantification of land cover fractions in an urban area of Berlin, Germany,
using simulated hyperspectral EnMAP data with a spatial resolution of
30m$\times$30m. We us... | computer science |
31,082 | From Selective Deep Convolutional Features to Compact Binary
Representations for Image Retrieval | cs.CV | Convolutional Neural Network (CNN) is a very powerful approach to extract
discriminative local descriptors for effective image search. Recent work adopts
fine-tuned strategies to further improve the discriminative power of the
descriptors. Taking a different approach, in this paper, we propose a novel
framework to achi... | computer science |
31,083 | Deep Reinforcement Learning for Image Hashing | cs.CV | Deep hashing methods have received much attention recently, which achieve
promising results by taking advantage of the strong representation power of
deep networks. However, most existing deep hashing methods learn a whole set of
hashing functions independently and directly, while ignore the correlation
between differe... | computer science |
31,084 | A Deep Unsupervised Learning Approach Toward MTBI Identification Using
Diffusion MRI | cs.CV | Mild traumatic brain injury (mTBI) is a growing public health problem with an
estimated incidence of one million people annually in US. Neurocognitive tests
have been used to both assess the patient condition and to monitor the patient
progress. This work aims to directly use diffusion MR images taken shortly
after inj... | computer science |
31,085 | Rotate your Networks: Better Weight Consolidation and Less Catastrophic
Forgetting | cs.CV | In this paper we propose an approach to avoiding catastrophic forgetting in
sequential task learning scenarios. Our technique is based on a network
reparameterization that approximately diagonalizes the Fisher Information
Matrix of the network parameters. This reparameterization takes the form of a
factorized rotation ... | computer science |
31,086 | TSViz: Demystification of Deep Learning Models for Time-Series Analysis | cs.CV | This paper presents a novel framework for demystification of convolutional
deep learning models for time series analysis. This is a step towards making
informed/explainable decisions in the domain of time series, powered by deep
learning. There have been numerous efforts to increase the interpretability of
image-centri... | computer science |
31,087 | Practical Issues of Action-conditioned Next Image Prediction | cs.CV | The problem of action-conditioned image prediction is to predict the expected
next frame given the current camera frame the robot observes and an action
selected by the robot. We provide the first comparison of two recent popular
models, especially for image prediction on cars. Our major finding is that
action tiling e... | computer science |
31,088 | Texture Segmentation Based Video Compression Using Convolutional Neural
Networks | cs.CV | There has been a growing interest in using different approaches to improve
the coding efficiency of modern video codec in recent years as demand for
web-based video consumption increases. In this paper, we propose a model-based
approach that uses texture analysis/synthesis to reconstruct blocks in texture
regions of a ... | computer science |
31,089 | Hole Filling with Multiple Reference Views in DIBR View Synthesis | cs.CV | Depth-image-based rendering (DIBR) oriented view synthesis has been widely
employed in the current depth-based 3D video systems by synthesizing a virtual
view from an arbitrary viewpoint. However, holes may appear in the synthesized
view due to disocclusion, thus significantly degrading the quality.
Consequently, effor... | computer science |
31,090 | Automatic segmenting teeth in X-ray images: Trends, a novel data set,
benchmarking and future perspectives | cs.CV | This review presents an in-depth study of the literature on segmentation
methods applied in dental imaging. Ten segmentation methods were studied and
categorized according to the type of the segmentation method (region-based,
threshold-based, cluster-based, boundary-based or watershed-based), type of
X-ray images used ... | computer science |
31,091 | Tracking Noisy Targets: A Review of Recent Object Tracking Approaches | cs.CV | Visual object tracking is an important computer vision problem with numerous
real-world applications including human-computer interaction, autonomous
vehicles, robotics, motion-based recognition, video indexing, surveillance and
security. In this paper, we aim to extensively review the latest trends and
advances in the... | computer science |
31,092 | Boosting Image Forgery Detection using Resampling Features and Copy-move
analysis | cs.CV | Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these comp... | computer science |
31,093 | Tracking all members of a honey bee colony over their lifetime | cs.CV | Computational approaches to the analysis of collective behavior in social
insects increasingly rely on motion paths as an intermediate data layer from
which one can infer individual behaviors or social interactions. Honey bees are
a popular model for learning and memory. Previous experience has been shown to
affect and... | computer science |
31,094 | Full-Frame Scene Coordinate Regression for Image-Based Localization | cs.CV | Image-based localization, or camera relocalization, is a fundamental problem
in computer vision and robotics, and it refers to estimating camera pose from
an image. Recent state-of-the-art approaches use learning based methods, such
as Random Forests (RFs) and Convolutional Neural Networks (CNNs), to regress
for each p... | computer science |
31,095 | RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic
Videos Without Manual Annotations | cs.CV | Objective: Accurate surgery duration estimation is necessary for optimal OR
planning, which plays an important role for patient comfort and safety as well
as resource optimization. It is however challenging to preoperatively predict
surgery duration since it varies significantly depending on the patient
condition, surg... | computer science |
31,096 | Piecewise Flat Embedding for Image Segmentation | cs.CV | We propose a new nonlinear embedding -- Piecewise Flat Embedding (PFE) -- for
image segmentation. Based on the theory of sparse signal recovery, piecewise
flat embedding attempts to recover a piecewise constant image representation
with sparse region boundaries and sparse cluster value scattering. The
resultant piecewi... | computer science |
31,097 | Multiple Target Tracking by Learning Feature Representation and Distance
Metric Jointly | cs.CV | Designing a robust affinity model is the key issue in multiple target
tracking (MTT). This paper proposes a novel affinity model by learning feature
representation and distance metric jointly in a unified deep architecture.
Specifically, we design a CNN network to obtain appearance cue tailored towards
person Re-ID, an... | computer science |
31,098 | Triplet-based Deep Similarity Learning for Person Re-Identification | cs.CV | In recent years, person re-identification (re-id) catches great attention in
both computer vision community and industry. In this paper, we propose a new
framework for person re-identification with a triplet-based deep similarity
learning using convolutional neural networks (CNNs). The network is trained
with triplet i... | computer science |
31,099 | Video Event Recognition and Anomaly Detection by Combining Gaussian
Process and Hierarchical Dirichlet Process Models | cs.CV | In this paper, we present an unsupervised learning framework for analyzing
activities and interactions in surveillance videos. In our framework, three
levels of video events are connected by Hierarchical Dirichlet Process (HDP)
model: low-level visual features, simple atomic activities, and multi-agent
interactions. At... | computer science |
31,100 | Unsupervised Deep Domain Adaptation for Pedestrian Detection | cs.CV | This paper addresses the problem of unsupervised domain adaptation on the
task of pedestrian detection in crowded scenes. First, we utilize an iterative
algorithm to iteratively select and auto-annotate positive pedestrian samples
with high confidence as the training samples for the target domain. Meanwhile,
we also re... | computer science |
31,101 | Temporally Object-based Video Co-Segmentation | cs.CV | In this paper, we propose an unsupervised video object co-segmentation
framework based on the primary object proposals to extract the common
foreground object(s) from a given video set. In addition to the objectness
attributes and motion coherence our framework exploits the temporal consistency
of the object-like regio... | computer science |
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