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30,202 | For Your Eyes Only: Learning to Summarize First-Person Videos | cs.CV | With the increasing amount of video data, it is desirable to highlight or
summarize the videos of interest for viewing, search, or storage purposes.
However, existing summarization approaches are typically trained from
third-person videos, which cannot generalize to highlight the first-person
ones. By advancing deep le... | computer science |
30,203 | Sketch-to-Image Generation Using Deep Contextual Completion | cs.CV | When the input to pix2pix translation is a badly drawn sketch, the output
follows the input edges due to the strict alignment imposed by the translation
process. In this paper we propose sketch-to-image generation, where the output
edges do not necessarily follow the input edges. We address the image
generation problem... | computer science |
30,204 | Unsupervised Domain Adaptation with Similarity Learning | cs.CV | The objective of unsupervised domain adaptation is to leverage features from
a labeled source domain and learn a classifier for an unlabeled target domain,
with a similar but different data distribution. Most deep learning approaches
to domain adaptation consist of two steps: (i) learn features that preserve a
low risk... | computer science |
30,205 | Dense 3D Regression for Hand Pose Estimation | cs.CV | We present a simple and effective method for 3D hand pose estimation from a
single depth frame. As opposed to previous state-of-the-art methods based on
holistic 3D regression, our method works on dense pixel-wise estimation. This
is achieved by careful design choices in pose parameterization, which leverages
both 2D a... | computer science |
30,206 | Visual Feature Attribution using Wasserstein GANs | cs.CV | Attributing the pixels of an input image to a certain category is an
important and well-studied problem in computer vision, with applications
ranging from weakly supervised localisation to understanding hidden effects in
the data. In recent years, approaches based on interpreting a previously
trained neural network cla... | computer science |
30,207 | MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation | cs.CV | Learning-based methods are believed to work well for unconstrained gaze
estimation, i.e. gaze estimation from a monocular RGB camera without
assumptions regarding user, environment, or camera. However, current gaze
datasets were collected under laboratory conditions and methods were not
evaluated across multiple datase... | computer science |
30,208 | StarGAN: Unified Generative Adversarial Networks for Multi-Domain
Image-to-Image Translation | cs.CV | Recent studies have shown remarkable success in image-to-image translation
for two domains. However, existing approaches have limited scalability and
robustness in handling more than two domains, since different models should be
built independently for every pair of image domains. To address this
limitation, we propose... | computer science |
30,209 | Long-Term On-Board Prediction of People in Traffic Scenes under
Uncertainty | cs.CV | Progress towards advanced systems for assisted and autonomous driving is
leveraging recent advances in recognition and segmentation methods. Yet, we are
still facing challenges in bringing reliable driving to inner cities, as those
are composed of highly dynamic scenes observed from a moving platform at
considerable sp... | computer science |
30,210 | Distance to Center of Mass Encoding for Instance Segmentation | cs.CV | The instance segmentation can be considered an extension of the object
detection problem where bounding boxes are replaced by object contours.
Strictly speaking the problem requires to identify each pixel instance and
class independently of the artifice used for this mean. The advantage of
instance segmentation over th... | computer science |
30,211 | Efficient and Invariant Convolutional Neural Networks for Dense
Prediction | cs.CV | Convolutional neural networks have shown great success on feature extraction
from raw input data such as images. Although convolutional neural networks are
invariant to translations on the inputs, they are not invariant to other
transformations, including rotation and flip. Recent attempts have been made to
incorporate... | computer science |
30,212 | Video Enhancement with Task-Oriented Flow | cs.CV | Many video processing algorithms rely on optical flow to register different
frames within a sequence. However, a precise estimation of optical flow is
often neither tractable nor optimal for a particular task. In this paper, we
propose task-oriented flow (TOFlow), a flow representation tailored for
