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30,302 | Blind estimation of white Gaussian noise variance in highly textured
images | cs.CV | In the paper, a new method of blind estimation of noise variance in a single
highly textured image is proposed. An input image is divided into 8x8 blocks
and discrete cosine transform (DCT) is performed for each block. A part of 64
DCT coefficients with lowest energy calculated through all blocks is selected
for furthe... | computer science |
30,303 | DeepSkeleton: Skeleton Map for 3D Human Pose Regression | cs.CV | Despite recent success on 2D human pose estimation, 3D human pose estimation
still remains an open problem. A key challenge is the ill-posed depth ambiguity
nature. This paper presents a novel intermediate feature representation named
skeleton map for regression. It distills structural context from irrelavant
propertie... | computer science |
30,304 | Sparse Photometric 3D Face Reconstruction Guided by Morphable Models | cs.CV | We present a novel 3D face reconstruction technique that leverages sparse
photometric stereo (PS) and latest advances on face registration/modeling from
a single image. We observe that 3D morphable faces approach provides a
reasonable geometry proxy for light position calibration. Specifically, we
develop a robust opti... | computer science |
30,305 | PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation | cs.CV | We present PointFusion, a generic 3D object detection method that leverages
both image and 3D point cloud information. Unlike existing methods that either
use multi-stage pipelines or hold sensor and dataset-specific assumptions,
PointFusion is conceptually simple and application-agnostic. The image data and
the raw po... | computer science |
30,306 | Occlusion-aware Hand Pose Estimation Using Hierarchical Mixture Density
Network | cs.CV | Hand pose estimation is to predict the pose parameters representing a 3D hand
model, such as locations of hand joints. This problem is very challenging due
to large changes in viewpoints and articulations, and intense self-occlusions,
etc. Many researchers have investigated the problem from both aspects of input
featur... | computer science |
30,307 | Learning Spatio-temporal Features with Partial Expression Sequences for
on-the-Fly Prediction | cs.CV | Spatio-temporal feature encoding is essential for encoding facial expression
dynamics in video sequences. At test time, most spatio-temporal encoding
methods assume that a temporally segmented sequence is fed to a learned model,
which could require the prediction to wait until the full sequence is available
to an auxil... | computer science |
30,308 | Joint Blind Motion Deblurring and Depth Estimation of Light Field | cs.CV | Removing camera motion blur from a single light field is a challenging task
since it is highly ill-posed inverse problem. The problem becomes even worse
when blur kernel varies spatially due to scene depth variation and high-order
camera motion. In this paper, we propose a novel algorithm to estimate all blur
model var... | computer science |
30,309 | Local Jet Pattern: A Robust Descriptor for Texture Classification | cs.CV | Methods based on local image features have recently shown promise for texture
classification tasks, especially in the presence of large intra-class variation
due to illumination, scale, and viewpoint changes. Inspired by the theories of
image structure analysis, this paper presents a simple, efficient, yet robust
descr... | computer science |
30,310 | A Generative Model of 3D Object Layouts in Apartments | cs.CV | Understanding indoor scenes is an important task in computer vision. This
task is typically ambiguous, so we require a strong prior, that captures the
regularity of indoor environments. This is naturally expressed by a
probabilistic model over 3D room layouts and geometry, reasoning over complex
layouts in 3D space, in... | computer science |
30,311 | Saccade Sequence Prediction: Beyond Static Saliency Maps | cs.CV | Visual attention is a field with a considerable history, with eye movement
control and prediction forming an important subfield. Fixation modeling in the
past decades has been largely dominated computationally by a number of highly
influential bottom-up saliency models, such as the Itti-Koch-Niebur model. The
accuracy ... | computer science |
30,312 | Colour Constancy: Biologically-inspired Contrast Variant Pooling
Mechanism | cs.CV | Pooling is a ubiquitous operation in image processing algorithms that allows
for higher-level processes to collect relevant low-level features from a region
of interest. Currently, max-pooling is one of the most commonly used operators
in the computational literature. However, it can lack robustness to outliers
due to ... | computer science |
30,313 | Detection-aided liver lesion segmentation using deep learning | cs.CV | A fully automatic technique for segmenting the liver and localizing its
unhealthy tissues is a convenient tool in order to diagnose hepatic diseases
and assess the response to the according treatments. In this work we propose a
method to segment the liver and its lesions from Computed Tomography (CT) scans
using Convol... | computer science |
30,314 | Deep Learning for identifying radiogenomic associations in breast cancer | cs.CV | Purpose: To determine whether deep learning models can distinguish between
breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic
resonance imaging (DCE-MRI). Materials and methods: In this institutional
review board-approved single-center study, we analyzed DCE-MR images of 270
patients at our in... | computer science |
30,315 | Towards Alzheimer's Disease Classification through Transfer Learning | cs.CV | Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI
through machine learning have been a subject of intense research in recent
years. Recent success of deep learning in computer vision have progressed such
research further. However, common limitations with such algorithms are reliance
on a large nu... | computer science |
30,316 | Structured learning and detailed interpretation of minimal object images | cs.CV | We model the process of human full interpretation of object images, namely
the ability to identify and localize all semantic features and parts that are
recognized by human observers. The task is approached by dividing the
interpretation of the complete object to the interpretation of multiple reduced
but interpretable... | computer science |
30,317 | Optical Flow Guided Feature: A Fast and Robust Motion Representation for
Video Action Recognition | cs.CV | Motion representation plays a vital role in human action recognition in
videos. In this study, we introduce a novel compact motion representation for
video action recognition, named Optical Flow guided Feature (OFF), which
enables the network to distill temporal information through a fast and robust
approach. The OFF i... | computer science |
30,318 | Predicting Depression Severity by Multi-Modal Feature Engineering and
Fusion | cs.CV | We present our preliminary work to determine if patient's vocal acoustic,
linguistic, and facial patterns could predict clinical ratings of depression
severity, namely Patient Health Questionnaire depression scale (PHQ-8). We
proposed a multi modal fusion model that combines three different modalities:
audio, video , a... | computer science |
30,319 | Future Person Localization in First-Person Videos | cs.CV | We present a new task that predicts future locations of people observed in
first-person videos. Consider a first-person video stream continuously recorded
by a wearable camera. Given a short clip of a person that is extracted from the
complete stream, we aim to predict his location in future frames. To facilitate
this ... | computer science |
30,320 | A Closer Look at Spatiotemporal Convolutions for Action Recognition | cs.CV | In this paper we discuss several forms of spatiotemporal convolutions for
video analysis and study their effects on action recognition. Our motivation
stems from the observation that 2D CNNs applied to individual frames of the
video have remained solid performers in action recognition. In this work we
empirically demon... | computer science |
30,321 | ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene | cs.CV | Arbitrary-oriented text detection in the wild is a very challenging task, due
to the aspect ratio, scale, orientation, and illumination variations. In this
paper, we propose a novel method, namely Arbitrary-oriented Text (or ArbText
for short) detector, for efficient text detection in unconstrained natural
scene images... | computer science |
30,322 | A novel graph structure for salient object detection based on divergence
background and compact foreground | cs.CV | In this paper, we propose an efficient and discriminative model for salient
object detection. Our method is carried out in a stepwise mechanism based on
both divergence background and compact foreground cues. In order to effectively
enhance the distinction between nodes along object boundaries and the
similarity among ... | computer science |
30,323 | Unsupervised Learning for Cell-level Visual Representation in
Histopathology Images with Generative Adversarial Networks | cs.CV | The visual attributes of cells, such as the nuclear morphology and chromatin
openness, are critical for histopathology image analysis. By learning
cell-level visual representation, we can obtain a rich mix of features that are
highly reusable for various tasks, such as cell-level classification, nuclei
segmentation, an... | computer science |
30,324 | Radially-Distorted Conjugate Translations | cs.CV | This paper introduces the first minimal solvers that jointly solve for
affine-rectification and radial lens distortion from coplanar repeated
patterns. Even with imagery from moderately distorted lenses, plane
rectification using the pinhole camera model is inaccurate or invalid. The
proposed solvers incorporate lens d... | computer science |
30,325 | 3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds
Using Local Contextual Cues | cs.CV | 3D data such as point clouds and meshes are becoming more and more available.
The goal of this paper is to obtain 3D object and scene classification and
semantic segmentation. Because point clouds have irregular formats, most of the
existing methods convert the 3D data into multiple 2D projection images or 3D
voxel gri... | computer science |
30,326 | Improving Video Generation for Multi-functional Applications | cs.CV | In this paper, we aim to improve the state-of-the-art video generative
adversarial networks (GANs) with a view towards multi-functional applications.
