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30,102 | Image-Image Domain Adaptation with Preserved Self-Similarity and
Domain-Dissimilarity for Person Re-identification | cs.CV | Person re-identification (re-ID) models trained on one domain often fail to
generalize well to another. In our attempt, we present a "learning via
translation" framework. In the baseline, we translate the labeled images from
source to target domain in an unsupervised manner. We then train re-ID models
with the translat... | computer science |
30,103 | DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial
Networks | cs.CV | We present an end-to-end learning approach for motion deblurring, which is
based on conditional GAN and content loss. It improves the state-of-the art in
terms of peak signal-to-noise ratio, structural similarity measure and by
visual appearance. The quality of the deblurring model is also evaluated in a
novel way on a... | computer science |
30,104 | Diverse and Accurate Image Description Using a Variational Auto-Encoder
with an Additive Gaussian Encoding Space | cs.CV | This paper explores image caption generation using conditional variational
auto-encoders (CVAEs). Standard CVAEs with a fixed Gaussian prior yield
descriptions with too little variability. Instead, we propose two models that
explicitly structure the latent space around $K$ components corresponding to
different types of... | computer science |
30,105 | Robust Non-line-of-sight Imaging with Single Photon Detectors | cs.CV | Imaging objects that are obscured by scattering and occlusion is an important
challenge for many applications. For example, navigation and mapping
capabilities of autonomous vehicles could be improved, vision in harsh weather
conditions or under water could be facilitated, or search and rescue scenarios
could become mo... | computer science |
30,106 | Spectral-Spatial Feature Extraction and Classification by ANN Supervised
with Center Loss in Hyperspectral Imagery | cs.CV | In this paper, we propose a spectral-spatial feature extraction and
classification framework based on artificial neuron network (ANN) in the
context of hyperspectral imagery. With limited labeled samples, only spectral
information is exploited for training and spatial context is integrated
posteriorly at the testing st... | computer science |
30,107 | Let Features Decide for Themselves: Feature Mask Network for Person
Re-identification | cs.CV | Person re-identification aims at establishing the identity of a pedestrian
from a gallery that contains images of multiple people obtained from a
multi-camera system. Many challenges such as occlusions, drastic lighting and
pose variations across the camera views, indiscriminate visual appearances,
cluttered background... | computer science |
30,108 | Adversarial Attacks Beyond the Image Space | cs.CV | Generating adversarial examples is an intriguing problem and an important way
of understanding the working mechanism of deep neural networks. Recently, it
has attracted a lot of attention in the computer vision community. Most
existing approaches generated perturbations in image space, i.e., each pixel
can be modified ... | computer science |
30,109 | Block-Cyclic Stochastic Coordinate Descent for Deep Neural Networks | cs.CV | We present a stochastic first-order optimization algorithm, named BCSC, that
adds a cyclic constraint to stochastic block-coordinate descent. It uses
different subsets of the data to update different subsets of the parameters,
thus limiting the detrimental effect of outliers in the training set. Empirical
tests in benc... | computer science |
30,110 | Stochastic metamorphosis with template uncertainties | cs.CV | In this paper, we investigate two stochastic perturbations of the
metamorphosis equations of image analysis, in the geometrical context of the
Euler-Poincar\'e theory. In the metamorphosis of images, the Lie group of
diffeomorphisms deforms a template image that is undergoing its own internal
dynamics as it deforms. Th... | computer science |
30,111 | Tracking in Aerial Hyperspectral Videos using Deep Kernelized
Correlation Filters | cs.CV | Hyperspectral imaging holds enormous potential to improve the
state-of-the-art in aerial vehicle tracking with low spatial and temporal
resolutions. Recently, adaptive multi-modal hyperspectral sensors, controlled
by Dynamic Data Driven Applications Systems (DDDAS) methodology, have attracted
growing interest due to th... | computer science |
30,112 | MegDet: A Large Mini-Batch Object Detector | cs.CV | The improvements in recent CNN-based object detection works, from R-CNN [11],
Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly
come from new network, new framework, or novel loss design. But mini-batch
size, a key factor in the training, has not been well studied. In this paper,
we propos... | computer science |
30,113 | Optical Character Recognition (OCR) for Telugu: Database, Algorithm and
Application | cs.CV | Telugu is a Dravidian language spoken by more than 80 million people
worldwide. The optical character recognition (OCR) of the Telugu script has
wide ranging applications including education, health-care, administration etc.
