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30,802 | Feature Space Transfer for Data Augmentation | cs.CV | The problem of data augmentation in feature space is considered. A new
architecture, denoted the FeATure TransfEr Network (FATTEN), is proposed for
the modeling of feature trajectories induced by variations of object pose. This
architecture exploits a parametrization of the pose manifold in terms of pose
and appearance... | computer science |
30,803 | Inverted Residuals and Linear Bottlenecks: Mobile Networks for
Classification, Detection and Segmentation | cs.CV | In this paper we describe a new mobile architecture, MobileNetV2, that
improves the state of the art performance of mobile models on multiple tasks
and benchmarks as well as across a spectrum of different model sizes. We also
describe efficient ways of applying these mobile models to object detection in
a novel framewo... | computer science |
30,804 | Semi-supervised Fisher vector network | cs.CV | In this work we explore how the architecture proposed in [8], which expresses
the processing steps of the classical Fisher vector pipeline approaches, i.e.
dimensionality reduction by principal component analysis (PCA) projection,
Gaussian mixture model (GMM) and Fisher vector descriptor extraction as network
layers, c... | computer science |
30,805 | Size-to-depth: A New Perspective for Single Image Depth Estimation | cs.CV | In this paper we consider the problem of single monocular image depth
estimation. It is a challenging problem due to its ill-posedness nature and has
found wide application in industry. Previous efforts belongs roughly to two
families: learning-based method and interactive method. Learning-based method,
in which deep c... | computer science |
30,806 | Deep Net Triage: Analyzing the Importance of Network Layers via
Structural Compression | cs.CV | Despite their prevalence, deep networks are poorly understood. This is due,
at least in part, to their highly parameterized nature. As such, while certain
structures have been found to work better than others, the significance of a
model's unique structure, or the importance of a given layer, and how these
translate to... | computer science |
30,807 | Hyperspectral recovery from RGB images using Gaussian Processes | cs.CV | Hyperspectral cameras preserve the fine spectral details of scenes that are
generally lost in the traditional RGB cameras due to the gross quantization of
radiance. These details are desirable in numerous imaging applications,
nevertheless the high cost of hyperspectral hardware and the associated
physical constraints ... | computer science |
30,808 | Efficient Trimmed Convolutional Arithmetic Encoding for Lossless Image
Compression | cs.CV | Arithmetic encoding is an essential class of coding techniques which have
been widely used in various data compression systems and exhibited promising
performance. One key issue of arithmetic encoding method is to predict the
probability of the current symbol to be encoded from its context, i.e., the
preceding encoded ... | computer science |
30,809 | Combining Stereo Disparity and Optical Flow for Basic Scene Flow | cs.CV | Scene flow is a description of real world motion in 3D that contains more
information than optical flow. Because of its complexity there exists no
applicable variant for real-time scene flow estimation in an automotive or
commercial vehicle context that is sufficiently robust and accurate. Therefore,
many applications ... | computer science |
30,810 | SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm | cs.CV | Speckle noise, inherent in synthetic aperture radar (SAR) images, degrades
the performance of the various SAR image analysis tasks. Thus, speckle noise
reduction is a critical preprocessing step for smoothing homogeneous regions
while preserving details. This letter proposes a variational despeckling
approach where L1-... | computer science |
30,811 | Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly | cs.CV | Learning similarity functions between image pairs with deep neural networks
yields highly correlated activations of embeddings. In this work, we show how
to improve the robustness of such embeddings by exploiting the independence
within ensembles. To this end, we divide the last embedding layer of a deep
network into a... | computer science |
30,812 | Classification of histopathological breast cancer images using iterative
VMD aided Zernike moments & textural signatures | cs.CV | In this paper we present a novel method for an automated diagnosis of breast
carcinoma through multilevel iterative variational mode decomposition (VMD) and
textural features encompassing Zernaike moments, fractal dimension and entropy
features namely, Kapoor entropy, Renyi entropy, Yager entropy features are
extracted... | computer science |
30,813 | Detecting abnormal events in video using Narrowed Motion Clusters | cs.CV | We formulate the abnormal event detection problem as an outlier detection
task and we propose a two-stage algorithm based on k-means clustering and
one-class Support Vector Machines (SVM) to eliminate outliers. After extracting
motion features from the training video containing only normal events, we apply
k-means clus... | computer science |
30,814 | Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles
Segmentation in Brain MR | cs.CV | Ventricular volume and its progression are known to be linked to several
brain diseases such as dementia and schizophrenia. Therefore accurate
measurement of ventricle volume is vital for longitudinal studies on these
disorders, making automated ventricle segmentation algorithms desirable. In the
past few years, deep n... | computer science |
30,815 | Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis | cs.CV | We propose a novel hierarchical approach for text-to-image synthesis by
