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27,602 | Sparse Autoencoder for Unsupervised Nucleus Detection and Representation
in Histopathology Images | cs.CV | Histopathology images are crucial to the study of complex diseases such as
cancer. The histologic characteristics of nuclei play a key role in disease
diagnosis, prognosis and analysis. In this work, we propose a sparse
Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus
detection and feature e... | computer science |
27,603 | A Good Practice Towards Top Performance of Face Recognition: Transferred
Deep Feature Fusion | cs.CV | Unconstrained face recognition performance evaluations have traditionally
focused on Labeled Faces in the Wild (LFW) dataset for imagery and the
YouTubeFaces (YTF) dataset for videos in the last couple of years. Spectacular
progress in this field has resulted in saturation on verification and
identification accuracies ... | computer science |
27,604 | Learning a Variational Network for Reconstruction of Accelerated MRI
Data | cs.CV | Purpose: To allow fast and high-quality reconstruction of clinical
accelerated multi-coil MR data by learning a variational network that combines
the mathematical structure of variational models with deep learning.
Theory and Methods: Generalized compressed sensing reconstruction formulated
as a variational model is ... | computer science |
27,605 | A Comparison of Directional Distances for Hand Pose Estimation | cs.CV | Benchmarking methods for 3d hand tracking is still an open problem due to the
difficulty of acquiring ground truth data. We introduce a new dataset and
benchmarking protocol that is insensitive to the accumulative error of other
protocols. To this end, we create testing frame pairs of increasing difficulty
and measure ... | computer science |
27,606 | Convolutional neural networks for segmentation and object detection of
human semen | cs.CV | We compare a set of convolutional neural network (CNN) architectures for the
task of segmenting and detecting human sperm cells in an image taken from a
semen sample. In contrast to previous work, samples are not stained or washed
to allow for full sperm quality analysis, making analysis harder due to
clutter. Our resu... | computer science |
27,607 | Truncating Wide Networks using Binary Tree Architectures | cs.CV | Recent study shows that a wide deep network can obtain accuracy comparable to
a deeper but narrower network. Compared to narrower and deeper networks, wide
networks employ relatively less number of layers and have various important
benefits, such that they have less running time on parallel computing devices,
and they ... | computer science |
27,608 | Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model
with Salient Points | cs.CV | Hand motion capture has been an active research topic in recent years,
following the success of full-body pose tracking. Despite similarities, hand
tracking proves to be more challenging, characterized by a higher
dimensionality, severe occlusions and self-similarity between fingers. For this
reason, most approaches re... | computer science |
27,609 | Block-Matching Convolutional Neural Network for Image Denoising | cs.CV | There are two main streams in up-to-date image denoising algorithms:
non-local self similarity (NSS) prior based methods and convolutional neural
network (CNN) based methods. The NSS based methods are favorable on images with
regular and repetitive patterns while the CNN based methods perform better on
irregular struct... | computer science |
27,610 | 3D Object Reconstruction from Hand-Object Interactions | cs.CV | Recent advances have enabled 3d object reconstruction approaches using a
single off-the-shelf RGB-D camera. Although these approaches are successful for
a wide range of object classes, they rely on stable and distinctive geometric
or texture features. Many objects like mechanical parts, toys, household or
decorative ar... | computer science |
27,611 | Spatiotemporal Networks for Video Emotion Recognition | cs.CV | Our experiment adapts several popular deep learning methods as well as some
traditional methods on the problem of video emotion recognition. In our
experiment, we use the CNN-LSTM architecture for visual information extraction
and classification and utilize traditional methods such as for audio feature
classification. ... | computer science |
27,612 | The 2017 DAVIS Challenge on Video Object Segmentation | cs.CV | We present the 2017 DAVIS Challenge on Video Object Segmentation, a public
dataset, benchmark, and competition specifically designed for the task of video
object segmentation. Following the footsteps of other successful initiatives,
such as ILSVRC and PASCAL VOC, which established the avenue of research in the
fields o... | computer science |
27,613 | Hierarchical Surface Prediction for 3D Object Reconstruction | cs.CV | Recently, Convolutional Neural Networks have shown promising results for 3D
geometry prediction. They can make predictions from very little input data such
as a single color image. A major limitation of such approaches is that they
only predict a coarse resolution voxel grid, which does not capture the surface
of the o... | computer science |
27,614 | Unsupervised Action Proposal Ranking through Proposal Recombination | cs.CV | Recently, action proposal methods have played an important role in action
recognition tasks, as they reduce the search space dramatically. Most