specific video proce... | computer science |
30,213 | Deep Extreme Cut: From Extreme Points to Object Segmentation | cs.CV | This paper explores the use of extreme points in an object (left-most,
right-most, top, bottom pixels) as input to obtain precise object segmentation
for images and videos. We do so by adding an extra channel to the image in the
input of a convolutional neural network (CNN), which contains a Gaussian
centered in each o... | computer science |
30,214 | Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic
Imagery | cs.CV | In human learning, it is common to use multiple sources of information
jointly. However, most existing feature learning approaches learn from only a
single task. In this paper, we propose a novel multi-task deep network to learn
generalizable high-level visual representations. Since multi-task learning
requires annotat... | computer science |
30,215 | Appearance-and-Relation Networks for Video Classification | cs.CV | Spatiotemporal feature learning in videos is a fundamental and difficult
problem in computer vision. This paper presents a new architecture, termed as
Appearance-and-Relation Network (ARTNet), to learn video representation in an
end-to-end manner. ARTNets are constructed by stacking multiple generic
building blocks, ca... | computer science |
30,216 | Convolutional Image Captioning | cs.CV | Image captioning is an important but challenging task, applicable to virtual
assistants, editing tools, image indexing, and support of the disabled. Its
challenges are due to the variability and ambiguity of possible image
descriptions. In recent years significant progress has been made in image
captioning, using Recur... | computer science |
30,217 | Cost-Effective Active Learning for Melanoma Segmentation | cs.CV | We propose a novel Active Learning framework capable to train effectively a
convolutional neural network for semantic segmentation of medical imaging, with
a limited amount of training labeled data. Our contribution is a practical
Cost-Effective Active Learning approach using dropout at test time as Monte
Carlo samplin... | computer science |
30,218 | Multiple Instance Curriculum Learning for Weakly Supervised Object
Detection | cs.CV | When supervising an object detector with weakly labeled data, most existing
approaches are prone to trapping in the discriminative object parts, e.g.,
finding the face of a cat instead of the full body, due to lacking the
supervision on the extent of full objects. To address this challenge, we
incorporate object segmen... | computer science |
30,219 | CondenseNet: An Efficient DenseNet using Learned Group Convolutions | cs.CV | Deep neural networks are increasingly used on mobile devices, where
computational resources are limited. In this paper we develop CondenseNet, a
novel network architecture with unprecedented efficiency. It combines dense
connectivity between layers with a mechanism to remove unused connections. The
dense connectivity f... | computer science |
30,220 | On the Relations of Correlation Filter Based Trackers and Struck | cs.CV | In recent years, two types of trackers, namely correlation filter based
tracker (CF tracker) and structured output tracker (Struck), have exhibited the
state-of-the-art performance. However, there seems to be lack of analytic work
on their relations in the computer vision community. In this paper, we
investigate two st... | computer science |
30,221 | Structure-Aware and Temporally Coherent 3D Human Pose Estimation | cs.CV | Deep learning methods for 3D human pose estimation from RGB images require a
huge amount of domain-specific labeled data for good in-the-wild performance.
However, obtaining annotated 3D pose data requires a complex motion capture
setup which is generally limited to controlled settings. We propose a
semi-supervised lea... | computer science |
30,222 | Predictive Learning: Using Future Representation Learning Variantial
Autoencoder for Human Action Prediction | cs.CV | The unsupervised Pretraining method has been widely used in aiding human
action recognition. However, existing methods focus on reconstructing the
already present frames rather than generating frames which happen in future.In
this paper, We propose an improved Variantial Autoencoder model to extract the
features with a... | computer science |
30,223 | Gradually Updated Neural Networks for Large-Scale Image Recognition | cs.CV | Depth is one of the keys that make neural networks succeed in the task of
large-scale image recognition. The state-of-the-art network architectures
usually increase the depths by cascading convolutional layers or building
blocks. In this paper, we present an alternative method to increase the depth.