inferring semantic layout. Instead of learning a direct mapping from text to
image, our algorithm decomposes the generation process into multiple steps, in
which it first constructs a semantic layout from the text by the layout
generator and conver... | computer science |
30,816 | Reblur2Deblur: Deblurring Videos via Self-Supervised Learning | cs.CV | Motion blur is a fundamental problem in computer vision as it impacts image
quality and hinders inference. Traditional deblurring algorithms leverage the
physics of the image formation model and use hand-crafted priors: they usually
produce results that better reflect the underlying scene, but present
artifacts. Recent... | computer science |
30,817 | Localization-Aware Active Learning for Object Detection | cs.CV | Active learning - a class of algorithms that iteratively searches for the
most informative samples to include in a training dataset - has been shown to
be effective at annotating data for image classification. However, the use of
active learning for object detection is still largely unexplored as determining
informativ... | computer science |
30,818 | An Accurate and Real-time Self-blast Glass Insulator Location Method
Based On Faster R-CNN and U-net with Aerial Images | cs.CV | The location of broken insulators in aerial images is a challenging task.
This paper, focusing on the self-blast glass insulator, proposes a deep
learning solution. We address the broken insulators location problem as a low
signal-noise-ratio image location framework with two modules: 1) object
detection based on Fast ... | computer science |
30,819 | Deep Multi-Spectral Registration Using Invariant Descriptor Learning | cs.CV | In this paper, we introduce a novel deep-learning method to align
cross-spectral images. Our approach relies on a learned descriptor which is
invariant to different spectra. Multi-modal images of the same scene capture
different signals and therefore their registration is challenging and it is not
solved by classic app... | computer science |
30,820 | Fully Convolutional Multi-scale Residual DenseNets for Cardiac
Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers | cs.CV | Deep fully convolutional neural network (FCN) based architectures have shown
great potential in medical image segmentation. However, such architectures
usually have millions of parameters and inadequate number of training samples
leading to over-fitting and poor generalization. In this paper, we present a
novel highly ... | computer science |
30,821 | Long-term Visual Localization using Semantically Segmented Images | cs.CV | Robust cross-seasonal localization is one of the major challenges in
long-term visual navigation of autonomous vehicles. In this paper, we exploit
recent advances in semantic segmentation of images, i.e., where each pixel is
assigned a label related to the type of object it represents, to attack the
problem of long-ter... | computer science |
30,822 | Unsupervised Representation Learning with Laplacian Pyramid
Auto-encoders | cs.CV | Scale-space representation has been popular in computer vision community due
to its theoretical foundation. The motivation for generating a scale-space
representation of a given data set originates from the basic observation that
real-world objects are composed of different structures at different scales.
Hence, it's r... | computer science |
30,823 | Joint registration and synthesis using a probabilistic model for
alignment of MRI and histological sections | cs.CV | Nonlinear registration of 2D histological sections with corresponding slices
of MRI data is a critical step of 3D histology reconstruction. This task is
difficult due to the large differences in image contrast and resolution, as
well as the complex nonrigid distortions produced when sectioning the sample
and mounting i... | computer science |
30,824 | Autonomous Driving in Reality with Reinforcement Learning and Image
Translation | cs.CV | Supervised learning is widely used in training autonomous driving vehicle.
However, it is trained with large amount of supervised labeled data.
Reinforcement learning can be trained without abundant labeled data, but we
cannot train it in reality because it would involve many unpredictable
accidents. Nevertheless, trai... | computer science |
30,825 | Benchmark Visual Question Answer Models by using Focus Map | cs.CV | Inferring and Executing Programs for Visual Reasoning proposes a model for
visual reasoning that consists of a program generator and an execution engine
to avoid end-to-end models. To show that the model actually learns which
objects to focus on to answer the questions, the authors give a visualization
of the norm of t... | computer science |
30,826 | Re-ID done right: towards good practices for person re-identification | cs.CV | Training a deep architecture using a ranking loss has become standard for the
person re-identification task. Increasingly, these deep architectures include
additional components that leverage part detections, attribute predictions,
pose estimators and other auxiliary information, in order to more effectively
localize a... | computer science |
30,827 | Learning Deep Features for One-Class Classification | cs.CV | We propose a deep learning-based solution for the problem of feature learning
in one-class classification. The proposed method operates on top of a
Convolutional Neural Network (CNN) of choice and produces descriptive features
while maintaining a low intra-class variance in the feature space for the given
class. For th... | computer science |
30,828 | Low-Shot Learning from Imaginary Data | cs.CV | Humans can quickly learn new visual concepts, perhaps because they can easily
visualize or imagine what novel objects look like from different views.