unsupervised action proposal methods tend to generate hundreds of action
proposals which include many noisy, inconsistent, and unranked action
proposals, while supervised actio... | computer science |
27,615 | AMC: Attention guided Multi-modal Correlation Learning for Image Search | cs.CV | Given a user's query, traditional image search systems rank images according
to its relevance to a single modality (e.g., image content or surrounding
text). Nowadays, an increasing number of images on the Internet are available
with associated meta data in rich modalities (e.g., titles, keywords, tags,
etc.), which ca... | computer science |
27,616 | Cascaded Segmentation-Detection Networks for Word-Level Text Spotting | cs.CV | We introduce an algorithm for word-level text spotting that is able to
accurately and reliably determine the bounding regions of individual words of
text "in the wild". Our system is formed by the cascade of two convolutional
neural networks. The first network is fully convolutional and is in charge of
detecting areas ... | computer science |
27,617 | Guided Proofreading of Automatic Segmentations for Connectomics | cs.CV | Automatic cell image segmentation methods in connectomics produce merge and
split errors, which require correction through proofreading. Previous research
has identified the visual search for these errors as the bottleneck in
interactive proofreading. To aid error correction, we develop two classifiers
that automatical... | computer science |
27,618 | Simultaneous Feature Aggregating and Hashing for Large-scale Image
Search | cs.CV | In most state-of-the-art hashing-based visual search systems, local image
descriptors of an image are first aggregated as a single feature vector. This
feature vector is then subjected to a hashing function that produces a binary
hash code. In previous work, the aggregating and the hashing processes are
designed indepe... | computer science |
27,619 | OctNetFusion: Learning Depth Fusion from Data | cs.CV | In this paper, we present a learning based approach to depth fusion, i.e.,
dense 3D reconstruction from multiple depth images. The most common approach to
depth fusion is based on averaging truncated signed distance functions, which
was originally proposed by Curless and Levoy in 1996. While this method is
simple and p... | computer science |
27,620 | ME R-CNN: Multi-Expert R-CNN for Object Detection | cs.CV | We introduce Multi-Expert Region-based CNN (ME R-CNN) which is equipped with
multiple experts and built on top of the R-CNN framework known to be one of the
state-of-the-art object detection methods. ME R-CNN focuses in better capturing
the appearance variations caused by different shapes, poses, and viewing
angles. Th... | computer science |
27,621 | Deep Depth From Focus | cs.CV | Depth from Focus (DFF) is one of the classical ill-posed inverse problems in
computer vision. Most approaches recover the depth at each pixel based on the
focal setting which exhibits maximal sharpness. Yet, it is not obvious how to
reliably estimate the sharpness level, particularly in low-textured areas. In
this pape... | computer science |
27,622 | Pose2Instance: Harnessing Keypoints for Person Instance Segmentation | cs.CV | Human keypoints are a well-studied representation of people.We explore how to
use keypoint models to improve instance-level person segmentation. The main
idea is to harness the notion of a distance transform of oracle provided
keypoints or estimated keypoint heatmaps as a prior for person instance
segmentation task wit... | computer science |
27,623 | Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition | cs.CV | In this paper we address the problem of human action recognition from video
sequences. Inspired by the exemplary results obtained via automatic feature
learning and deep learning approaches in computer vision, we focus our
attention towards learning salient spatial features via a convolutional neural
network (CNN) and ... | computer science |
27,624 | Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point
Cloud Models | cs.CV | We present a new deep learning architecture (called Kd-network) that is
designed for 3D model recognition tasks and works with unstructured point
clouds. The new architecture performs multiplicative transformations and share
parameters of these transformations according to the subdivisions of the point
clouds imposed o... | computer science |
27,625 | Joint Regression and Ranking for Image Enhancement | cs.CV | Research on automated image enhancement has gained momentum in recent years,
partially due to the need for easy-to-use tools for enhancing pictures captured
by ubiquitous cameras on mobile devices. Many of the existing leading methods
employ machine-learning-based techniques, by which some enhancement parameters
for a ... | computer science |
27,626 | Estimation of Tissue Microstructure Using a Deep Network Inspired by a
Sparse Reconstruction Framework | cs.CV | Diffusion magnetic resonance imaging (dMRI) provides a unique tool for
noninvasively probing the microstructure of the neuronal tissue. The NODDI
model has been a popular approach to the estimation of tissue microstructure in
many neuroscience studies. It represents the diffusion signals with three types
of diffusion i... | computer science |
27,627 | A Computational Approach to Relative Aesthetics | cs.CV | Computational visual aesthetics has recently become an active research area.