The beautiful Telugu script however is very different from Germanic scripts
like English and Ge... | computer science |
30,114 | Face Attention Network: An Effective Face Detector for the Occluded
Faces | cs.CV | The performance of face detection has been largely improved with the
development of convolutional neural network. However, the occlusion issue due
to mask and sunglasses, is still a challenging problem. The improvement on the
recall of these occluded cases usually brings the risk of high false positives.
In this paper,... | computer science |
30,115 | Light-Head R-CNN: In Defense of Two-Stage Object Detector | cs.CV | In this paper, we first investigate why typical two-stage methods are not as
fast as single-stage, fast detectors like YOLO and SSD. We find that Faster
R-CNN and R-FCN perform an intensive computation after or before RoI warping.
Faster R-CNN involves two fully connected layers for RoI recognition, while
R-FCN produce... | computer science |
30,116 | Zero-shot Learning via Shared-Reconstruction-Graph Pursuit | cs.CV | Zero-shot learning (ZSL) aims to recognize objects from novel unseen classes
without any training data. Recently, structure-transfer based methods are
proposed to implement ZSL by transferring structural knowledge from the
semantic embedding space to image feature space to classify testing images.
However, we observe t... | computer science |
30,117 | Detection of Tooth caries in Bitewing Radiographs using Deep Learning | cs.CV | We develop a Computer Aided Diagnosis (CAD) system, which enhances the
performance of dentists in detecting wide range of dental caries. The CAD
System achieves this by acting as a second opinion for the dentists with way
higher sensitivity on the task of detecting cavities than the dentists
themselves. We develop anno... | computer science |
30,118 | Cascaded Pyramid Network for Multi-Person Pose Estimation | cs.CV | The topic of multi-person pose estimation has been largely improved recently,
especially with the development of convolutional neural network. However, there
still exist a lot of challenging cases, such as occluded keypoints, invisible
keypoints and complex background, which cannot be well addressed. In this
paper, we ... | computer science |
30,119 | Memory Based Online Learning of Deep Representations from Video Streams | cs.CV | We present a novel online unsupervised method for face identity learning from
video streams. The method exploits deep face descriptors together with a memory
based learning mechanism that takes advantage of the temporal coherence of
visual data. Specifically, we introduce a discriminative feature matching
solution base... | computer science |
30,120 | Attentive Explanations: Justifying Decisions and Pointing to the
Evidence (Extended Abstract) | cs.CV | Deep models are the defacto standard in visual decision problems due to their
impressive performance on a wide array of visual tasks. On the other hand,
their opaqueness has led to a surge of interest in explainable systems. In this
work, we emphasize the importance of model explanation in various forms such as
visual ... | computer science |
30,121 | Pixel-wise object tracking | cs.CV | In this paper, we propose a novel pixel-wise visual object tracking framework
that can track any anonymous object in a noisy background. The framework
consists of two submodels, a global attention model and a local segmentation
model. The global model generates a region of interests (ROI) that the object
may lie in the... | computer science |
30,122 | V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and
Human Pose Estimation from a Single Depth Map | cs.CV | Most of the existing deep learning-based methods for 3D hand and human pose
estimation from a single depth map are based on a common framework that takes a
2D depth map and directly regresses the 3D coordinates of keypoints, such as
hand or human body joints, via 2D convolutional neural networks (CNNs). The
first weakn... | computer science |
30,123 | Disentangling Factors of Variation by Mixing Them | cs.CV | We propose an unsupervised approach to learn image representations that
consist of disentangled factors of variation. A factor of variation corresponds
to an image attribute that can be discerned consistently across a set of
images, such as the pose or color of objects. Our disentangled representation
consists of a con... | computer science |
30,124 | Robust Seed Mask Generation for Interactive Image Segmentation | cs.CV | In interactive medical image segmentation, anatomical structures are
extracted from reconstructed volumetric images. The first iterations of user
interaction traditionally consist of drawing pictorial hints as an initial
estimate of the object to extract. Only after this time consuming first phase,
the efficient select... | computer science |
30,125 | Joint Object Category and 3D Pose Estimation from 2D Images | cs.CV | 2D object detection is the task of finding (i) what objects are present in an
image and (ii) where they are located, while 3D pose estimation is the task of
finding the pose of these objects in 3D space. State-of-the-art methods for
solving these tasks follow a two-stage approach where a 3D pose estimation
system is ap... | computer science |
30,126 | Self-Similarity Based Time Warping | cs.CV | In this work, we explore the problem of aligning two time-ordered point
clouds which are spatially transformed and re-parameterized versions of each
other. This has a diverse array of applications such as cross modal time series
synchronization (e.g. MOCAP to video) and alignment of discretized curves in