Our method is by in... | computer science |
30,224 | Unsupervised 3D Reconstruction from a Single Image via Adversarial
Learning | cs.CV | Recent advancements in deep learning opened new opportunities for learning a
high-quality 3D model from a single 2D image given sufficient training on
large-scale data sets. However, the significant imbalance between available
amount of images and 3D models, and the limited availability of labeled 2D
image data (i.e. m... | computer science |
30,225 | DeepRadiologyNet: Radiologist Level Pathology Detection in CT Head
Images | cs.CV | We describe a system to automatically filter clinically significant findings
from computerized tomography (CT) head scans, operating at performance levels
exceeding that of practicing radiologists. Our system, named DeepRadiologyNet,
builds on top of deep convolutional neural networks (CNNs) trained using
approximately... | computer science |
30,226 | In2I : Unsupervised Multi-Image-to-Image Translation Using Generative
Adversarial Networks | cs.CV | In unsupervised image-to-image translation, the goal is to learn the mapping
between an input image and an output image using a set of unpaired training
images. In this paper, we propose an extension of the unsupervised
image-to-image translation problem to multiple input setting. Given a set of
paired images from mult... | computer science |
30,227 | Semantically Consistent Image Completion with Fine-grained Details | cs.CV | Image completion has achieved significant progress due to advances in
generative adversarial networks (GANs). Albeit natural-looking, the synthesized
contents still lack details, especially for scenes with complex structures or
images with large holes. This is because there exists a gap between low-level
reconstruction... | computer science |
30,228 | HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal
Retrieval | cs.CV | As the rapid growth of multi-modal data, hashing methods for cross-modal
retrieval have received considerable attention. Deep-networks-based cross-modal
hashing methods are appealing as they can integrate feature learning and hash
coding into end-to-end trainable frameworks. However, it is still challenging
to find con... | computer science |
30,229 | Beyond Part Models: Person Retrieval with Refined Part Pooling (and a
Strong Convolutional Baseline) | cs.CV | Employing part-level features for pedestrian image description offers
fine-grained information and has been verified as beneficial for person
retrieval in very recent literature. A prerequisite of part discovery is that
each part should be well located. Instead of using external cues, e.g., pose
estimation, to directly... | computer science |
30,230 | Automatic Color Image Segmentation Using a Square Elemental Region-Based
Seeded Region Growing and Merging Method | cs.CV | This paper presents an efficient automatic color image segmentation method
using a seeded region growing and merging method based on square elemental
regions. Our segmentation method consists of the three steps: generating seed
regions, merging the regions, and applying a pixel-wise boundary determination
algorithm to ... | computer science |
30,231 | Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette
Sequence Images | cs.CV | In this paper, we develop a novel convolutional neural network based approach
to extract and aggregate useful information from gait silhouette sequence
images instead of simply representing the gait process by averaging silhouette
images. The network takes a pair of arbitrary length sequence images as inputs
and extrac... | computer science |
30,232 | Personalized and Occupational-aware Age Progression by Generative
Adversarial Networks | cs.CV | Face age progression, which aims to predict the future looks, is important
for various applications and has been received considerable attentions.
Existing methods and datasets are limited in exploring the effects of
occupations which may influence the personal appearances. In this paper, we
firstly introduce an occupa... | computer science |
30,233 | Learning a Rotation Invariant Detector with Rotatable Bounding Box | cs.CV | Detection of arbitrarily rotated objects is a challenging task due to the
difficulties of locating the multi-angle objects and separating them
effectively from the background. The existing methods are not robust to angle
varies of the objects because of the use of traditional bounding box, which is
a rotation variant s... | computer science |
30,234 | Coplanar Repeats by Energy Minimization | cs.CV | This paper proposes an automated method to detect, group and rectify
arbitrarily-arranged coplanar repeated elements via energy minimization. The
proposed energy functional combines several features that model how planes with
coplanar repeats are projected into images and captures global interactions
between different ... | computer science |
30,235 | STAR-RT: Visual attention for real-time video game playing | cs.CV | In this paper we present STAR-RT - the first working prototype of Selective
Tuning Attention Reference (STAR) model and Cognitive Programs (CPs). The
Selective Tuning (ST) model received substantial support through psychological
and neurophysiological experiments. The STAR framework expands ST and applies
it to practic... | computer science |
30,236 | SkipNet: Learning Dynamic Routing in Convolutional Networks | cs.CV | Increasing depth and complexity in convolutional neural networks has enabled
significant progress in visual perception tasks. However, incremental
improvements in accuracy are often accompanied by exponentially deeper models
that push the computational limits of modern hardware. These incremental
improvements in accura... | computer science |
30,237 | Depth Map Completion by Jointly Exploiting Blurry Color Images and
Sparse Depth Maps | cs.CV | We aim at predicting a complete and high-resolution depth map from
incomplete, sparse and noisy depth measurements. Existing methods handle this
problem either by exploiting various regularizations on the depth maps directly
or resorting to learning based methods. When the corresponding color images are
available, the ... | computer science |
30,238 | Query-Adaptive R-CNN for Open-Vocabulary Object Detection and Retrieval | cs.CV | We address the problem of open-vocabulary object retrieval and localization,
which is to retrieve and localize objects from a very large-scale image
database immediately by a textual query (e.g., a word or phrase). We first
propose Query-Adaptive R-CNN, a simple yet strong framework for open-vocabulary
object detection... | computer science |
30,239 | Structure propagation for zero-shot learning | cs.CV | The key of zero-shot learning (ZSL) is how to find the information transfer
model for bridging the gap between images and semantic information (texts or
attributes). Existing ZSL methods usually construct the compatibility function
between images and class labels with the consideration of the relevance on the
semantic ... | computer science |
30,240 | DeepDeblur: Fast one-step blurry face images restoration | cs.CV | We propose a very fast and effective one-step restoring method for blurry
face images. In the last decades, many blind deblurring algorithms have been
proposed to restore latent sharp images. However, these algorithms run slowly
because of involving two steps: kernel estimation and following non-blind
deconvolution or ... | computer science |
30,241 | Dynamic Graph Generation Network: Generating Relational Knowledge from
Diagrams | cs.CV | In this work, we introduce a new algorithm for analyzing a diagram, which
contains visual and textual information in an abstract and integrated way.