Our improved video GAN model does not separate foreground from background nor
dynamic from static patterns, but learns to generate the entire video clip
conjointly. Our m... | computer science |
30,327 | Spatially-Adaptive Filter Units for Deep Neural Networks | cs.CV | Classical deep convolutional networks increase receptive field size by either
gradual resolution reduction or application of hand-crafted dilated
convolutions to prevent increase in the number of parameters. In this paper we
propose a novel displaced aggregation unit (DAU) that does not require
hand-crafting. In contra... | computer science |
30,328 | Auxiliary Guided Autoregressive Variational Autoencoders | cs.CV | Generative modeling of high-dimensional data is a key problem in machine
learning. Successful approaches include latent variable models and
autoregressive models. The complementary strengths of these approaches, to
model global and local image statistics respectively, suggest hybrid models
combining the strengths of bo... | computer science |
30,329 | ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban
Scenes | cs.CV | Exploiting synthetic data to learn deep models has attracted increasing
attention in recent years. However, the intrinsic domain difference between
synthetic and real images usually causes a significant performance drop when
applying the learned model to real world scenarios. This is mainly due to two
reasons: 1) the m... | computer science |
30,330 | Relation Networks for Object Detection | cs.CV | Although it is well believed for years that modeling relations between
objects would help object recognition, there has not been evidence that the
idea is working in the deep learning era. All state-of-the-art object detection
systems still rely on recognizing object instances individually, without
exploiting their rel... | computer science |
30,331 | Towards High Performance Video Object Detection | cs.CV | There has been significant progresses for image object detection in recent
years. Nevertheless, video object detection has received little attention,
although it is more challenging and more important in practical scenarios.
Built upon the recent works, this work proposes a unified approach based on
the principle of ... | computer science |
30,332 | Multi-Channel CNN-based Object Detection for Enhanced Situation
Awareness | cs.CV | Object Detection is critical for automatic military operations. However, the
performance of current object detection algorithms is deficient in terms of the
requirements in military scenarios. This is mainly because the object presence
is hard to detect due to the indistinguishable appearance and dramatic changes
of ob... | computer science |
30,333 | Super SloMo: High Quality Estimation of Multiple Intermediate Frames for
Video Interpolation | cs.CV | Given two consecutive frames, video interpolation aims at generating
intermediate frame(s) to form both spatially and temporally coherent video
sequences. While most existing methods focus on single-frame interpolation, we
propose an end-to-end convolutional neural network for variable-length
multi-frame video interpol... | computer science |
30,334 | Budget-Aware Activity Detection with A Recurrent Policy Network | cs.CV | In this paper, we address the challenging problem of effi- cient temporal
activity detection in untrimmed long videos. While most recent work has focused
and advanced the de- tection accuracy, the inference time can take seconds to
minutes in processing one video, which is computationally prohibitive for many
applicati... | computer science |
30,335 | Graph Distillation for Action Detection with Privileged Information | cs.CV | In this work, we propose a technique that tackles the video understanding
problem under a realistic, demanding condition in which we have limited labeled
data and partially observed training modalities. Common methods such as
transfer learning do not take advantage of the rich information from extra
modalities potentia... | computer science |
30,336 | Semantic Photometric Bundle Adjustment on Natural Sequences | cs.CV | The problem of obtaining dense reconstruction of an object in a natural
sequence of images has been long studied in computer vision. Classically this
problem has been solved through the application of bundle adjustment (BA). More
recently, excellent results have been attained through the application of
photometric bund... | computer science |
30,337 | Video retrieval based on deep convolutional neural network | cs.CV | Recently, with the enormous growth of online videos, fast video retrieval
research has received increasing attention. As an extension of image hashing
techniques, traditional video hashing methods mainly depend on hand-crafted
features and transform the real-valued features into binary hash codes. As
videos provide far... | computer science |
30,338 | Distance-based Camera Network Topology Inference for Person
Re-identification | cs.CV | In this paper, we propose a novel distance-based camera network topology
inference method for efficient person re-identification. To this end, we first
calibrate each camera and estimate relative scales between cameras. Using the
calibration results of multiple cameras, we calculate the speed of each person
and infer t... | computer science |
30,339 | Learning Depth from Monocular Videos using Direct Methods | cs.CV | The ability to predict depth from a single image - using recent advances in
CNNs - is of increasing interest to the vision community. Unsupervised
strategies to learning are particularly appealing as they can utilize much
larger and varied monocular video datasets during learning without the need for
ground truth depth... | computer science |
30,340 | Inertial-aided Rolling Shutter Relative Pose Estimation | cs.CV | Relative pose estimation is a fundamental problem in computer vision and it
has been studied for conventional global shutter cameras for decades. However,
recently, a rolling shutter camera has been widely used due to its low cost
imaging capability and, since the rolling shutter camera captures the image
line-by-line,... | computer science |
30,341 | Rank of Experts: Detection Network Ensemble | cs.CV | The recent advances of convolutional detectors show impressive performance
improvement for large scale object detection. However, in general, the
detection performance usually decreases as the object classes to be detected
increases, and it is a practically challenging problem to train a dominant
model for all classes ... | computer science |
30,342 | Delineation of Skin Strata in Reflectance Confocal Microscopy Images
using Recurrent Convolutional Networks with Toeplitz Attention | cs.CV | Reflectance confocal microscopy (RCM) is an effective, non-invasive
pre-screening tool for skin cancer diagnosis, but it requires extensive
training and experience to assess accurately. There are few quantitative tools
available to standardize image acquisition and analysis, and the ones that are
available are not inte... | computer science |
30,343 | 3D Facial Action Units Recognition for Emotional Expression | cs.CV | The muscular activities caused the activation of certain AUs for every facial
expression at the certain duration of time throughout the facial expression.