images. Most o... | computer science |
30,127 | Dropping Activation Outputs with Localized First-layer Deep Network for
Enhancing User Privacy and Data Security | cs.CV | Deep learning methods can play a crucial role in anomaly detection,
prediction, and supporting decision making for applications like personal
health-care, pervasive body sensing, etc. However, current architecture of deep
networks suffers the privacy issue that users need to give out their data to
the model (typically ... | computer science |
30,128 | On Nearest Neighbors in Non Local Means Denoising | cs.CV | To denoise a reference patch, the Non-Local-Means denoising filter processes
a set of neighbor patches. Few Nearest Neighbors (NN) are used to limit the
computational burden of the algorithm. Here here we show analytically that the
NN approach introduces a bias in the denoised patch, and we propose a different
neighbor... | computer science |
30,129 | Knowledge Concentration: Learning 100K Object Classifiers in a Single
CNN | cs.CV | Fine-grained image labels are desirable for many computer vision
applications, such as visual search or mobile AI assistant. These applications
rely on image classification models that can produce hundreds of thousands
(e.g. 100K) of diversified fine-grained image labels on input images. However,
training a network at ... | computer science |
30,130 | $S^4$Net: Single Stage Salient-Instance Segmentation | cs.CV | In this paper, we consider an interesting vision problem---salient instance
segmentation. Other than producing approximate bounding boxes, our network also
outputs high-quality instance-level segments. Taking into account the
category-independent property of each target, we design a single stage salient
instance segmen... | computer science |
30,131 | A deep learning-based method for relative location prediction in CT scan
images | cs.CV | Relative location prediction in computed tomography (CT) scan images is a
challenging problem. In this paper, a regression model based on one-dimensional
convolutional neural networks is proposed to determine the relative location of
a CT scan image both robustly and precisely. A public dataset is employed to
validate ... | computer science |
30,132 | Multi-Image Semantic Matching by Mining Consistent Features | cs.CV | This work proposes a multi-image matching method to estimate semantic
correspondences across multiple images. In contrast to the previous methods
that optimize all pairwise correspondences, the proposed method identifies and
matches only a sparse set of reliable features in the image collection. In this
way, the propos... | computer science |
30,133 | Proximal Alternating Direction Network: A Globally Converged Deep
Unrolling Framework | cs.CV | Deep learning models have gained great success in many real-world
applications. However, most existing networks are typically designed in
heuristic manners, thus lack of rigorous mathematical principles and
derivations. Several recent studies build deep structures by unrolling a
particular optimization model that invol... | computer science |
30,134 | Fully Convolutional Neural Networks for Page Segmentation of Historical
Document Images | cs.CV | We propose a high-performance fully convolutional neural network (FCN) for
historical document segmentation that is designed to process a single page in
one step. The advantage of this model beside its speed is its ability to
directly learn from raw pixels instead of using preprocessing steps e. g.
feature computation ... | computer science |
30,135 | Residual Parameter Transfer for Deep Domain Adaptation | cs.CV | The goal of Deep Domain Adaptation is to make it possible to use Deep Nets
trained in one domain where there is enough annotated training data in another
where there is little or none. Most current approaches have focused on learning
feature representations that are invariant to the changes that occur when going
from o... | computer science |
30,136 | Total Variation-Based Dense Depth from Multi-Camera Array | cs.CV | Multi-Camera arrays are increasingly employed in both consumer and industrial
applications, and various passive techniques are documented to estimate depth
from such camera arrays. Current depth estimation methods provide useful
estimations of depth in an imaged scene but are often impractical due to
significant comput... | computer science |
30,137 | The Application of Preconditioned Alternating Direction Method of
Multipliers in Depth from Focal Stack | cs.CV | Post capture refocusing effect in smartphone cameras is achievable by using
focal stacks. However, the accuracy of this effect is totally dependent on the
combination of the depth layers in the stack. The accuracy of the extended
depth of field effect in this application can be improved significantly by
computing an ac... | computer science |
30,138 | Repulsion Loss: Detecting Pedestrians in a Crowd | cs.CV | Detecting individual pedestrians in a crowd remains a challenging problem
since the pedestrians often gather together and occlude each other in
real-world scenarios. In this paper, we first explore how a state-of-the-art
pedestrian detector is harmed by crowd occlusion via experimentation, providing
insights into the c... | computer science |
30,139 | Receptive Field Block Net for Accurate and Fast Object Detection | cs.CV | Current top-performing object detectors depend on deep CNN backbones, such as
ResNet-101 and Inception, benefiting from their powerful feature representation
but suffering from high computational cost. Conversely, some lightweight model