Whereas diagrams contain richer information compared with individual
image-based or language-based data, proper solutions for automatically
understanding them have not bee... | computer science |
30,242 | Hierarchical Siamese Network for Thermal Infrared Object Tracking | cs.CV | Most thermal infrared (TIR) tracking methods are discriminative, which treat
the tracking problem as a classification task. However, the objective of the
classifier (label prediction) is not coupled to the objective of the tracker
(location estimation). The classification task focuses on the between-class
difference of... | computer science |
30,243 | Accessible Melanoma Detection using Smartphones and Mobile Image
Analysis | cs.CV | We investigate the design of an entire mobile imaging system for early
detection of melanoma. Different from previous work, we focus on
smartphone-captured visible light images. Our design addresses two major
challenges. First, images acquired using a smartphone under loosely-controlled
environmental conditions may be ... | computer science |
30,244 | Discriminative Region Proposal Adversarial Networks for High-Quality
Image-to-Image Translation | cs.CV | Image-to-image translation has been made much progress with embracing
Generative Adversarial Networks (GANs). However, it's still very challenging
for translation tasks that require high-quality, especially at high-resolution
and photo-reality. In this paper, we present Discriminative Region Proposal
Adversarial Networ... | computer science |
30,245 | Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? | cs.CV | The purpose of this study is to determine whether current video datasets have
sufficient data for training very deep convolutional neural networks (CNNs)
with spatio-temporal three-dimensional (3D) kernels. Recently, the performance
levels of 3D CNNs in the field of action recognition have improved
significantly. Howev... | computer science |
30,246 | Joint Cuts and Matching of Partitions in One Graph | cs.CV | As two fundamental problems, graph cuts and graph matching have been
investigated over decades, resulting in vast literature in these two topics
respectively. However the way of jointly applying and solving graph cuts and
matching receives few attention. In this paper, we first formalize the problem
of simultaneously c... | computer science |
30,247 | FCLT - A Fully-Correlational Long-Term Tracker | cs.CV | We propose FCLT - a fully-correlational long-term tracker. The two main
components of FCLT are a short-term tracker which localizes the target in each
frame and a detector which re-detects the target when it is lost. Both the
short-term tracker and the detector are based on correlation filters. The
detector exploits pr... | computer science |
30,248 | Hierarchical Video Generation from Orthogonal Information: Optical Flow
and Texture | cs.CV | Learning to represent and generate videos from unlabeled data is a very
challenging problem. To generate realistic videos, it is important not only to
ensure that the appearance of each frame is real, but also to ensure the
plausibility of a video motion and consistency of a video appearance in the
time direction. The ... | computer science |
30,249 | Transfer Learning in CNNs Using Filter-Trees | cs.CV | Convolutional Neural Networks (CNNs) are very effective for many pattern
recognition tasks. However, training deep CNNs needs extensive computation and
large training data. In this paper we propose Bank of Filter-Trees (BFT) as a
trans- fer learning mechanism for improving efficiency of learning CNNs. A
filter-tree cor... | computer science |
30,250 | Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold
Training and Voting | cs.CV | In this paper we introduce a method that significantly reduces the character
error rates for OCR text obtained from OCRopus models trained on early printed
books. The method uses a combination of cross fold training and confidence
based voting. After allocating the available ground truth in different subsets
several tr... | computer science |
30,251 | Exploiting the potential of unlabeled endoscopic video data with
self-supervised learning | cs.CV | Surgical data science is a new research field that aims to observe all
aspects of the patient treatment process in order to provide the right
assistance at the right time. Due to the breakthrough successes of deep
learning-based solutions for automatic image annotation, the availability of
reference annotations for alg... | computer science |
30,252 | On the Robustness of Semantic Segmentation Models to Adversarial Attacks | cs.CV | Deep Neural Networks (DNNs) have been demonstrated to perform exceptionally
well on most recognition tasks such as image classification and segmentation.