This paper presents the methods to recognise facial Action Unit (AU) using
facial distance of the facial features which activates the muscles. The seven
facial actio... | computer science |
30,344 | A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation | cs.CV | In this paper, we adopt 3D CNNs to segment the pancreas in CT images.
Although deep neural networks have been proven to be very effective on many 2D
vision tasks, it is still challenging to apply them to 3D applications due to
the limited amount of annotated 3D data and limited computational resources. We
propose a nov... | computer science |
30,345 | InverseNet: Solving Inverse Problems with Splitting Networks | cs.CV | We propose a new method that uses deep learning techniques to solve the
inverse problems. The inverse problem is cast in the form of learning an
end-to-end mapping from observed data to the ground-truth. Inspired by the
splitting strategy widely used in regularized iterative algorithm to tackle
inverse problems, the ma... | computer science |
30,346 | Real-time Semantic Image Segmentation via Spatial Sparsity | cs.CV | We propose an approach to semantic (image) segmentation that reduces the
computational costs by a factor of 25 with limited impact on the quality of
results. Semantic segmentation has a number of practical applications, and for
most such applications the computational costs are critical. The method follows
a typical tw... | computer science |
30,347 | Learning Deep Representations for Word Spotting Under Weak Supervision | cs.CV | Convolutional Neural Networks have made their mark in various fields of
computer vision in recent years. They have achieved state-of-the-art
performance in the field of document analysis as well. However, CNNs require a
large amount of annotated training data and, hence, great manual effort. In our
approach, we introdu... | computer science |
30,348 | Deformable Shape Completion with Graph Convolutional Autoencoders | cs.CV | The availability of affordable and portable depth sensors has made scanning
objects and people simpler than ever. However, dealing with occlusions and
missing parts is still a significant challenge. The problem of reconstructing a
(possibly non-rigidly moving) 3D object from a single or multiple partial scans
has recei... | computer science |
30,349 | Neural Signatures for Licence Plate Re-identification | cs.CV | The problem of vehicle licence plate re-identification is generally
considered as a one-shot image retrieval problem. The objective of this task is
to learn a feature representation (called a "signature") for licence plates.
Incoming licence plate images are converted to signatures and matched to a
previously collected... | computer science |
30,350 | Unsupervised Generative Adversarial Cross-modal Hashing | cs.CV | Cross-modal hashing aims to map heterogeneous multimedia data into a common
Hamming space, which can realize fast and flexible retrieval across different
modalities. Unsupervised cross-modal hashing is more flexible and applicable
than supervised methods, since no intensive labeling work is involved. However,
existing ... | computer science |
30,351 | Precision Learning: Towards Use of Known Operators in Neural Networks | cs.CV | In this paper, we consider the use of prior knowledge within neural networks.
In particular, we investigate the effect of a known transform within the
mapping from input data space to the output domain. We demonstrate that use of
known transforms is able to change maximal error bounds.