based detectors fulfil real time processing, while their accuracies are often
criti... | computer science |
30,140 | Efficient Multi-Person Pose Estimation with Provable Guarantees | cs.CV | Multi-person pose estimation (MPPE) in natural images is key to the
meaningful use of visual data in many fields including movement science,
security, and rehabilitation. In this paper we tackle MPPE with a bottom-up
approach, starting with candidate detections of body parts from a convolutional
neural network (CNN) an... | computer science |
30,141 | Universal Denoising Networks : A Novel CNN-based Network Architecture
for Image Denoising | cs.CV | We design a novel network architecture for learning discriminative image
models that are employed to efficiently tackle the problem of grayscale and
color image denoising. Based on the proposed architecture, we introduce two
different variants. The first network involves convolutional layers as a core
component, while ... | computer science |
30,142 | Efficient Implementation of a Recognition System Using the Cortex
Ventral Stream Model | cs.CV | In this paper, an efficient implementation for a recognition system based on
the original HMAX model of the visual cortex is proposed. Various optimizations
targeted to increase accuracy at the so-called layers S1, C1, and S2 of the
HMAX model are proposed. At layer S1, all unimportant information such as
illumination ... | computer science |
30,143 | A smartphone application to measure the quality of pest control spraying
machines via image analysis | cs.CV | The need for higher agricultural productivity has demanded the intensive use
of pesticides. However, their correct use depends on assessment methods that
can accurately predict how well the pesticides' spraying covered the intended
crop region. Some methods have been proposed in the literature, but their high
cost and ... | computer science |
30,144 | Discussion among Different Methods of Updating Model Filter in Object
Tracking | cs.CV | Discriminative correlation filters (DCF) have recently shown excellent
performance in visual object tracking area. In this paper, we summarize the
methods of updating model filter from discriminative correlation filter (DCF)
based tracking algorithms and analyzes similarities and differences among these
methods. We ded... | computer science |
30,145 | Robust Object Tracking Based on Self-adaptive Search Area | cs.CV | Discriminative correlation filter (DCF) based trackers have recently achieved
excellent performance with great computational efficiency. However, DCF based
trackers suffer boundary effects, which result in the unstable performance in
challenging situations exhibiting fast motion. In this paper, we propose a
novel metho... | computer science |
30,146 | UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional
Census Loss | cs.CV | In the era of end-to-end deep learning, many advances in computer vision are
driven by large amounts of labeled data. In the optical flow setting, however,
obtaining dense per-pixel ground truth for real scenes is difficult and thus
such data is rare. Therefore, recent end-to-end convolutional networks for
optical flow... | computer science |
30,147 | Functional Map of the World | cs.CV | We present a new dataset, Functional Map of the World (fMoW), which aims to
inspire the development of machine learning models capable of predicting the
functional purpose of buildings and land use from temporal sequences of
satellite images and a rich set of metadata features. The metadata provided
with each image ena... | computer science |
30,148 | SilNet : Single- and Multi-View Reconstruction by Learning from
Silhouettes | cs.CV | The objective of this paper is 3D shape understanding from single and
multiple images. To this end, we introduce a new deep-learning architecture and
loss function, SilNet, that can handle multiple views in an order-agnostic
manner. The architecture is fully convolutional, and for training we use a
proxy task of silhou... | computer science |
30,149 | Aperture Supervision for Monocular Depth Estimation | cs.CV | We present a novel method to train machine learning algorithms to estimate
scene depths from a single image, by using the information provided by a
camera's aperture as supervision. Prior works use a depth sensor's outputs or
images of the same scene from alternate viewpoints as supervision, while our
method instead us... | computer science |
30,150 | Non-local Neural Networks | cs.CV | Both convolutional and recurrent operations are building blocks that process
one local neighborhood at a time. In this paper, we present non-local
operations as a generic family of building blocks for capturing long-range
dependencies. Inspired by the classical non-local means method in computer
vision, our non-local o... | computer science |
30,151 | WAYLA - Generating Images from Eye Movements | cs.CV | We present a method for reconstructing images viewed by observers based only
on their eye movements. By exploring the relationships between gaze patterns
and image stimuli, the "What Are You Looking At?" (WAYLA) system learns to
synthesize photo-realistic images that are similar to the original pictures
being viewed. T... | computer science |
30,152 | Generating Analytic Insights on Human Behaviour using Image Processing | cs.CV | This paper proposes a method to track human figures in physical spaces and
then utilizes this data to generate several data points such as footfall
distribution, demographic analysis,heat maps as well as gender distribution.