However, they have also been shown to be vulnerable to adversarial examples.
This phenomenon has recently attracted a lot of attention but it has not been
extensively... | computer science |
30,253 | Training Convolutional Neural Networks with Limited Training Data for
Ear Recognition in the Wild | cs.CV | Identity recognition from ear images is an active field of research within
the biometric community. The ability to capture ear images from a distance and
in a covert manner makes ear recognition technology an appealing choice for
surveillance and security applications as well as related application domains.
In contrast... | computer science |
30,254 | SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again | cs.CV | We present a novel method for detecting 3D model instances and estimating
their 6D poses from RGB data in a single shot. To this end, we extend the
popular SSD paradigm to cover the full 6D pose space and train on synthetic
model data only. Our approach competes or surpasses current state-of-the-art
methods that levera... | computer science |
30,255 | CAR-Net: Clairvoyant Attentive Recurrent Network | cs.CV | We present an interpretable framework for path prediction that learns
scene-specific causations behind agents' behaviors. We exploit two sources of
information: the past motion trajectory of the agent of interest and a wide
top-down view of the scene. We propose a Clairvoyant Attentive Recurrent
Network (CAR-Net) that ... | computer science |
30,256 | Particle Filter Re-detection for Visual Tracking via Correlation Filters | cs.CV | Most of the correlation filter based tracking algorithms can achieve good
performance and maintain fast computational speed. However, in some complicated
tracking scenes, there is a fatal defect that causes the object to be located
inaccurately. In order to address this problem, we propose a particle filter
redetection... | computer science |
30,257 | Attentive Generative Adversarial Network for Raindrop Removal from a
Single Image | cs.CV | Raindrops adhered to a glass window or camera lens can severely hamper the
visibility of a background scene, and degrade an image considerably. In this
paper, we address the problem by visually removing raindrops, and thus
transforming a raindrop degraded image into a clean image. The problem is
intractable, since firs... | computer science |
30,258 | Learning Channel Inter-dependencies at Multiple Scales on Dense Networks
for Face Recognition | cs.CV | We propose a new deep network structure for unconstrained face recognition.
The proposed network integrates several key components together in order to
characterize complex data distributions, such as in unconstrained face images.
Inspired by recent progress in deep networks, we consider some important
concepts, includ... | computer science |
30,259 | 3D-A-Nets: 3D Deep Dense Descriptor for Volumetric Shapes with
Adversarial Networks | cs.CV | Recently researchers have been shifting their focus towards learned 3D shape
descriptors from hand-craft ones to better address challenging issues of the
deformation and structural variation inherently present in 3D objects. 3D
geometric data are often transformed to 3D Voxel grids with regular format in
order to be be... | computer science |
30,260 | Revisiting hand-crafted feature for action recognition: a set of
improved dense trajectories | cs.CV | We propose a feature for action recognition called Trajectory-Set (TS), on
top of the improved Dense Trajectory (iDT). The TS feature encodes only
trajectories around densely sampled interest points, without any appearance
features. Experimental results on the UCF50, UCF101, and HMDB51 action datasets
demonstrate that ... | computer science |
30,261 | Recurrent Segmentation for Variable Computational Budgets | cs.CV | State-of-the-art systems for semantic image segmentation use feed-forward
pipelines with fixed computational costs. Building an image segmentation system
that works across a range of computational budgets is challenging and
time-intensive as new architectures must be designed and trained for every
computational setting... | computer science |
30,262 | Restricting Greed in Training of Generative Adversarial Network | cs.CV | Generative adversarial network (GAN) has gotten wide re-search interest in
the field of deep learning. Variations of GAN have achieved competitive results
on specific tasks. However, the stability of training and diversity of
generated instances are still worth studying further. Training of GAN can be
thought of as a g... | computer science |
30,263 | Guaranteed Outlier Removal for Point Cloud Registration with
Correspondences | cs.CV | An established approach for 3D point cloud registration is to estimate the
registration function from 3D keypoint correspondences. Typically, a robust
technique is required to conduct the estimation, since there are false
correspondences or outliers. Current 3D keypoint techniques are much less
accurate than their 2D c... | computer science |
30,264 | Multi-stream 3D FCN with Multi-scale Deep Supervision for Multi-modality
Isointense Infant Brain MR Image Segmentation | cs.CV | We present a method to address the challenging problem of segmentation of
multi-modality isointense infant brain MR images into white matter (WM), gray
matter (GM), and cerebrospinal fluid (CSF). Our method is based on
context-guided, multi-stream fully convolutional networks (FCN), which after
training, can directly m... | computer science |
30,265 | Tracking for Half an Hour | cs.CV | Long-term tracking requires extreme stability to the multitude of model