Incorporating this ability to hallucinate novel instances of new concepts might
help machine vision systems perform better low-shot learning, i.e., learning
concepts from... | computer science |
30,829 | An Automated System for Epilepsy Detection using EEG Brain Signals based
on Deep Learning Approach | cs.CV | Epilepsy is a neurological disorder and for its detection, encephalography
(EEG) is a commonly used clinical approach. Manual inspection of EEG brain
signals is a time-consuming and laborious process, which puts heavy burden on
neurologists and affects their performance. Several automatic techniques have
been proposed ... | computer science |
30,830 | ConvSRC: SmartPhone based Periocular Recognition using Deep
Convolutional Neural Network and Sparsity Augmented Collaborative
Representation | cs.CV | Smartphone based periocular recognition has gained significant attention from
biometric research community because of the limitations of biometric modalities
like face, iris etc. Most of the existing methods for periocular recognition
employ hand-crafted features. Recently, learning based image representation
technique... | computer science |
30,831 | Semi-supervised FusedGAN for Conditional Image Generation | cs.CV | We present FusedGAN, a deep network for conditional image synthesis with
controllable sampling of diverse images. Fidelity, diversity and controllable
sampling are the main quality measures of a good image generation model. Most
existing models are insufficient in all three aspects. The FusedGAN can perform
controllabl... | computer science |
30,832 | Fruit Quantity and Quality Estimation using a Robotic Vision System | cs.CV | Accurate localisation of crop remains highly challenging in unstructured
environments such as farms. Many of the developed systems still rely on the use
of hand selected features for crop identification and often neglect the
estimation of crop quantity and quality, which is key to assigning labor during
farming process... | computer science |
30,833 | Image Captioning using Deep Neural Architectures | cs.CV | Automatically creating the description of an image using any natural
languages sentence like English is a very challenging task. It requires
expertise of both image processing as well as natural language processing. This
paper discuss about different available models for image captioning task. We
have also discussed ab... | computer science |
30,834 | Light-weight pixel context encoders for image inpainting | cs.CV | In this work we propose Pixel Content Encoders (PCE), a light-weight image
inpainting model, capable of generating novel con-tent for large missing
regions in images. Unlike previously presented convolutional neural network
based models, our PCE model has an order of magnitude fewer trainable
parameters. Moreover, by i... | computer science |
30,835 | Additive Margin Softmax for Face Verification | cs.CV | In this paper, we propose a conceptually simple and geometrically
interpretable objective function, i.e. additive margin Softmax (AM-Softmax),
for deep face verification. In general, the face verification task can be
viewed as a metric learning problem, so learning large-margin face features
whose intra-class variation... | computer science |
30,836 | Multi-View Stereo 3D Edge Reconstruction | cs.CV | This paper presents a novel method for the reconstruction of 3D edges in
multi-view stereo scenarios. Previous research in the field typically relied on
video sequences and limited the reconstruction process to either straight
line-segments, or edge-points, i.e., 3D points that correspond to image edges.