The proposed framework aims to establish this while utilizing minimum
computational resources w... | computer science |
30,153 | Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons | cs.CV | Deep feed-forward convolutional neural networks (CNNs) have become ubiquitous
in virtually all machine learning and computer vision challenges; however,
advancements in CNNs have arguably reached an engineering saturation point
where incremental novelty results in minor performance gains. Although there is
evidence tha... | computer science |
30,154 | Dynamic High Resolution Deformable Articulated Tracking | cs.CV | The last several years have seen significant progress in using depth cameras
for tracking articulated objects such as human bodies, hands, and robotic
manipulators. Most approaches focus on tracking skeletal parameters of a fixed
shape model, which makes them insufficient for applications that require
accurate estimate... | computer science |
30,155 | Personalization of Saliency Estimation | cs.CV | Most existing saliency models use low-level features or task descriptions
when generating attention predictions. However, the link between observer
characteristics and gaze patterns is rarely investigated. We present a novel
saliency prediction technique which takes viewers' identities and personal
traits into consider... | computer science |
30,156 | Identifying Most Walkable Direction for Navigation in an Outdoor
Environment | cs.CV | We present an approach for identifying the most walkable direction for
navigation using a hand-held camera. Our approach extracts semantically rich
contextual information from the scene using a custom encoder-decoder
architecture for semantic segmentation and models the spatial and temporal
behavior of objects in the s... | computer science |
30,157 | Integrating both Visual and Audio Cues for Enhanced Video Caption | cs.CV | Video caption refers to generating a descriptive sentence for a specific
short video clip automatically, which has achieved remarkable success recently.
However, most of the existing methods focus more on visual information while
ignoring the synchronized audio cues. We propose three multimodal deep fusion
strategies t... | computer science |
30,158 | CMCGAN: A Uniform Framework for Cross-Modal Visual-Audio Mutual
Generation | cs.CV | Visual and audio modalities are two symbiotic modalities underlying videos,
which contain both common and complementary information. If they can be mined
and fused sufficiently, performances of related video tasks can be
significantly enhanced. However, due to the environmental interference or
sensor fault, sometimes, ... | computer science |
30,159 | Visual Question Answering as a Meta Learning Task | cs.CV | The predominant approach to Visual Question Answering (VQA) demands that the
model represents within its weights all of the information required to answer
any question about any image. Learning this information from any real training
set seems unlikely, and representing it in a reasonable number of weights
doubly so. W... | computer science |
30,160 | The Devil is in the Middle: Exploiting Mid-level Representations for
Cross-Domain Instance Matching | cs.CV | Many vision problems require matching images of object instances across
different domains. These include fine-grained sketch-based image retrieval
(FG-SBIR) and Person Re-identification (person ReID). Existing approaches
attempt to learn a joint embedding space where images from different domains
can be directly compar... | computer science |
30,161 | Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions | cs.CV | Neural networks rely on convolutions to aggregate spatial information.