updates and robustness to the disappearance and loss of the target as such will
inevitably happen. For motivation, we have taken 10 randomly selected
OTB-sequences, doubled each by attaching a reversed version and repeated each
double sequence 20 t... | computer science |
30,266 | Learning Less is More - 6D Camera Localization via 3D Surface Regression | cs.CV | Popular research areas like autonomous driving and augmented reality have
renewed the interest in image-based camera localization. In this work, we
address the task of predicting the 6D camera pose from a single RGB image in a
given 3D environment. With the advent of neural networks, previous works have
either learned ... | computer science |
30,267 | Differential Generative Adversarial Networks: Synthesizing Non-linear
Facial Variations with Limited Number of Training Data | cs.CV | In face-related applications with a public available dataset, synthesizing
non-linear facial variations (e.g., facial expression, head-pose, illumination,
etc.) through a generative model is helpful in addressing the lack of training
data. In reality, however, there is insufficient data to even train the
generative mod... | computer science |
30,268 | 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks | cs.CV | Convolutional networks are the de-facto standard for analyzing
spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this
data is naturally dense (e.g., photos), many other data sources are inherently
sparse. Examples include 3D point clouds that were obtained using a LiDAR
scanner or RGB-D camera.... | computer science |
30,269 | Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain
Adaptation | cs.CV | In this work, we face the problem of unsupervised domain adaptation with a
novel deep learning approach which leverages on our finding that entropy
minimization is induced by the optimal alignment of second order statistics
between source and target domains. We formally demonstrate this hypothesis and,
aiming at achiev... | computer science |
30,270 | Scalable and Compact 3D Action Recognition with Approximated RBF Kernel
Machines | cs.CV | Despite the recent deep learning (DL) revolution, kernel machines still
remain powerful methods for action recognition. DL has brought the use of large
datasets and this is typically a problem for kernel approaches, which are not
scaling up efficiently due to kernel Gram matrices. Nevertheless, kernel
methods are still... | computer science |
30,271 | Camera Style Adaptation for Person Re-identification | cs.CV | Being a cross-camera retrieval task, person re-identification suffers from
image style variations caused by different cameras. The art implicitly
addresses this problem by learning a camera-invariant descriptor subspace. In
this paper, we explicitly consider this challenge by introducing camera style
(CamStyle) adaptat... | computer science |
30,272 | Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks | cs.CV | Convolutional Neural Networks (CNN) have been regarded as a powerful class of
models for image recognition problems. Nevertheless, it is not trivial when
utilizing a CNN for learning spatio-temporal video representation. A few
studies have shown that performing 3D convolutions is a rewarding approach to
capture both sp... | computer science |
30,273 | Super-Resolution for Overhead Imagery Using DenseNets and Adversarial
Learning | cs.CV | Recent advances in Generative Adversarial Learning allow for new modalities
of image super-resolution by learning low to high resolution mappings. In this
paper we present our work using Generative Adversarial Networks (GANs) with
applications to overhead and satellite imagery. We have experimented with
several state-o... | computer science |
30,274 | Learning Face Age Progression: A Pyramid Architecture of GANs | cs.CV | The two underlying requirements of face age progression, i.e. aging accuracy
and identity permanence, are not well handled in the literature. In this paper,
we present a novel generative adversarial network based approach. It separately
models the constraints for the intrinsic subject-specific characteristics and
the a... | computer science |
30,275 | Learning to Segment Every Thing | cs.CV | Existing methods for object instance segmentation require all training
instances to be labeled with segmentation masks. This requirement makes it
expensive to annotate new categories and has restricted instance segmentation
models to ~100 well-annotated classes. The goal of this paper is to propose a
new partially supe... | computer science |
30,276 | A Pose-Sensitive Embedding for Person Re-Identification with Expanded
Cross Neighborhood Re-Ranking | cs.CV | Person re identification is a challenging retrieval task that requires
matching a person's acquired image across non overlapping camera views. In this
paper we propose an effective approach that incorporates both the fine and
coarse pose information of the person to learn a discriminative embedding. In
contrast to the ... | computer science |
30,277 | Exposing Computer Generated Images by Using Deep Convolutional Neural
Networks | cs.CV | The recent computer graphics developments have upraised the quality of the
generated digital content, astonishing the most skeptical viewer. Games and
movies have taken advantage of this fact but, at the same time, these advances
have brought serious negative impacts like the ones yielded by fakeimages
produced with ma... | computer science |
30,278 | DOTA: A Large-scale Dataset for Object Detection in Aerial Images | cs.CV | Object detection is an important and challenging problem in computer vision.