Existing state-of-art methods formulate this as a binary classification task
where a given image is predicted to be beautiful or not. In many applications
such as image retrieval and enhancement, it is more important to rank images
based on th... | computer science |
27,628 | A Structured Approach to Predicting Image Enhancement Parameters | cs.CV | Social networking on mobile devices has become a commonplace of everyday
life. In addition, photo capturing process has become trivial due to the
advances in mobile imaging. Hence people capture a lot of photos everyday and
they want them to be visually-attractive. This has given rise to automated,
one-touch enhancemen... | computer science |
27,629 | Relative Learning from Web Images for Content-adaptive Enhancement | cs.CV | Personalized and content-adaptive image enhancement can find many
applications in the age of social media and mobile computing. This paper
presents a relative-learning-based approach, which, unlike previous methods,
does not require matching original and enhanced images for training. This
allows the use of massive onli... | computer science |
27,630 | Improving Vision-based Self-positioning in Intelligent Transportation
Systems via Integrated Lane and Vehicle Detection | cs.CV | Traffic congestion is a widespread problem. Dynamic traffic routing systems
and congestion pricing are getting importance in recent research. Lane
prediction and vehicle density estimation is an important component of such
systems. We introduce a novel problem of vehicle self-positioning which
involves predicting the n... | computer science |
27,631 | Investigating Human Factors in Image Forgery Detection | cs.CV | In today's age of internet and social media, one can find an enormous volume
of forged images on-line. These images have been used in the past to convey
falsified information and achieve harmful intentions. The spread and the effect
of the social media only makes this problem more severe. While creating forged
images h... | computer science |
27,632 | Classification of Diabetic Retinopathy Images Using Multi-Class
Multiple-Instance Learning Based on Color Correlogram Features | cs.CV | All people with diabetes have the risk of developing diabetic retinopathy
(DR), a vision-threatening complication. Early detection and timely treatment
can reduce the occurrence of blindness due to DR. Computer-aided diagnosis has
the potential benefit of improving the accuracy and speed in DR detection. This
study is ... | computer science |
27,633 | Smart Mining for Deep Metric Learning | cs.CV | To solve deep metric learning problems and producing feature embeddings,
current methodologies will commonly use a triplet model to minimise the
relative distance between samples from the same class and maximise the relative
distance between samples from different classes. Though successful, the
training convergence of... | computer science |
27,634 | Incremental Tube Construction for Human Action Detection | cs.CV | Current state-of-the-art action detection systems are tailored for offline
batch-processing applications. However, for online applications like
human-robot interaction, current systems fall short, either because they only
detect one action per video, or because they assume that the entire video is
available ahead of ti... | computer science |
27,635 | On the Relation between Color Image Denoising and Classification | cs.CV | Large amount of image denoising literature focuses on single channel images
and often experimentally validates the proposed methods on tens of images at
most. In this paper, we investigate the interaction between denoising and
classification on large scale dataset. Inspired by classification models, we
propose a novel ... | computer science |
27,636 | The UMCD Dataset | cs.CV | In recent years, the technological improvements of low-cost small-scale
Unmanned Aerial Vehicles (UAVs) are promoting an ever-increasing use of them in
different tasks. In particular, the use of small-scale UAVs is useful in all
these low-altitude tasks in which common UAVs cannot be adopted, such as
recurrent comprehe... | computer science |
27,637 | Non-Convex Weighted Lp Minimization based Group Sparse Representation
Framework for Image Denoising | cs.CV | Nonlocal image representation or group sparsity has attracted considerable
interest in various low-level vision tasks and has led to several
state-of-the-art image denoising techniques, such as BM3D, LSSC. In the past,
convex optimization with sparsity-promoting convex regularization was usually
regarded as a standard ... | computer science |
27,638 | Effect of Super Resolution on High Dimensional Features for Unsupervised
Face Recognition in the Wild | cs.CV | Majority of the face recognition algorithms use query faces captured from
uncontrolled, in the wild, environment. Often caused by the cameras limited
capabilities, it is common for these captured facial images to be blurred or
low resolution. Super resolution algorithms are therefore crucial in improving