However, spatial convolutions are expensive in terms of model size and
computation, both of which grow quadratically with respect to kernel size. In
this paper, we present a parameter-free, FLOP-free "shift" operation as an
alternative to spatial co... | computer science |
30,162 | A Face Fairness Framework for 3D Meshes | cs.CV | In this paper, we present a face fairness framework for 3D meshes that
preserves the regular shape of faces and is applicable to a variety of 3D mesh
restoration tasks. Specifically, we present a number of desirable properties
for any mesh restoration method and show that our framework satisfies them. We
then apply our... | computer science |
30,163 | Object Discovery By Generative Adversarial & Ranking Networks | cs.CV | The deep generative adversarial networks (GAN) recently have been shown to be
promising for different computer vision applications, like image editing,
synthesizing high resolution images, generating videos, etc. These networks and
the corresponding learning scheme can handle various visual space mappings. We
approach ... | computer science |
30,164 | Video Semantic Object Segmentation by Self-Adaptation of DCNN | cs.CV | This paper proposes a new framework for semantic segmentation of objects in
videos. We address the label inconsistency problem of deep convolutional neural
networks (DCNNs) by exploiting the fact that videos have multiple frames; in a
few frames the object is confidently-estimated (CE) and we use the information
in the... | computer science |
30,165 | AlignedReID: Surpassing Human-Level Performance in Person
Re-Identification | cs.CV | In this paper, we propose a novel method called AlignedReID that extracts a
global feature which is jointly learned with local features. Global feature
learning benefits greatly from local feature learning, which performs an
alignment/matching by calculating the shortest path between two sets of local
features, without... | computer science |
30,166 | An Analysis of Scale Invariance in Object Detection - SNIP | cs.CV | An analysis of different techniques for recognizing and detecting objects
under extreme scale variation is presented. Scale specific and scale invariant
design of detectors are compared by training them with different configurations
of input data. To examine if upsampling images is necessary for detecting small
objects... | computer science |
30,167 | Temporal 3D ConvNets: New Architecture and Transfer Learning for Video
Classification | cs.CV | The work in this paper is driven by the question how to exploit the temporal
cues available in videos for their accurate classification, and for human
action recognition in particular? Thus far, the vision community has focused on
spatio-temporal approaches with fixed temporal convolution kernel depths. We
introduce a ... | computer science |
30,168 | Integral Human Pose Regression | cs.CV | State-of-the-art human pose estimation methods are based on heat map
representation. In spite of the good performance, the representation has a few
issues in nature, such as not differentiable and quantization error. This work
shows that a simple integral operation relates and unifies the heat map
representation and jo... | computer science |
30,169 | Multi-Level Recurrent Residual Networks for Action Recognition | cs.CV | Most existing Convolutional Neural Networks(CNNs) used for action recognition
are either difficult to optimize or underuse crucial temporal information.
Inspired by the fact that the recurrent model consistently makes breakthroughs
in the task related to sequence, we propose a novel Multi-Level Recurrent
Residual Netwo... | computer science |
30,170 | 3D Point Cloud Classification and Segmentation using 3D Modified Fisher
Vector Representation for Convolutional Neural Networks | cs.CV | The point cloud is gaining prominence as a method for representing 3D shapes,
but its irregular format poses a challenge for deep learning methods. The
common solution of transforming the data into a 3D voxel grid introduces its
own challenges, mainly large memory size. In this paper we propose a novel 3D
point cloud r... | computer science |
30,171 | Neuron-level Selective Context Aggregation for Scene Segmentation | cs.CV | Contextual information provides important cues for disambiguating visually
similar pixels in scene segmentation. In this paper, we introduce a
neuron-level Selective Context Aggregation (SCA) module for scene segmentation,
comprised of a contextual dependency predictor and a context aggregation
operator. The dependency... | computer science |
30,172 | Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky
Noisy-or Network | cs.CV | Automatic diagnosing lung cancer from Computed Tomography (CT) scans involves
two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the
whole-lung/pulmonary malignancy. Currently, there are many studies about the
first step, but few about the second step. Since the existence of nodule does
not defin... | computer science |
30,173 | RGB-D-based Human Motion Recognition with Deep Learning: A Survey | cs.CV | Human motion recognition is one of the most important branches of
human-centered research activities. In recent years, motion recognition based
on RGB-D data has attracted much attention. Along with the development in
artificial intelligence, deep learning techniques have gained remarkable
success in computer vision. I... | computer science |
30,174 | Conditional Image-Text Embedding Networks | cs.CV | This paper presents an approach for grounding phrases in images which jointly
learns multiple text-conditioned embeddings in a single end-to-end model. In
order to differentiate text phrases into semantically distinct subspaces, we
propose a concept weight branch that automatically assigns phrases to
embeddings, wherea... | computer science |
30,175 | VITON: An Image-based Virtual Try-on Network | cs.CV | We present an image-based VIirtual Try-On Network (VITON) without using 3D
information in any form, which seamlessly transfers a desired clothing item
onto the corresponding region of a person using a coarse-to-fine strategy.