We instead pro... | computer science |
30,837 | Face Recognition via Centralized Coordinate Learning | cs.CV | Owe to the rapid development of deep neural network (DNN) techniques and the
emergence of large scale face databases, face recognition has achieved a great
success in recent years. During the training process of DNN, the face features
and classification vectors to be learned will interact with each other, while
the dis... | computer science |
30,838 | TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image
Segmentation | cs.CV | Pixel-wise image segmentation is demanding task in computer vision. Classical
U-Net architectures composed of encoders and decoders are very popular for
segmentation of medical images, satellite images etc. Typically, neural network
initialized with weights from a network pre-trained on a large data set like
ImageNet s... | computer science |
30,839 | Sparsely Connected Convolutional Networks | cs.CV | Residual learning with skip connections permits training ultra-deep neural
networks and obtains superb performance. Building in this direction, DenseNets
proposed a dense connection structure where each layer is directly connected to
all of its predecessors. The densely connected structure leads to better
information f... | computer science |
30,840 | On the influence of Dice loss function in multi-class organ segmentation
of abdominal CT using 3D fully convolutional networks | cs.CV | Deep learning-based methods achieved impressive results for the segmentation
of medical images. With the development of 3D fully convolutional networks
(FCNs), it has become feasible to produce improved results for multi-organ
segmentation of 3D computed tomography (CT) images. The results of multi-organ
segmentation u... | computer science |
30,841 | Extend the shallow part of Single Shot MultiBox Detector via
Convolutional Neural Network | cs.CV | Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the
current object detection field, which uses fully convolutional neural network
to detect all scaled objects in an image. Deconvolutional Single Shot Detector
(DSSD) is an approach which introduces more context information by adding the
deconvolu... | computer science |
30,842 | PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark | cs.CV | Thermal infrared (TIR) pedestrian tracking is one of the most important
components in numerous applications of computer vision, which has a major
advantage: it can track the pedestrians in total darkness. How to evaluate the
TIR pedestrian tracker fairly on a benchmark dataset is significant for the
development of this... | computer science |
30,843 | 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer
disease studies | cs.CV | Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal
form, Mild Cognitive Impairment (MCI), has been the subject of extensive
research in recent years. Some recent studies have shown promising results in
the AD and MCI determination using structural and functional Magnetic Resonance
Imaging (sMRI... | computer science |
30,844 | RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face
Alignment | cs.CV | We propose a novel method for real-time face alignment in videos based on a
recurrent encoder-decoder network model. Our proposed model predicts 2D facial
point heat maps regularized by both detection and regression loss, while
uniquely exploiting recurrent learning at both spatial and temporal dimensions.
At the spati... | computer science |
30,845 | An End-to-End Deep Learning Histochemical Scoring System for Breast
Cancer Tissue Microarray | cs.CV | One of the methods for stratifying different molecular classes of breast
cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer
relevant biomarkers to stain tumour tissues prepared on tissue microarray
(TMA). To determine the molecular class of the tumour, pathologists will have
to manually mark... | computer science |
30,846 | Fully Point-wise Convolutional Neural Network for Modeling Statistical
Regularities in Natural Images | cs.CV | Modeling statistical regularities is the problem of representing the pixel
distributions in natural images, and usually applied to solve the ill-posed
image processing problems. In this paper, we present an extremely efficient CNN
architecture for modeling statistical regularities. Our method is based on the
observatio... | computer science |
30,847 | SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial
Beauty Prediction | cs.CV | Facial beauty prediction (FBP) is a significant visual recognition problem to
make assessment of facial attractiveness that is consistent to human
perception. To tackle this problem, various data-driven models, especially
state-of-the-art deep learning techniques, were introduced, and benchmark
dataset become one of th... | computer science |
30,848 | Quality Classified Image Analysis with Application to Face Detection and
Recognition | cs.CV | Motion blur, out of focus, insufficient spatial resolution, lossy compression
and many other factors can all cause an image to have poor quality. However,
image quality is a largely ignored issue in traditional pattern recognition
literature. In this paper, we use face detection and recognition as case
studies to show ... | computer science |
30,849 | Quantitative analysis of patch-based fully convolutional neural networks
for tissue segmentation on brain magnetic resonance imaging | cs.CV | Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has
attracted the attention of medical doctors and researchers since variations in
tissue volume help in diagnosing and monitoring neurological diseases. Several
proposals have been designed throughout the years comprising conventional
machine learn... | computer science |
30,850 | Piggyback: Adapting a Single Network to Multiple Tasks by Learning to
Mask Weights | cs.CV | This work presents a method for adapting a single, fixed deep neural network
to multiple tasks without affecting performance on already learned tasks. By
building upon ideas from network quantization and pruning, we learn binary
masks that piggyback on an existing network, or are applied to unmodified
weights of that n... | computer science |
30,851 | How would surround vehicles move? A Unified Framework for Maneuver
Classification and Motion Prediction | cs.CV | Reliable prediction of surround vehicle motion is a critical requirement for
path planning for autonomous vehicles. In this paper we propose a unified
framework for surround vehicle maneuver classification and motion prediction
that exploits multiple cues, namely, the estimated motion of vehicles, an
understanding of t... | computer science |
30,852 | A Foreground Inference Network for Video Surveillance Using Multi-View
Receptive Field | cs.CV | Foreground (FG) pixel labelling plays a vital role in video surveillance.