the resolution... | computer science |
27,639 | Weakly Supervised Dense Video Captioning | cs.CV | This paper focuses on a novel and challenging vision task, dense video
captioning, which aims to automatically describe a video clip with multiple
informative and diverse caption sentences. The proposed method is trained
without explicit annotation of fine-grained sentence to video region-sequence
correspondence, but i... | computer science |
27,640 | Isotropic reconstruction of 3D fluorescence microscopy images using
convolutional neural networks | cs.CV | Fluorescence microscopy images usually show severe anisotropy in axial versus
lateral resolution. This hampers downstream processing, i.e. the automatic
extraction of quantitative biological data. While deconvolution methods and
other techniques to address this problem exist, they are either time consuming
to apply or ... | computer science |
27,641 | Generating Descriptions with Grounded and Co-Referenced People | cs.CV | Learning how to generate descriptions of images or videos received major
interest both in the Computer Vision and Natural Language Processing
communities. While a few works have proposed to learn a grounding during the
generation process in an unsupervised way (via an attention mechanism), it
remains unclear how good t... | computer science |
27,642 | Generate To Adapt: Aligning Domains using Generative Adversarial
Networks | cs.CV | Domain Adaptation is an actively researched problem in Computer Vision. In
this work, we propose an approach that leverages unsupervised data to bring the
source and target distributions closer in a learned joint feature space. We
accomplish this by inducing a symbiotic relationship between the learned
embedding and a ... | computer science |
27,643 | Action Representation Using Classifier Decision Boundaries | cs.CV | Most popular deep learning based models for action recognition are designed
to generate separate predictions within their short temporal windows, which are
often aggregated by heuristic means to assign an action label to the full video
segment. Given that not all frames from a video characterize the underlying
action, ... | computer science |
27,644 | Beyond triplet loss: a deep quadruplet network for person
re-identification | cs.CV | Person re-identification (ReID) is an important task in wide area video
surveillance which focuses on identifying people across different cameras.
Recently, deep learning networks with a triplet loss become a common framework
for person ReID. However, the triplet loss pays main attentions on obtaining
correct orders on... | computer science |
27,645 | Object-Part Attention Model for Fine-grained Image Classification | cs.CV | Fine-grained image classification is to recognize hundreds of subcategories
belonging to the same basic-level category, such as 200 subcategories belonging
to the bird, which is highly challenging due to large variance in the same
subcategory and small variance among different subcategories. Existing methods
generally ... | computer science |
27,646 | How to Make an Image More Memorable? A Deep Style Transfer Approach | cs.CV | Recent works have shown that it is possible to automatically predict
intrinsic image properties like memorability. In this paper, we take a step
forward addressing the question: "Can we make an image more memorable?".
Methods for automatically increasing image memorability would have an impact in
many application field... | computer science |
27,647 | Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation | cs.CV | Most state-of-the-art motion segmentation algorithms draw their potential
from modeling motion differences of local entities such as point trajectories
in terms of pairwise potentials in graphical models. Inference in instances of
minimum cost multicut problems defined on such graphs al- lows to optimize the
number of ... | computer science |
27,648 | A Convolution Tree with Deconvolution Branches: Exploiting Geometric
Relationships for Single Shot Keypoint Detection | cs.CV | Recently, Deep Convolution Networks (DCNNs) have been applied to the task of
face alignment and have shown potential for learning improved feature
representations. Although deeper layers can capture abstract concepts like
pose, it is difficult to capture the geometric relationships among the
keypoints in DCNNs. In this... | computer science |
27,649 | Automated Latent Fingerprint Recognition | cs.CV | Latent fingerprints are one of the most important and widely used evidence in
law enforcement and forensic agencies worldwide. Yet, NIST evaluations show
that the performance of state-of-the-art latent recognition systems is far from
satisfactory. An automated latent fingerprint recognition system with high
accuracy is... | computer science |
27,650 | Semantically-Guided Video Object Segmentation | cs.CV | This paper tackles the problem of semi-supervised video object segmentation,
that is, segmenting an object in a sequence given its mask in the first frame.