Conditioned upon a new clothing-agnostic yet descriptive person representation,
our framework ... | computer science |
30,176 | Frustum PointNets for 3D Object Detection from RGB-D Data | cs.CV | While object recognition on 2D images is getting more and more mature, 3D
understanding is eagerly in demand yet largely underexplored. In this paper, we
study the 3D object detection problem from RGB-D data captured by depth sensors
in both indoor and outdoor environments. Different from previous deep learning
methods... | computer science |
30,177 | Learning Deep Representations of Medical Images using Siamese CNNs with
Application to Content-Based Image Retrieval | cs.CV | Deep neural networks have been investigated in learning latent
representations of medical images, yet most of the studies limit their approach
in a single supervised convolutional neural network (CNN), which usually rely
heavily on a large scale annotated dataset for training. To learn image
representations with less s... | computer science |
30,178 | Temporal Relational Reasoning in Videos | cs.CV | Temporal relational reasoning, the ability to link meaningful transformations
of objects or entities over time, is a fundamental property of intelligent
species. In this paper, we introduce an effective and interpretable network
module, the Temporal Relation Network (TRN), designed to learn and reason about
temporal de... | computer science |
30,179 | Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action
Recognition | cs.CV | Despite the growing discriminative capabilities of modern deep learning
methods for recognition tasks, the inner workings of the state-of-art models
still remain mostly black-boxes. In this paper, we propose a systematic
interpretation of model parameters and hidden representations of Residual
Temporal Convolutional Ne... | computer science |
30,180 | W-Net: A Deep Model for Fully Unsupervised Image Segmentation | cs.CV | While significant attention has been recently focused on designing supervised
deep semantic segmentation algorithms for vision tasks, there are many domains
in which sufficient supervised pixel-level labels are difficult to obtain. In
this paper, we revisit the problem of purely unsupervised image segmentation
and prop... | computer science |
30,181 | Adversarial Feature Augmentation for Unsupervised Domain Adaptation | cs.CV | Recent works showed that Generative Adversarial Networks (GANs) can be
successfully applied in unsupervised domain adaptation, where, given a labeled
source dataset and an unlabeled target dataset, the goal is to train powerful
classifiers for the target samples. In particular, it was shown that a GAN
objective functio... | computer science |
30,182 | Person Transfer GAN to Bridge Domain Gap for Person Re-Identification | cs.CV | Although the performance of person Re-Identification (ReID) has been
significantly boosted, many challenging issues in real scenarios have not been
fully investigated, e.g., the complex scenes and lighting variations, viewpoint
and pose changes, and the large number of identities in a camera network. To
facilitate the ... | computer science |
30,183 | Geometric Cross-Modal Comparison of Heterogeneous Sensor Data | cs.CV | In this work, we address the problem of cross-modal comparison of aerial data
streams. A variety of simulated automobile trajectories are sensed using two
different modalities: full-motion video, and radio-frequency (RF) signals
received by detectors at various locations. The information represented by the
two modaliti... | computer science |
30,184 | 3D Anisotropic Hybrid Network: Transferring Convolutional Features from
2D Images to 3D Anisotropic Volumes | cs.CV | While deep convolutional neural networks (CNN) have been successfully applied
for 2D image analysis, it is still challenging to apply them to 3D anisotropic
volumes, especially when the within-slice resolution is much higher than the
between-slice resolution and when the amount of 3D volumes is relatively small.
On one... | computer science |
30,185 | Exploiting temporal information for 3D pose estimation | cs.CV | In this work, we address the problem of 3D human pose estimation from a
sequence of 2D human poses. Although the recent success of deep networks has
led many state-of-the-art methods for 3D pose estimation to train deep networks
end-to-end to predict from images directly, the top-performing approaches have
shown the ef... | computer science |
30,186 | SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance
Segmentation | cs.CV | We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive
deep learning framework for 3D object instance segmentation on point clouds.