Recent engineering solutions have attempted to exploit the efficacy of deep
learning (DL) models initially targeted for image classification to deal with
FG pixel labelling. One major drawback of such strategy is the lacking
delineation of visual... | computer science |
30,853 | Structured Inhomogeneous Density Map Learning for Crowd Counting | cs.CV | In this paper, we aim at tackling the problem of crowd counting in extremely
high-density scenes, which contain hundreds, or even thousands of people. We
begin by a comprehensive analysis of the most widely used density map-based
methods, and demonstrate how easily existing methods are affected by the
inhomogeneous den... | computer science |
30,854 | Learning Light Field Reconstruction from a Single Coded Image | cs.CV | Light field imaging is a rich way of representing the 3D world around us.
However, due to limited sensor resolution capturing light field data inherently
poses spatio-angular resolution trade-off. In this paper, we propose a deep
learning based solution to tackle the resolution trade-off. Specifically, we
reconstruct f... | computer science |
30,855 | EnKCF: Ensemble of Kernelized Correlation Filters for High-Speed Object
Tracking | cs.CV | Computer vision technologies are very attractive for practical applications
running on embedded systems. For such an application, it is desirable for the
deployed algorithms to run in high-speed and require no offline training. To
develop a single-target tracking algorithm with these properties, we propose an
ensemble ... | computer science |
30,856 | Boundary-based Image Forgery Detection by Fast Shallow CNN | cs.CV | Image forgery detection is the task of detecting and localizing forged parts
in tampered images. Previous works mostly focus on high resolution images using
traces of resampling features, demosaicing features or sharpness of edges.
However, a good detection method should also be applicable to low resolution
images beca... | computer science |
30,857 | End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars
with Visual Perception | cs.CV | Convolutional Neural Networks (CNN) have been successfully applied to
autonomous driving tasks, many in an end-to-end manner. Previous end-to-end
steering control methods take an image or an image sequence as the input and
directly predict the steering angle with CNN. Although single task learning on
steering angles ha... | computer science |
30,858 | Multi-pseudo Regularized Label for Generated Samples in Person
Re-Identification | cs.CV | Sufficient training data is normally required to train deeply learned models.
However, the number of pedestrian images per ID in person re-identification
(re-ID) datasets is usually limited, since manually annotations are required
for multiple camera views. To produce more data for training deeply learned
models, gener... | computer science |
30,859 | Denoising Prior Driven Deep Neural Network for Image Restoration | cs.CV | Deep neural networks (DNNs) have shown very promising results for various
image restoration (IR) tasks. However, the design of network architectures
remains a major challenging for achieving further improvements. While most
existing DNN-based methods solve the IR problems by directly mapping low
quality images to desir... | computer science |
30,860 | Deep joint rain and haze removal from single images | cs.CV | Rain removal from a single image is a challenge which has been studied for a
long time. In this paper, a novel convolutional neural network based on wavelet
and dark channel is proposed. On one hand, we think that rain streaks
correspond to high frequency component of the image. Therefore, haar wavelet
transform is a g... | computer science |
30,861 | Decoupled Learning for Conditional Adversarial Networks | cs.CV | Incorporating encoding-decoding nets with adversarial nets has been widely
adopted in image generation tasks. We observe that the state-of-the-art
achievements were obtained by carefully balancing the reconstruction loss and
adversarial loss, and such balance shifts with different network structures,
datasets, and trai... | computer science |
30,862 | Dense Recurrent Neural Networks for Scene Labeling | cs.CV | Recently recurrent neural networks (RNNs) have demonstrated the ability to
improve scene labeling through capturing long-range dependencies among image
units. In this paper, we propose dense RNNs for scene labeling by exploring
various long-range semantic dependencies among image units. In comparison with
existing RNN ... | computer science |
30,863 | Scene recognition with CNNs: objects, scales and dataset bias | cs.CV | Since scenes are composed in part of objects, accurate recognition of scenes
requires knowledge about both scenes and objects. In this paper we address two
related problems: 1) scale induced dataset bias in multi-scale convolutional
neural network (CNN) architectures, and 2) how to combine effectively
scene-centric and... | computer science |
30,864 | MRI Image-to-Image Translation for Cross-Modality Image Registration and
Segmentation | cs.CV | We develop a novel cross-modality generation framework that learns to
generate predicted modalities from given modalities in MR images without real
acquisition. Our proposed method performs image-to-image translation by means
of a deep learning model that leverages conditional generative adversarial