One of the main challenges in this scenario is the change of appearance of the
objects of interest. Their semantics, on the other hand, do not vary. This
paper inve... | computer science |
27,651 | Convolutional Neural Pyramid for Image Processing | cs.CV | We propose a principled convolutional neural pyramid (CNP) framework for
general low-level vision and image processing tasks. It is based on the
essential finding that many applications require large receptive fields for
structure understanding. But corresponding neural networks for regression
either stack many layers ... | computer science |
27,652 | "RAPID" Regions-of-Interest Detection In Big Histopathological Images | cs.CV | The sheer volume and size of histopathological images (e.g.,10^6 MPixel)
underscores the need for faster and more accurate Regions-of-interest (ROI)
detection algorithms. In this paper, we propose such an algorithm, which has
four main components that help achieve greater accuracy and faster speed:
First, while using c... | computer science |
27,653 | Supervised Deep Hashing for Hierarchical Labeled Data | cs.CV | Recently, hashing methods have been widely used in large-scale image
retrieval. However, most existing hashing methods did not consider the
hierarchical relation of labels, which means that they ignored the rich
information stored in the hierarchy. Moreover, most of previous works treat
each bit in a hash code equally,... | computer science |
27,654 | Generalized Rank Pooling for Activity Recognition | cs.CV | Most popular deep models for action recognition split video sequences into
short sub-sequences consisting of a few frames; frame-based features are then
pooled for recognizing the activity. Usually, this pooling step discards the
temporal order of the frames, which could otherwise be used for better
recognition. Toward... | computer science |
27,655 | Partial Face Detection in the Mobile Domain | cs.CV | Generic face detection algorithms do not perform well in the mobile domain
due to significant presence of occluded and partially visible faces. One
promising technique to handle the challenge of partial faces is to design face
detectors based on facial segments. In this paper two different approaches of
facial segment-... | computer science |
27,656 | Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular
Depth Estimation | cs.CV | This paper addresses the problem of depth estimation from a single still
image. Inspired by recent works on multi- scale convolutional neural networks
(CNN), we propose a deep model which fuses complementary information derived
from multiple CNN side outputs. Different from previous methods, the
integration is obtained... | computer science |
27,657 | ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical
Coherence Tomography using Fully Convolutional Network | cs.CV | Optical coherence tomography (OCT) is used for non-invasive diagnosis of
diabetic macular edema assessing the retinal layers. In this paper, we propose
a new fully convolutional deep architecture, termed ReLayNet, for end-to-end
segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses
a contracti... | computer science |
27,658 | Egocentric Video Description based on Temporally-Linked Sequences | cs.CV | Egocentric vision consists in acquiring images along the day from a first
person point-of-view using wearable cameras. The automatic analysis of this
information allows to discover daily patterns for improving the quality of life
of the user. A natural topic that arises in egocentric vision is storytelling,
that is, ho... | computer science |
27,659 | Semi-Latent GAN: Learning to generate and modify facial images from
attributes | cs.CV | Generating and manipulating human facial images using high-level attributal
controls are important and interesting problems. The models proposed in
previous work can solve one of these two problems (generation or manipulation),
but not both coherently. This paper proposes a novel model that learns how to
both generate ... | computer science |
27,660 | Could you guess an interesting movie from the posters?: An evaluation of
vision-based features on movie poster database | cs.CV | In this paper, we aim to estimate the Winner of world-wide film festival from
the exhibited movie poster. The task is an extremely challenging because the
estimation must be done with only an exhibited movie poster, without any film
ratings and box-office takings. In order to tackle this problem, we have
created a new ... | computer science |
27,661 | Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor | cs.CV | We present an approach for real-time, robust and accurate hand pose
estimation from moving egocentric RGB-D cameras in cluttered real environments.
Existing methods typically fail for hand-object interactions in cluttered
scenes imaged from egocentric viewpoints, common for virtual or augmented
reality applications. Ou... | computer science |
27,662 | High-Quality Correspondence and Segmentation Estimation for Dual-Lens
Smart-Phone Portraits | cs.CV | Estimating correspondence between two images and extracting the foreground
object are two challenges in computer vision. With dual-lens smart phones, such
as iPhone 7Plus and Huawei P9, coming into the market, two images of slightly
different views provide us new information to unify the two topics. We propose
a joint ... | computer science |
27,663 | DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial
Action Coding | cs.CV | Human face exhibits an inherent hierarchy in its representations (i.e.,
holistic facial expressions can be encoded via a set of facial action units
(AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown
great results in unsupervised extraction of hierarchical latent representations
from large amo... | computer science |
27,664 | Investigating Natural Image Pleasantness Recognition using Deep Features
and Eye Tracking for Loosely Controlled Human-computer Interaction | cs.CV | This paper revisits recognition of natural image pleasantness by employing
deep convolutional neural networks and affordable eye trackers. There exist
several approaches to recognize image pleasantness: (1) computer vision, and
(2) psychophysical signals. For natural images, computer vision approaches have
not been as ... | computer science |
27,665 | Hand3D: Hand Pose Estimation using 3D Neural Network | cs.CV | We propose a novel 3D neural network architecture for 3D hand pose estimation
from a single depth image. Different from previous works that mostly run on 2D
depth image domain and require intermediate or post process to bring in the
supervision from 3D space, we convert the depth map to a 3D volumetric
representation, ... | computer science |
27,666 | Clothing and People - A Social Signal Processing Perspective | cs.CV | In our society and century, clothing is not anymore used only as a means for
body protection. Our paper builds upon the evidence, studied within the social
sciences, that clothing brings a clear communicative message in terms of social
signals, influencing the impression and behaviour of others towards a person.