SGPN uses a single network to predict point grouping proposals and a
corresponding semantic class for each proposal, from which we can directly
extract instance segm... | computer science |
30,187 | Image Inpainting using Multi-Scale Feature Image Translation | cs.CV | We study the task of image inpainting, which is to fill in the missing region
of an incomplete image with plausible contents. To this end, we propose a
learning-based approach to generate visually coherent completion given a
high-resolution image with missing components. In order to overcome the
difficulty to directly ... | computer science |
30,188 | Regularization of Deep Neural Networks with Spectral Dropout | cs.CV | The big breakthrough on the ImageNet challenge in 2012 was partially due to
the `dropout' technique used to avoid overfitting. Here, we introduce a new
approach called `Spectral Dropout' to improve the generalization ability of
deep neural networks. We cast the proposed approach in the form of regular
Convolutional Neu... | computer science |
30,189 | Unsupervised End-to-end Learning for Deformable Medical Image
Registration | cs.CV | We propose a registration algorithm for 2D CT/MRI medical images with a new
unsupervised end-to-end strategy using convolutional neural networks. The
contributions of our algorithm are threefold: (1) We transplant traditional
image registration algorithms to an end-to-end convolutional neural network
framework, while m... | computer science |
30,190 | Self-Reinforced Cascaded Regression for Face Alignment | cs.CV | Cascaded regression is prevailing in face alignment thanks to its accuracy
and robustness, but typically demands manually annotated examples having low
discrepancy between shape-indexed features and shape updates. In this paper, we
propose a self-reinforced strategy that iteratively expands the quantity and
improves th... | computer science |
30,191 | Self-view Grounding Given a Narrated 360° Video | cs.CV | Narrated 360{\deg} videos are typically provided in many touring scenarios to
mimic real-world experience. However, previous work has shown that smart
assistance (i.e., providing visual guidance) can significantly help users to
follow the Normal Field of View (NFoV) corresponding to the narrative. In this
project, we a... | computer science |
30,192 | Deep Expander Networks: Efficient Deep Networks from Graph Theory | cs.CV | Deep Neural Networks, while being unreasonably effective for several vision
tasks, have their usage limited by the computational and memory requirements,
both during training and inference stages. Analyzing and improving the
connectivity patterns between layers of a network has resulted in several
compact architectures... | computer science |
30,193 | Boosted Cascaded Convnets for Multilabel Classification of Thoracic
Diseases in Chest Radiographs | cs.CV | Chest X-ray is one of the most accessible medical imaging technique for
diagnosis of multiple diseases. With the availability of ChestX-ray14, which is
a massive dataset of chest X-ray images and provides annotations for 14
thoracic diseases; it is possible to train Deep Convolutional Neural Networks
(DCNN) to build Co... | computer science |
30,194 | Region-based Quality Estimation Network for Large-scale Person
Re-identification | cs.CV | One of the major restrictions on the performance of video-based person re-id
is partial noise caused by occlusion, blur and illumination. Since different
spatial regions of a single frame have various quality, and the quality of the
same region also varies across frames in a tracklet, a good way to address the
problem ... | computer science |
30,195 | 3D Based Landmark Tracker Using Superpixels Based Segmentation for
Neuroscience and Biomechanics Studies | cs.CV | Examining locomotion has improved our basic understanding of motor control
and aided in treating motor impairment. Mice and rats are premier models of
human disease and increasingly the model systems of choice for basic
neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics
of these running roden... | computer science |
30,196 | Real-Time Seamless Single Shot 6D Object Pose Prediction | cs.CV | We propose a single-shot approach for simultaneously detecting an object in
an RGB image and predicting its 6D pose without requiring multiple stages or
having to examine multiple hypotheses. Unlike a recently proposed single-shot
technique for this task (Kehl et al., ICCV'17) that only predicts an
approximate 6D pose ... | computer science |
30,197 | Feature Selective Networks for Object Detection | cs.CV | Objects for detection usually have distinct characteristics in different
sub-regions and different aspect ratios. However, in prevalent two-stage object
detection methods, Region-of-Interest (RoI) features are extracted by RoI
pooling with little emphasis on these translation-variant feature components.
We present feat... | computer science |
30,198 | Supervised Hashing with End-to-End Binary Deep Neural Network | cs.CV | Image hashing is a popular technique applied to large scale content-based
visual retrieval due to its compact and efficient binary codes. Our work
proposes a new end-to-end deep network architecture for supervised hashing
which directly learns binary codes from input images and maintains good
properties over binary cod... | computer science |
30,199 | CatGAN: Coupled Adversarial Transfer for Domain Generation | cs.CV | This paper introduces a Coupled adversarial transfer GAN (CatGAN), an
efficient solution to domain alignment. The basic principles of CatGAN focus on
the domain generation strategy for adaptation which is motivated by the
generative adversarial net (GAN) and the adversarial discriminative domain
adaptation (ADDA). CatG... | computer science |
30,200 | Deep learning analysis of the myocardium in coronary CT angiography for
identification of patients with functionally significant coronary artery
stenosis | cs.CV | In patients with coronary artery stenoses of intermediate severity, the
functional significance needs to be determined. Fractional flow reserve (FFR)
measurement, performed during invasive coronary angiography (ICA), is most
often used in clinical practice. To reduce the number of ICA procedures, we
present a method fo... | computer science |
30,201 | SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels | cs.CV | We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant
of deep neural networks for irregular structured and geometric input, e.g.,
graphs or meshes. Our main contribution is a novel convolution operator based
on B-splines, that makes the computation time independent from the kernel size
due to th... | computer science |
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