In order to explore the effect ... | computer science |
30,352 | Unsupervised Classification of PolSAR Data Using a Scattering Similarity
Measure Derived from a Geodesic Distance | cs.CV | In this letter, we propose a novel technique for obtaining scattering
components from Polarimetric Synthetic Aperture Radar (PolSAR) data using the
geodesic distance on the unit sphere. This geodesic distance is obtained
between an elementary target and the observed Kennaugh matrix, and it is
further utilized to comput... | computer science |
30,353 | Single-Shot Object Detection with Enriched Semantics | cs.CV | We propose a novel single shot object detection network named Detection with
Enriched Semantics (DES). Our motivation is to enrich the semantics of object
detection features within a typical deep detector, by a semantic segmentation
branch and a location-agnostic module. The segmentation branch is supervised by
weak se... | computer science |
30,354 | Unsupervised Learning for Color Constancy | cs.CV | Most digital camera pipelines use color constancy methods to reduce the
influence of illumination and camera sensor on the colors of scene objects. The
highest accuracy of color correction is obtained with learning-based color
constancy methods, but they require a significant amount of calibrated training
images with k... | computer science |
30,355 | Image to Image Translation for Domain Adaptation | cs.CV | We propose a general framework for unsupervised domain adaptation, which
allows deep neural networks trained on a source domain to be tested on a
different target domain without requiring any training annotations in the
target domain. This is achieved by adding extra networks and losses that help
regularize the feature... | computer science |
30,356 | Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural
Networks | cs.CV | Smart systems that can accurately diagnose patients with mental disorders and
identify effective treatments based on brain functional imaging data are of
great applicability and are gaining much attention. Most previous machine
learning studies use hand-designed features, such as functional connectivity,
which does not... | computer science |
30,357 | Multi-Content GAN for Few-Shot Font Style Transfer | cs.CV | In this work, we focus on the challenge of taking partial observations of
highly-stylized text and generalizing the observations to generate unobserved
glyphs in the ornamented typeface. To generate a set of multi-content images
following a consistent style from very few examples, we propose an end-to-end
stacked condi... | computer science |
30,358 | Splenomegaly Segmentation using Global Convolutional Kernels and
Conditional Generative Adversarial Networks | cs.CV | Spleen volume estimation using automated image segmentation technique may be
used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance
Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN)
segmentation methods have demonstrated advantages for abdominal organ
segmentation. ... | computer science |
30,359 | Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas
Segmentation | cs.CV | Whole brain segmentation and cortical surface parcellation are essential in
understanding the anatomical-functional relationships of the brain. Multi-atlas
segmentation has been regarded as one of the leading segmentation methods for
the whole brain segmentation. In our recent work, the multi-atlas technique has
been a... | computer science |
30,360 | Lecture video indexing using boosted margin maximizing neural networks | cs.CV | This paper presents a novel approach for lecture video indexing using a
boosted deep convolutional neural network system. The indexing is performed by
matching high quality slide images, for which text is either known or
extracted, to lower resolution video frames with possible noise, perspective
distortion, and occlus... | computer science |
30,361 | Fruit recognition from images using deep learning | cs.CV | In this paper we introduce a new, high-quality, dataset of images containing
fruits. We also present the results of some numerical experiment for training a
neural network to detect fruits. We discuss the reason why we chose to use
fruits in this project by proposing a few applications that could use this kind
of neura... | computer science |
30,362 | Taming Adversarial Domain Transfer with Structural Constraints for Image
Enhancement | cs.CV | The goal of this work is to improve images of traffic scenes that are
degraded by natural causes such as fog, rain and limited visibility during the
night. For these applications, it is next to impossible to get pixel perfect
pairs of the same scene, with and without the degrading conditions. This makes
it unsuitable f... | computer science |
30,363 | From Pixels to Object Sequences: Recurrent Semantic Instance
Segmentation | cs.CV | We present a recurrent model for semantic instance segmentation that
sequentially generates binary masks and their associated class probabilities
for every object in an image. Our proposed system is trainable end-to-end from
an input image to a sequence of labeled masks and, compared to methods relying
on object propos... | computer science |
30,364 | DR-Net: Transmission Steered Single Image Dehazing Network with Weakly
Supervised Refinement | cs.CV | Despite the recent progress in image dehazing, several problems remain
largely unsolved such as robustness for varying scenes, the visual quality of
reconstructed images, and effectiveness and flexibility for applications. To
tackle these problems, we propose a new deep network architecture for single
image dehazing ca... | computer science |
30,365 | Compressed Video Action Recognition | cs.CV | Training robust deep video representations has proven to be much more
challenging than learning deep image representations and consequently hampered
tasks like video action recognition. This is in part due to the enormous size
of raw video streams, the associated amount of computation required, and the
high temporal re... | computer science |
30,366 | GAGAN: Geometry-Aware Generative Adversarial Networks | cs.CV | Deep generative models learned through adversarial training have become
increasingly popular for their ability to generate naturalistic image textures.