Although the past decade has witnessed major advances in object detection in
natural scenes, such successes have been slow to aerial imagery, not only
because of the huge variation in the scale, orientation and shape of the object
instances on... | computer science |
30,279 | An Adversarial Neuro-Tensorial Approach For Learning Disentangled
Representations | cs.CV | Several factors contribute to the appearance of an object in a visual scene,
including pose, illumination, and deformation, among others. Each factor
accounts for a source of variability in the data, while the multiplicative
interactions of these factors emulate the entangled variability, giving rise to
the rich struct... | computer science |
30,280 | Entropy-difference based stereo error detection | cs.CV | Stereo depth estimation is error-prone; hence, effective error detection
methods are desirable. Most such existing methods depend on characteristics of
the stereo matching cost curve, making them unduly dependent on functional
details of the matching algorithm. As a remedy, we propose a novel error
detection approach b... | computer science |
30,281 | DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer
Classification | cs.CV | Globally, in 2016, one out of eleven adults suffered from Diabetes Mellitus.
Diabetic Foot Ulcers (DFU) are a major complication of this disease, which if
not managed properly can lead to amputation. Current clinical approaches to DFU
treatment rely on patient and clinician vigilance, which has significant
limitations ... | computer science |
30,282 | Multi-class Semantic Segmentation of Skin Lesions via Fully
Convolutional Networks | cs.CV | Early detection of skin cancer, particularly melanoma, is crucial to enable
advanced treatment. Due to the rapid growth of skin cancers, there is a growing
need of computerized analysis for skin lesions. These processes including
detection, classification, and segmentation. There are three main types of skin
lesions in... | computer science |
30,283 | AttnGAN: Fine-Grained Text to Image Generation with Attentional
Generative Adversarial Networks | cs.CV | In this paper, we propose an Attentional Generative Adversarial Network
(AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained
text-to-image generation. With a novel attentional generative network, the
AttnGAN can synthesize fine-grained details at different subregions of the
image by paying at... | computer science |
30,284 | Highlighting objects of interest in an image by integrating saliency and
depth | cs.CV | Stereo images have been captured primarily for 3D reconstruction in the past.
However, the depth information acquired from stereo can also be used along with
saliency to highlight certain objects in a scene. This approach can be used to
make still images more interesting to look at, and highlight objects of
interest in... | computer science |
30,285 | Learning from Longitudinal Face Demonstration - Where Tractable Deep
Modeling Meets Inverse Reinforcement Learning | cs.CV | This paper presents a novel Subject-dependent Deep Aging Path (SDAP), which
inherits the merits of both Generative Probabilistic Modeling and Inverse
Reinforcement Learning to model the facial structures and the longitudinal face
aging process of a given subject. The proposed SDAP is optimized using
tractable log-likel... | computer science |
30,286 | Deep Lesion Graphs in the Wild: Relationship Learning and Organization
of Significant Radiology Image Findings in a Diverse Large-scale Lesion
Database | cs.CV | Radiologists in their daily work routinely find and annotate significant
abnormalities on a large number of radiology images. Such abnormalities, or
lesions, have collected over years and stored in hospitals' picture archiving
and communication systems. However, they are basically unsorted and lack
semantic annotations... | computer science |
30,287 | Deep learning analysis of breast MRIs for prediction of occult invasive
disease in ductal carcinoma in situ | cs.CV | Purpose: To determine whether deep learning-based algorithms applied to
breast MR images can aid in the prediction of occult invasive disease following
the di- agnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.
Material and Methods: In this institutional review board-approved study, we
analyzed dynamic c... | computer science |
30,288 | Deep-Person: Learning Discriminative Deep Features for Person
Re-Identification | cs.CV | Recently, many methods of person re-identification (Re-ID) rely on part-based
feature representation to learn a discriminative pedestrian descriptor.