networks (cGANs). O... | computer science |
30,865 | Towards Automated Tuberculosis detection using Deep Learning | cs.CV | Tuberculosis(TB) in India is the world's largest TB epidemic. TB leads to
480,000 deaths every year. Between the years 2006 and 2014, Indian economy lost
US$340 Billion due to TB. This combined with the emergence of drug resistant
bacteria in India makes the problem worse. The government of India has hence
come up with... | computer science |
30,866 | Staff line Removal using Generative Adversarial Networks | cs.CV | Staff line removal is a crucial pre-processing step in Optical Music
Recognition. It is a challenging task to simultaneously reduce the noise and
also retain the quality of music symbol context in ancient degraded music score
images. In this paper we propose a novel approach for staff line removal, based
on Generative ... | computer science |
30,867 | Word Level Font-to-Font Image Translation using Convolutional Recurrent
Generative Adversarial Networks | cs.CV | Conversion of one font to another font is very useful in real life
applications. In this paper, we propose a Convolutional Recurrent Generative
model to solve the word level font transfer problem. Our network is able to
convert the font style of any printed text images from its current font to the
required font. The ne... | computer science |
30,868 | Fluorescence Microscopy Image Segmentation Using Convolutional Neural
Network With Generative Adversarial Networks | cs.CV | Recent advance in fluorescence microscopy enables acquisition of 3D image
volumes with better quality and deeper penetration into tissue. Segmentation is
a required step to characterize and analyze biological structures in the
images. 3D segmentation using deep learning has achieved promising results in
microscopy imag... | computer science |
30,869 | Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder
Network | cs.CV | In this paper, we introduce a novel technique to recover the pen trajectory
of offline characters which is a crucial step for handwritten character
recognition. Generally, online acquisition approach has more advantage than its
offline counterpart as the online technique keeps track of the pen movement.
Hence, pen tip ... | computer science |
30,870 | DiscrimNet: Semi-Supervised Action Recognition from Videos using
Generative Adversarial Networks | cs.CV | We propose an action recognition framework using Gen- erative Adversarial
Networks. Our model involves train- ing a deep convolutional generative
adversarial network (DCGAN) using a large video activity dataset without la-
bel information. Then we use the trained discriminator from the GAN model as an
unsupervised pre-... | computer science |
30,871 | Vehicle Detection in Aerial Images | cs.CV | The detection of vehicles in aerial images is widely applied in many
applications. Comparing with object detection in the ground view images,
vehicle detection in aerial images remains a challenging problem because of
small vehicle size, monotone appearance and the complex background. In this
paper, we propose a novel ... | computer science |
30,872 | Learning to Prune Filters in Convolutional Neural Networks | cs.CV | Many state-of-the-art computer vision algorithms use large scale
convolutional neural networks (CNNs) as basic building blocks. These CNNs are
known for their huge number of parameters, high redundancy in weights, and
tremendous computing resource consumptions. This paper presents a learning
algorithm to simplify and s... | computer science |
30,873 | Numerical Coordinate Regression with Convolutional Neural Networks | cs.CV | We study deep learning approaches to inferring numerical coordinates for
points of interest in an input image. Existing convolutional neural
network-based solutions to this problem either take a heatmap matching approach
or regress to coordinates with a fully connected output layer. Neither of these
approaches is ideal... | computer science |
30,874 | Let's Dance: Learning From Online Dance Videos | cs.CV | In recent years, deep neural network approaches have naturally extended to
the video domain, in their simplest case by aggregating per-frame
classifications as a baseline for action recognition. A majority of the work in
this area extends from the imaging domain, leading to visual-feature heavy
approaches on temporal d... | computer science |
30,875 | Revisiting Video Saliency: A Large-scale Benchmark and a New Model | cs.CV | In this work, we contribute to video saliency research in two ways. First, we
introduce a new benchmark for predicting human eye movements during dynamic
scene free-viewing, which is long-time urged in this field. Our dataset, named
DHF1K (Dynamic Human Fixation), consists of 1K high-quality, elaborately
selected video... | computer science |
30,876 | Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action
Recognition | cs.CV | Dynamics of human body skeletons convey significant information for human
action recognition. Conventional approaches for modeling skeletons usually rely
on hand-crafted parts or traversal rules, thus resulting in limited expressive
power and difficulties of generalization. In this work, we propose a novel
model of dyn... | computer science |
30,877 | Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of