In fac... | computer science |
27,667 | Learned Watershed: End-to-End Learning of Seeded Segmentation | cs.CV | Learned boundary maps are known to outperform hand- crafted ones as a basis
for the watershed algorithm. We show, for the first time, how to train
watershed computation jointly with boundary map prediction. The estimator for
the merging priorities is cast as a neural network that is con- volutional
(over space) and rec... | computer science |
27,668 | Deep Unsupervised Similarity Learning using Partially Ordered Sets | cs.CV | Unsupervised learning of visual similarities is of paramount importance to
computer vision, particularly due to lacking training data for fine-grained
similarities. Deep learning of similarities is often based on relationships
between pairs or triplets of samples. Many of these relations are unreliable
and mutually con... | computer science |
27,669 | Automated Unsupervised Segmentation of Liver Lesions in CT scans via
Cahn-Hilliard Phase Separation | cs.CV | The segmentation of liver lesions is crucial for detection, diagnosis and
monitoring progression of liver cancer. However, design of accurate automated
methods remains challenging due to high noise in CT scans, low contrast between
liver and lesions, as well as large lesion variability. We propose a 3D
automatic, unsup... | computer science |
27,670 | Three-Dimensional Segmentation of Vesicular Networks of Fungal Hyphae in
Macroscopic Microscopy Image Stacks | cs.CV | Automating the extraction and quantification of features from
three-dimensional (3-D) image stacks is a critical task for advancing computer
vision research. The union of 3-D image acquisition and analysis enables the
quantification of biological resistance of a plant tissue to fungal infection
through the analysis of ... | computer science |
27,671 | Pixelwise Instance Segmentation with a Dynamically Instantiated Network | cs.CV | Semantic segmentation and object detection research have recently achieved
rapid progress. However, the former task has no notion of different instances
of the same object, and the latter operates at a coarse, bounding-box level. We
propose an Instance Segmentation system that produces a segmentation map where
each pix... | computer science |
27,672 | Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D
Scenes | cs.CV | The use of rendered images, whether from completely synthetic datasets or
from 3D reconstructions, is increasingly prevalent in vision tasks. However,
little attention has been given to how the selection of viewpoints affects the
performance of rendered training sets. In this paper, we propose a data-driven
approach to... | computer science |
27,673 | GoDP: Globally optimized dual pathway system for facial landmark
localization in-the-wild | cs.CV | Facial landmark localization is a fundamental module for pose-invariant face
recognition. The most common approach for facial landmark detection is cascaded
regression, which is composed of two steps: feature extraction and facial shape
regression. Recent methods employ deep convolutional networks to extract robust
fea... | computer science |
27,674 | A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image
Reconstruction | cs.CV | Inspired by recent advances in deep learning, we propose a framework for
reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images
from undersampled data using a deep cascade of convolutional neural networks
(CNNs) to accelerate the data acquisition process. In particular, we address
the case where ... | computer science |
27,675 | Learning Cross-Modal Deep Representations for Robust Pedestrian
Detection | cs.CV | This paper presents a novel method for detecting pedestrians under adverse
illumination conditions. Our approach relies on a novel cross-modality learning
framework and it is based on two main phases. First, given a multimodal
dataset, a deep convolutional network is employed to learn a non-linear
mapping, modeling the... | computer science |
27,676 | Seismic facies recognition based on prestack data using deep
convolutional autoencoder | cs.CV | Prestack seismic data carries much useful information that can help us find
more complex atypical reservoirs. Therefore, we are increasingly inclined to
use prestack seismic data for seis- mic facies recognition. However, due to the
inclusion of ex- cessive redundancy, effective feature extraction from prestack
seismic... | computer science |
27,677 | Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised
Approach | cs.CV | In this paper, we study the task of 3D human pose estimation in the wild.