However, apart from the visual texture, the visual appearance of objects is
significantly affected by their shape geometry, information which is not taken
into account ... | computer science |
30,367 | Low-Rank Tensor Completion by Truncated Nuclear Norm Regularization | cs.CV | Currently, low-rank tensor completion has gained cumulative attention in
recovering incomplete visual data whose partial elements are missing. By taking
a color image or video as a three-dimensional (3D) tensor, previous studies
have suggested several definitions of tensor nuclear norm. However, they have
limitations a... | computer science |
30,368 | Automatic Recognition of Coal and Gangue based on Convolution Neural
Network | cs.CV | We designed a gangue sorting system,and built a convolutional neural network
model based on AlexNet. Data enhancement and transfer learning are used to
solve the problem which the convolution neural network has insufficient
training data in the training stage. An object detection and region clipping
algorithm is propos... | computer science |
30,369 | Feature Agglomeration Networks for Single Stage Face Detection | cs.CV | Recent years have witnessed promising results of face detection using deep
learning, especially for the family of region-based convolutional neural
networks (R-CNN) methods and their variants. Despite making remarkable
progresses, face detection in the wild remains an open research challenge
especially when detecting f... | computer science |
30,370 | Cascade R-CNN: Delving into High Quality Object Detection | cs.CV | In object detection, an intersection over union (IoU) threshold is required
to define positives and negatives. An object detector, trained with low IoU
threshold, e.g. 0.5, usually produces noisy detections. However, detection
performance tends to degrade with increasing the IoU thresholds. Two main
factors are respons... | computer science |
30,371 | Multimodal Visual Concept Learning with Weakly Supervised Techniques | cs.CV | Despite the availability of a huge amount of video data accompanied by
descriptive texts, it is not always easy to exploit the information contained
in natural language in order to automatically recognize video concepts. Towards
this goal, in this paper we use textual cues as means of supervision,
introducing two weakl... | computer science |
30,372 | A Deep Learning Approach to Drone Monitoring | cs.CV | A drone monitoring system that integrates deep-learning-based detection and
tracking modules is proposed in this work. The biggest challenge in adopting
deep learning methods for drone detection is the limited amount of training
drone images. To address this issue, we develop a model-based drone
augmentation technique ... | computer science |
30,373 | Learning Object Detectors from Scratch with Gated Recurrent Feature
Pyramids | cs.CV | In this paper, we propose gated recurrent feature pyramid for the problem of
learning object detection from scratch. Our approach is motivated by the recent
work of deeply supervised object detector (DSOD), but explores new network
architecture that dynamically adjusts the supervision intensities of
intermediate layers... | computer science |
30,374 | Composition-aided Sketch-realistic Portrait Generation | cs.CV | Sketch portrait generation is of wide applications including digital
entertainment and law enforcement. Despite the great progress achieved by
existing face sketch generation methods, they mostly yield blurred effects and
great deformation over various facial parts. In order to tackle this challenge,
we propose a novel... | computer science |
30,375 | Learning Reduced-Resolution and Super-Resolution Networks in Synch | cs.CV | Recent studies have shown that deep convolutional neural networks achieve the
excellent performance on image super-resolution. However, CNN-based methods
restore the super-resolution results depending on interpolations a lot. In this
paper, we present an end-to-end network (Reduced & Super-Resolution Network,
RSRNet) f... | computer science |
30,376 | Composite Quantization | cs.CV | This paper studies the compact coding approach to approximate nearest
neighbor search. We introduce a composite quantization framework. It uses the
composition of several ($M$) elements, each of which is selected from a
different dictionary, to accurately approximate a $D$-dimensional vector, thus
yielding accurate sea... | computer science |
30,377 | FSSD: Feature Fusion Single Shot Multibox Detector | cs.CV | SSD (Single Shot Multibox Detetor) is one of the best object detection
algorithms with both high accuracy and fast speed. However, SSD's feature
pyramid detection method makes it hard to fuse the features from different
scales. In this paper, we proposed FSSD (Feature Fusion Single Shot Multibox
Detector), an enhanced ... | computer science |
30,378 | Leaf Identification Using a Deep Convolutional Neural Network | cs.CV | Convolutional neural networks (CNNs) have become popular especially in
computer vision in the last few years because they achieved outstanding
performance on different tasks, such as image classifications. We propose a
nine-layer CNN for leaf identification using the famous Flavia and Foliage
datasets. Usually the supe... | computer science |
30,379 | Face Translation between Images and Videos using Identity-aware CycleGAN | cs.CV | This paper presents a new problem of unpaired face translation between images
and videos, which can be applied to facial video prediction and enhancement. In
this problem there exist two major technical challenges: 1) designing a robust
translation model between static images and dynamic videos, and 2) preserving
facia... | computer science |
30,380 | Feature Generating Networks for Zero-Shot Learning | cs.CV | Suffering from the extreme training data imbalance between seen and unseen
classes, most of existing state-of-the-art approaches fail to achieve
satisfactory results for the challenging generalized zero-shot learning task.