Deep Convolutional Neural Networks | cs.CV | To address memory and computation resource limitations for hardware-oriented
acceleration of deep convolutional neural networks (CNNs), we present a
computation flow, stacked filters stationary flow (SFS), and a corresponding
data encoding format, relative indexed compressed sparse filter format (CSF),
to make the best... | computer science |
30,878 | Survey on Emotional Body Gesture Recognition | cs.CV | Automatic emotion recognition has become a trending research topic in the
past decade. While works based on facial expressions or speech abound,
recognizing affect from body gestures remains a less explored topic. We present
a new comprehensive survey hoping to boost research in the field. We first
introduce emotional ... | computer science |
30,879 | Statistically Motivated Second Order Pooling | cs.CV | Second-order pooling, a.k.a. bilinear pooling, has proven effective for
visual recognition. The recent progress in this area has focused on either
designing normalization techniques for second-order models, or compressing the
second-order representations. However, these two directions have typically been
followed separ... | computer science |
30,880 | Side Information for Face Completion: a Robust PCA Approach | cs.CV | Robust principal component analysis (RPCA) is a powerful method for learning
low-rank feature representation of various visual data. However, for certain
types as well as significant amount of error corruption, it fails to yield
satisfactory results; a drawback that can be alleviated by exploiting
domain-dependent prio... | computer science |
30,881 | DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning | cs.CV | Facial analysis technologies have recently measured up to the capabilities of
expert clinicians in syndrome identification. To date, these technologies could
only identify phenotypes of a few diseases, limiting their role in clinical
settings where hundreds of diagnoses must be considered.
We developed a facial analy... | computer science |
30,882 | ArcFace: Additive Angular Margin Loss for Deep Face Recognition | cs.CV | Convolutional neural networks have significantly boosted the performance of
face recognition in recent years due to its high capacity in learning
discriminative features. To enhance the discriminative power of the Softmax
loss, multiplicative angular margin and additive cosine margin incorporate
angular margin and cosi... | computer science |
30,883 | Estimation of Variance and Spatial Correlation Width for Fine-scale
Measurement Error in Digital Elevation Model | cs.CV | In this paper, we borrow from blind noise parameter estimation (BNPE)
methodology early developed in the image processing field an original and
innovative no-reference approach to estimate Digital Elevation Model (DEM)
vertical error parameters without resorting to a reference DEM. The challenges
associated with the pr... | computer science |
30,884 | Dynamic Graph CNN for Learning on Point Clouds | cs.CV | Point clouds provide a flexible and scalable geometric representation
suitable for countless applications in computer graphics; they also comprise
the raw output of most 3D data acquisition devices. Hence, the design of
intelligent computational models that act directly on point clouds is critical,
especially when effi... | computer science |
30,885 | Feeding Hand-Crafted Features for Enhancing the Performance of
Convolutional Neural Networks | cs.CV | Since the convolutional neural network (CNN) is be- lieved to find right
features for a given problem, the study of hand-crafted features is somewhat
neglected these days. In this paper, we show that finding an appropriate
feature for the given problem may be still important as they can en- hance the
performance of CNN... | computer science |
30,886 | Structured Triplet Learning with POS-tag Guided Attention for Visual
Question Answering | cs.CV | Visual question answering (VQA) is of significant interest due to its
potential to be a strong test of image understanding systems and to probe the
connection between language and vision. Despite much recent progress, general
VQA is far from a solved problem. In this paper, we focus on the VQA
multiple-choice task, and... | computer science |
30,887 | Deep Structured Energy-Based Image Inpainting | cs.CV | In this paper, we propose a structured image inpainting method employing an
energy based model. In order to learn structural relationship between patterns
observed in images and missing regions of the images, we employ an energy-based
structured prediction method. The structural relationship is learned by
minimizing an... | computer science |
30,888 | Near-lossless L-infinity constrained Multi-rate Image Decompression via
Deep Neural Network | cs.CV | Recently a number of CNN-based techniques were proposed to remove image
compression artifacts. As in other restoration applications, these techniques
all learn a mapping from decompressed patches to the original counterparts
under the ubiquitous L2 metric. However, this approach is incapable of
restoring distinctive im... | computer science |
30,889 | Unsupervised learning from videos using temporal coherency deep networks | cs.CV | In this work we address the challenging problem of unsupervised learning from
videos. Existing methods utilize the spatio-temporal continuity in contiguous
video frames as regularization for the learning process. Typically, this