However, the spatial context between these parts is ignored for the independent
extractor to each separate part. In this paper, we propose to apply Long
Short-Term Memory... | computer science |
30,289 | An Adaptive Fuzzy-Based System to Simulate, Quantify and Compensate
Color Blindness | cs.CV | About 8% of the male population of the world are affected by a determined
type of color vision disturbance, which varies from the partial to complete
reduction of the ability to distinguish certain colors. A considerable amount
of color blind people are able to live all life long without knowing they have
color vision ... | computer science |
30,290 | Image2Mesh: A Learning Framework for Single Image 3D Reconstruction | cs.CV | One challenge that remains open in 3D deep learning is how to efficiently
represent 3D data to feed deep networks. Recent works have relied on volumetric
or point cloud representations, but such approaches suffer from a number of
issues such as computational complexity, unordered data, and lack of finer
geometry. This ... | computer science |
30,291 | Do Convolutional Neural Networks act as Compositional Nearest Neighbors? | cs.CV | We present a simple approach based on pixel-wise nearest neighbors to
understand and interpret the internal operations of state-of-the-art neural
networks for pixel-level tasks. Specifically, we aim to understand the
synthesis and prediction mechanisms of state-of-the-art convolutional neural
networks for pixel-level t... | computer science |
30,292 | Road Extraction by Deep Residual U-Net | cs.CV | Road extraction from aerial images has been a hot research topic in the field
of remote sensing image analysis. In this letter, a semantic segmentation
neural network which combines the strengths of residual learning and U-Net is
proposed for road area extraction. The network is built with residual units and
has simila... | computer science |
30,293 | Interpretable Facial Relational Network Using Relational Importance | cs.CV | Human face analysis is an important task in computer vision. According to
cognitive-psychological studies, facial dynamics could provide crucial cues for
face analysis. In particular, the motion of facial local regions in facial
expression is related to the motion of other facial regions. In this paper, a
novel deep le... | computer science |
30,294 | Small Drone Field Experiment: Data Collection & Processing | cs.CV | Following an initiative formalized in April 2016 formally known as ARL West
between the U.S. Army Research Laboratory (ARL) and University of Southern
California's Institute for Creative Technologies (USC ICT), a field experiment
was coordinated and executed in the summer of 2016 by ARL, USC ICT, and
Headwall Photonics... | computer science |
30,295 | BLADE: Filter Learning for General Purpose Computational Photography | cs.CV | The Rapid and Accurate Image Super Resolution (RAISR) method of Romano,
Isidoro, and Milanfar is a computationally efficient image upscaling method
using a trained set of filters. We describe a generalization of RAISR, which we
name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable
edge-adaptive fi... | computer science |
30,296 | FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors | cs.CV | Face Super-Resolution (SR) is a domain-specific super-resolution problem. The
specific facial prior knowledge could be leveraged for better super-resolving
face images. We present a novel deep end-to-end trainable Face Super-Resolution
Network (FSRNet), which makes full use of the geometry prior, i.e., facial
landmark ... | computer science |
30,297 | Deep Depth Inference using Binocular and Monocular Cues | cs.CV | Human visual system relies on both binocular stereo cues and monocular
focusness cues to gain effective 3D perception. In computer vision, the two
problems are traditionally solved in separate tracks. In this paper, we present
a unified learning-based technique that simultaneously uses both types of cues
for depth infe... | computer science |
30,298 | Photo-to-Caricature Translation on Faces in the Wild | cs.CV | Recently, image-to-image translation has been made much progress owing to the
success of conditional Generative Adversarial Networks (cGANs). However, it's
still very challenging for translation tasks with the requirement of high-level
visual information conversion, such as photo-to-caricature translation that
requires... | computer science |
30,299 | Pipeline Generative Adversarial Networks for Facial Images Generation
with Multiple Attributes | cs.CV | Generative Adversarial Networks are proved to be efficient on various kinds
of image generation tasks. However, it is still a challenge if we want to
generate images precisely. Many researchers focus on how to generate images
with one attribute. But image generation under multiple attributes is still a
tough work. In t... | computer science |
30,300 | Convolutional Neural Networks for Breast Cancer Screening: Transfer
Learning with Exponential Decay | cs.CV | In this paper, we propose a Computer Assisted Diagnosis (CAD) system based on
a deep Convolutional Neural Network (CNN) model, to build an end-to-end
learning process that classifies breast mass lesions. We investigate the impact
that has transfer learning when large data is scarce, and explore the proper
way to fine-t... | computer science |
30,301 | Online Product Quantization | cs.CV | Approximate nearest neighbor (ANN) search has achieved great success in many
tasks. However, existing popular methods for ANN search, such as hashing and
quantization methods, are designed for static databases only. They cannot
handle well the database with data distribution evolving dynamically, due to
the high comput... | computer science |
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