To circumvent the need for labeled examples of unseen classes, we propose a
novel generative adve... | computer science |
30,381 | Energy-relaxed Wasserstein GANs(EnergyWGAN): Towards More Stable and
High Resolution Image Generation | cs.CV | Recently, generative adversarial networks (GANs) have achieved great impacts
on a broad number of applications, including low resolution(LR) image
synthesis. However, they suffer from unstable training especially when image
resolution increases. To overcome this bottleneck, this paper generalizes the
state-of-the-art W... | computer science |
30,382 | Towards Faster Training of Global Covariance Pooling Networks by
Iterative Matrix Square Root Normalization | cs.CV | Global covariance pooling in Convolutional neural neworks has achieved
impressive improvement over the classical first-order pooling. Recent works
have shown matrix square root normalization plays a central role in achieving
state-of-the-art performance. However, existing methods depending heavily on
eigenvalue decompo... | computer science |
30,383 | Learning Deep Correspondence through Prior and Posterior Feature
Constancy | cs.CV | Stereo matching algorithms usually consist of four steps, including matching
cost calculation, matching cost aggregation, disparity calculation, and
disparity refinement. Existing CNN-based methods only adopt CNN to solve parts
of the four steps, or use different networks to deal with different steps,
making them diffi... | computer science |
30,384 | CNN based Learning using Reflection and Retinex Models for Intrinsic
Image Decomposition | cs.CV | Most of the traditional work on intrinsic image decomposition rely on
deriving priors about scene characteristics. On the other hand, recent research
use deep learning models as in-and-out black box and do not consider the
well-established, traditional image formation process as the basis of their
intrinsic learning pr... | computer science |
30,385 | GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB | cs.CV | We address the highly challenging problem of real-time 3D hand tracking based
on a monocular RGB-only sequence. Our tracking method combines a convolutional
neural network with a kinematic 3D hand model, such that it generalizes well to
unseen data, is robust to occlusions and varying camera viewpoints, and leads
to an... | computer science |
30,386 | SOT for MOT | cs.CV | In this paper we present a robust tracker to solve the multiple object
tracking (MOT) problem, under the framework of tracking-by-detection. As the
first contribution, we innovatively combine single object tracking (SOT)
algorithms with multiple object tracking algorithms, and our results show that
SOT is a general way... | computer science |
30,387 | A Generalized Motion Pattern and FCN based approach for retinal fluid
detection and segmentation | cs.CV | SD-OCT is a non-invasive cross-sectional imaging modality used for diagnosis
of macular defects. Efficient detection and segmentation of the abnormalities
seen as biomarkers in OCT can help in analyzing the progression of the disease
and advising effective treatment for the associated disease. In this work, we
propose ... | computer science |
30,388 | Robust 3D Action Recognition through Sampling Local Appearances and
Global Distributions | cs.CV | 3D action recognition has broad applications in human-computer interaction
and intelligent surveillance. However, recognizing similar actions remains
challenging since previous literature fails to capture motion and shape cues
effectively from noisy depth data. In this paper, we propose a novel two-layer
Bag-of-Visual-... | computer science |
30,389 | An End-to-end 3D Convolutional Neural Network for Action Detection and
Segmentation in Videos | cs.CV | In this paper, we propose an end-to-end 3D CNN for action detection and
segmentation in videos. The proposed architecture is a unified deep network
that is able to recognize and localize action based on 3D convolution features.