This task is challenging due to lack of training data, as existing datasets are
either in the wild images with 2D pose or in the lab images with 3D pose.
We propose a weakly-supervised transfer learning method that uses mixed 2D
and 3D labels i... | computer science |
27,678 | Coupled Deep Learning for Heterogeneous Face Recognition | cs.CV | Heterogeneous face matching is a challenge issue in face recognition due to
large domain difference as well as insufficient pairwise images in different
modalities during training. This paper proposes a coupled deep learning (CDL)
approach for the heterogeneous face matching. CDL seeks a shared feature space
in which t... | computer science |
27,679 | A New Pseudo-color Technique Based on Intensity Information Protection
for Passive Sensor Imagery | cs.CV | Remote sensing image processing is so important in geo-sciences. Images which
are obtained by different types of sensors might initially be unrecognizable.
To make an acceptable visual perception in the images, some pre-processing
steps (for removing noises and etc) are preformed which they affect the
analysis of image... | computer science |
27,680 | First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose
Annotations | cs.CV | In this work we study the use of 3D hand poses to recognize first-person hand
actions interacting with 3D objects. Towards this goal, we collected RGB-D
video sequences of more than 100K frames of 45 daily hand action categories,
involving 25 different objects in several hand grasp configurations. To obtain
high qualit... | computer science |
27,681 | DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks | cs.CV | Despite a rapid rise in the quality of built-in smartphone cameras, their
physical limitations - small sensor size, compact lenses and the lack of
specific hardware, - impede them to achieve the quality results of DSLR
cameras. In this work we present an end-to-end deep learning approach that
bridges this gap by transl... | computer science |
27,682 | Metric Learning in Codebook Generation of Bag-of-Words for Person
Re-identification | cs.CV | Person re-identification is generally divided into two part: first how to
represent a pedestrian by discriminative visual descriptors and second how to
compare them by suitable distance metrics. Conventional methods isolate these
two parts, the first part usually unsupervised and the second part supervised.
The Bag-of-... | computer science |
27,683 | DualGAN: Unsupervised Dual Learning for Image-to-Image Translation | cs.CV | Conditional Generative Adversarial Networks (GANs) for cross-domain
image-to-image translation have made much progress recently. Depending on the
task complexity, thousands to millions of labeled image pairs are needed to
train a conditional GAN. However, human labeling is expensive, even
impractical, and large quantit... | computer science |
27,684 | An Empirical Evaluation of Visual Question Answering for Novel Objects | cs.CV | We study the problem of answering questions about images in the harder
setting, where the test questions and corresponding images contain novel
objects, which were not queried about in the training data. Such setting is
inevitable in real world-owing to the heavy tailed distribution of the visual
categories, there woul... | computer science |
27,685 | Deep Generative Adversarial Compression Artifact Removal | cs.CV | Compression artifacts arise in images whenever a lossy compression algorithm
is applied. These artifacts eliminate details present in the original image, or
add noise and small structures; because of these effects they make images less
pleasant for the human eye, and may also lead to decreased performance of
computer v... | computer science |
27,686 | Motion Saliency Based Automatic Delineation of Glottis Contour in
High-speed Digital Images | cs.CV | In recent years, high-speed videoendoscopy (HSV) has significantly aided the
diagnosis of voice pathologies and furthered the understanding the voice
production in recent years. As the first step of these studies, automatic
segmentation of glottal images till presents a major challenge for this
technique. In this paper... | computer science |
27,687 | Modeling Temporal Dynamics and Spatial Configurations of Actions Using
Two-Stream Recurrent Neural Networks | cs.CV | Recently, skeleton based action recognition gains more popularity due to
cost-effective depth sensors coupled with real-time skeleton estimation
algorithms. Traditional approaches based on handcrafted features are limited to
represent the complexity of motion patterns. Recent methods that use Recurrent
Neural Networks ... | computer science |
27,688 | BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis | cs.CV | In this paper we introduce a large-scale hand pose dataset, collected using a
novel capture method. Existing datasets are either generated synthetically or
captured using depth sensors: synthetic datasets exhibit a certain level of
appearance difference from real depth images, and real datasets are limited in
quantity ... | computer science |
27,689 | ClusterNet: Detecting Small Objects in Large Scenes by Exploiting
Spatio-Temporal Information | cs.CV | Object detection in wide area motion imagery (WAMI) has drawn the attention
of the computer vision research community for a number of years. WAMI proposes