temporal coherence of close frames is used as a free form of annotation,
encouraging the le... | computer science |
30,890 | The challenge of simultaneous object detection and pose estimation: a
comparative study | cs.CV | Detecting objects and estimating their pose remains as one of the major
challenges of the computer vision research community. There exists a compromise
between localizing the objects and estimating their viewpoints. The detector
ideally needs to be view-invariant, while the pose estimation process should be
able to gen... | computer science |
30,891 | When Vehicles See Pedestrians with Phones:A Multi-Cue Framework for
Recognizing Phone-based Activities of Pedestrians | cs.CV | The intelligent vehicle community has devoted considerable efforts to model
driver behavior, and in particular to detect and overcome driver distraction in
an effort to reduce accidents caused by driver negligence. However, as the
domain increasingly shifts towards autonomous and semi-autonomous solutions,
the driver i... | computer science |
30,892 | Personalized Human Activity Recognition Using Convolutional Neural
Networks | cs.CV | A major barrier to the personalized Human Activity Recognition using wearable
sensors is that the performance of the recognition model drops significantly
upon adoption of the system by new users or changes in physical/ behavioral
status of users. Therefore, the model needs to be retrained by collecting new
labeled dat... | computer science |
30,893 | Visual Weather Temperature Prediction | cs.CV | In this paper, we attempt to employ convolutional recurrent neural networks
for weather temperature estimation using only image data. We study ambient
temperature estimation based on deep neural networks in two scenarios a)
estimating temperature of a single outdoor image, and b) predicting temperature
of the last imag... | computer science |
30,894 | Class label autoencoder for zero-shot learning | cs.CV | Existing zero-shot learning (ZSL) methods usually learn a projection function
between a feature space and a semantic embedding space(text or attribute space)
in the training seen classes or testing unseen classes. However, the projection
function cannot be used between the feature space and multi-semantic embedding
spa... | computer science |
30,895 | Abnormal Heartbeat Detection Using Recurrent Neural Networks | cs.CV | The observation and management of cardiac features (using automated cardiac
auscultation) is of significant interest to the healthcare community. In this
work, we propose for the first time the use of recurrent neural networks (RNNs)
for automated cardiac auscultation and detection of abnormal heartbeat
detection. The ... | computer science |
30,896 | Using Deep Autoencoders for Facial Expression Recognition | cs.CV | Feature descriptors involved in image processing are generally manually
chosen and high dimensional in nature. Selecting the most important features is
a very crucial task for systems like facial expression recognition. This paper
investigates the performance of deep autoencoders for feature selection and
dimension red... | computer science |
30,897 | Dual Asymmetric Deep Hashing Learning | cs.CV | Due to the impressive learning power, deep learning has achieved a remarkable
performance in supervised hash function learning. In this paper, we propose a
novel asymmetric supervised deep hashing method to preserve the semantic
structure among different categories and generate the binary codes
simultaneously. Specific... | computer science |
30,898 | Collaborative Large-Scale Dense 3D Reconstruction with Online
Inter-Agent Pose Optimisation | cs.CV | Reconstructing dense, volumetric models of real-world 3D scenes is important
for many tasks, but capturing large scenes can take significant time, and the
risk of transient changes to the scene goes up as the capture time increases.
These are good reasons to want instead to capture several smaller sub-scenes
that can b... | computer science |
30,899 | A Benchmark and Evaluation of Non-Rigid Structure from Motion | cs.CV | Non-Rigid structure from motion (NRSfM), is a long standing and central
problem in computer vision, allowing us to obtain 3D information from multiple
images when the scene is dynamic. A main issue regarding the further
development of this important computer vision topic, is the lack of high
quality data sets. We here ... | computer science |
30,900 | Global and Local Consistent Age Generative Adversarial Networks | cs.CV | Age progression/regression is a challenging task due to the complicated and
non-linear transformation in human aging process. Many researches have shown
that both global and local facial features are essential for face
representation, but previous GAN based methods mainly focused on the global
feature in age synthesis.... | computer science |
30,901 | Understanding Human Behaviors in Crowds by Imitating the Decision-Making
Process | cs.CV | Crowd behavior understanding is crucial yet challenging across a wide range
of applications, since crowd behavior is inherently determined by a sequential
decision-making process based on various factors, such as the pedestrians' own
destinations, interaction with nearby pedestrians and anticipation of upcoming
events.... | computer science |
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