A video is first divided into equal length clips and next for each clip a set
of tube propos... | computer science |
30,390 | Learning to Segment Moving Objects | cs.CV | We study the problem of segmenting moving objects in unconstrained videos.
Given a video, the task is to segment all the objects that exhibit independent
motion in at least one frame. We formulate this as a learning problem and
design our framework with three cues: (i) independent object motion between a
pair of frames... | computer science |
30,391 | Why my photos look sideways or upside down? Detecting Canonical
Orientation of Images using Convolutional Neural Networks | cs.CV | Image orientation detection requires high-level scene understanding. Humans
use object recognition and contextual scene information to correctly orient
images. In literature, the problem of image orientation detection is mostly
confronted by using low-level vision features, while some approaches
incorporate few easily ... | computer science |
30,392 | Iterative Deep Learning for Network Topology Extraction | cs.CV | This paper tackles the task of estimating the topology of filamentary
networks such as retinal vessels and road networks. Building on top of a global
model that performs a dense semantical classification of the pixels of the
image, we design a Convolutional Neural Network (CNN) that predicts the local
connectivity betw... | computer science |
30,393 | A Perceptual Measure for Deep Single Image Camera Calibration | cs.CV | Most current single image camera calibration methods rely on specific image
features or user input, and cannot be applied to natural images captured in
uncontrolled settings. We propose inferring directly camera calibration
parameters from a single image using a deep convolutional neural network. This
network is traine... | computer science |
30,394 | SfSNet : Learning Shape, Reflectance and Illuminance of Faces in the
Wild | cs.CV | We present SfSNet, an end-to-end learning framework for producing an accurate
decomposition of an unconstrained image of a human face into shape, reflectance
and illuminance. Our network is designed to reflect a physical lambertian
rendering model. SfSNet learns from a mixture of labeled synthetic and
unlabeled real wo... | computer science |
30,395 | Self-supervised Learning of Motion Capture | cs.CV | Current state-of-the-art solutions for motion capture from a single camera
are optimization driven: they optimize the parameters of a 3D human model so
that its re-projection matches measurements in the video (e.g. person
segmentation, optical flow, keypoint detections etc.). Optimization models are
susceptible to loca... | computer science |
30,396 | Long-Term Visual Object Tracking Benchmark | cs.CV | In this paper, we propose a new long video dataset (called Track Long and
Prosper - TLP) and benchmark for visual object tracking. The dataset consists
of 50 videos from real world scenarios, encompassing a duration of over 400
minutes (676K frames), making it more than 20 folds larger in average duration
per sequence ... | computer science |
30,397 | 3D Semantic Trajectory Reconstruction from 3D Pixel Continuum | cs.CV | This paper presents a method to reconstruct dense semantic trajectory stream
of human interactions in 3D from synchronized multiple videos. The interactions
inherently introduce self-occlusion and illumination/appearance/shape changes,
resulting in highly fragmented trajectory reconstruction with noisy and coarse
seman... | computer science |
30,398 | A+D-Net: Shadow Detection with Adversarial Shadow Attenuation | cs.CV | Single image shadow detection is a very challenging problem because of the
limited amount of information available in one image, as well as the scarcity
of annotated training data. In this work, we propose a novel adversarial
training based framework that yields a high performance shadow detection
network (D-Net). D-Ne... | computer science |
30,399 | Imagine it for me: Generative Adversarial Approach for Zero-Shot
Learning from Noisy Texts | cs.CV | Most existing zero-shot learning methods consider the problem as a visual
semantic embedding one. Given the demonstrated capability of Generative
Adversarial Networks(GANs) to generate images, we instead leverage GANs to
imagine unseen categories from text descriptions and hence recognize novel
classes with no examples... | computer science |
30,400 | Visual to Sound: Generating Natural Sound for Videos in the Wild | cs.CV | As two of the five traditional human senses (sight, hearing, taste, smell,
and touch), vision and sound are basic sources through which humans understand
the world. Often correlated during natural events, these two modalities combine
to jointly affect human perception. In this paper, we pose the task of
generating soun... | computer science |
30,401 | Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep
Learning in Self Driving Cars | cs.CV | As an initial assessment, over 480,000 labeled virtual images of normal
highway driving were readily generated in Grand Theft Auto V's virtual
environment. Using these images, a CNN was trained to detect following distance
to cars/objects ahead, lane markings, and driving angle (angular heading
relative to lane centerl... | computer science |
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