a number of unique challenges including extremely small object sizes, both
sparse and densely-packed objects, and extremely large search spaces (large
video frames).... | computer science |
27,690 | Automatic Liver Lesion Detection using Cascaded Deep Residual Networks | cs.CV | Automatic segmentation of liver lesions is a fundamental requirement towards
the creation of computer aided diagnosis (CAD) and decision support systems
(CDS). Traditional segmentation approaches depend heavily upon hand-crafted
features and a priori knowledge of the user. As such, these methods are
difficult to adopt ... | computer science |
27,691 | DeepPermNet: Visual Permutation Learning | cs.CV | We present a principled approach to uncover the structure of visual data by
solving a novel deep learning task coined visual permutation learning. The goal
of this task is to find the permutation that recovers the structure of data
from shuffled versions of it. In the case of natural images, this task boils
down to rec... | computer science |
27,692 | Detail-revealing Deep Video Super-resolution | cs.CV | Previous CNN-based video super-resolution approaches need to align multiple
frames to the reference. In this paper, we show that proper frame alignment and
motion compensation is crucial for achieving high quality results. We
accordingly propose a `sub-pixel motion compensation' (SPMC) layer in a CNN
framework. Analysi... | computer science |
27,693 | Tracking the Trackers: An Analysis of the State of the Art in Multiple
Object Tracking | cs.CV | Standardized benchmarks are crucial for the majority of computer vision
applications. Although leaderboards and ranking tables should not be
over-claimed, benchmarks often provide the most objective measure of
performance and are therefore important guides for research. We present a
benchmark for Multiple Object Tracki... | computer science |
27,694 | Deep Affordance-grounded Sensorimotor Object Recognition | cs.CV | It is well-established by cognitive neuroscience that human perception of
objects constitutes a complex process, where object appearance information is
combined with evidence about the so-called object "affordances", namely the
types of actions that humans typically perform when interacting with them. This
fact has rec... | computer science |
27,695 | Fine-graind Image Classification via Combining Vision and Language | cs.CV | Fine-grained image classification is a challenging task due to the large
intra-class variance and small inter-class variance, aiming at recognizing
hundreds of sub-categories belonging to the same basic-level category. Most
existing fine-grained image classification methods generally learn part
detection models to obta... | computer science |
27,696 | R-Clustering for Egocentric Video Segmentation | cs.CV | In this paper, we present a new method for egocentric video temporal
segmentation based on integrating a statistical mean change detector and
agglomerative clustering(AC) within an energy-minimization framework. Given the
tendency of most AC methods to oversegment video sequences when clustering
their frames, we combin... | computer science |
27,697 | Learning Human Motion Models for Long-term Predictions | cs.CV | We propose a new architecture for the learning of predictive spatio-temporal
motion models from data alone. Our approach, dubbed the Dropout Autoencoder
LSTM, is capable of synthesizing natural looking motion sequences over long
time horizons without catastrophic drift or motion degradation. The model
consists of two c... | computer science |
27,698 | ActionVLAD: Learning spatio-temporal aggregation for action
classification | cs.CV | In this work, we introduce a new video representation for action
classification that aggregates local convolutional features across the entire
spatio-temporal extent of the video. We do so by integrating state-of-the-art
two-stream networks with learnable spatio-temporal feature aggregation. The
resulting architecture ... | computer science |
27,699 | Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of
many temporal dimensions | cs.CV | Single particle cryo-electron microscopy (EM) is an increasingly popular
method for determining the 3-D structure of macromolecules from noisy 2-D
images of single macromolecules whose orientations and positions are random and
unknown. One of the great opportunities in cryo-EM is to recover the structure
of macromolecu... | computer science |
27,700 | Fast Learning and Prediction for Object Detection using Whitened CNN
Features | cs.CV | We combine features extracted from pre-trained convolutional neural networks
(CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of
both: the high detection performance of CNNs, automatic feature engineering,
fast model learning from few training samples and efficient sliding-window
detection. Th... | computer science |
27,701 | Surface Normals in the Wild | cs.CV | We study the problem of single-image depth estimation for images in the wild.
We collect human annotated surface normals and use them to train a neural
network that directly predicts pixel-wise depth. We propose two novel loss
functions for training with surface normal annotations. Experiments on NYU
Depth and our own ... | computer science |
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