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31,402 | RTSeg: Real-time Semantic Segmentation Comparative Study | cs.CV | Semantic segmentation benefits robotics related applications especially
autonomous driving. Most of the research on semantic segmentation is only on
increasing the accuracy of segmentation models with little attention to
computationally efficient solutions. The few work conducted in this direction
does not provide prin... | computer science |
31,403 | A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation
of Histopathological Images | cs.CV | This paper addresses the task of nuclei segmentation in high-resolution
histopathological images. We propose an auto- matic end-to-end deep neural
network algorithm for segmenta- tion of individual nuclei. A nucleus-boundary
model is introduced to predict nuclei and their boundaries simultaneously using
a fully convolu... | computer science |
31,404 | A framework with updateable joint images re-ranking for Person
Re-identification | cs.CV | Person re-identification plays an important role in realistic video
surveillance with increasing demand for public safety. In this paper, we
propose a novel framework with rules of updating images for person
re-identification in real-world surveillance system. First, Image Pool is
generated by using mean-shift tracking... | computer science |
31,405 | Instance Similarity Deep Hashing for Multi-Label Image Retrieval | cs.CV | Hash coding has been widely used in the approximate nearest neighbor search
for large-scale image retrieval. Recently, many deep hashing methods have been
proposed and shown largely improved performance over traditional
feature-learning-based methods. Most of these methods examine the pairwise
similarity on the semanti... | computer science |
31,406 | Rethinking Feature Distribution for Loss Functions in Image
Classification | cs.CV | We propose a large-margin Gaussian Mixture (L-GM) loss for deep neural
networks in classification tasks. Different from the softmax cross-entropy
loss, our proposal is established on the assumption that the deep features of
the training set follow a Gaussian Mixture distribution. By involving a
classification margin an... | computer science |
31,407 | Learning Effective Binary Visual Representations with Deep Networks | cs.CV | Although traditionally binary visual representations are mainly designed to
reduce computational and storage costs in the image retrieval research, this
paper argues that binary visual representations can be applied to large scale
recognition and detection problems in addition to hashing in retrieval.
Furthermore, the ... | computer science |
31,408 | Robustness of control point configurations for homography and planar
pose estimation | cs.CV | In this paper, we investigate the influence of the spatial configuration of a
number of $n \geq 4$ control points on the accuracy and robustness of space
resection methods, e.g. used by a fiducial marker for pose estimation. We find
robust configurations of control points by minimizing the first order perturbed
solutio... | computer science |
31,409 | Preserving Semantic Relations for Zero-Shot Learning | cs.CV | Zero-shot learning has gained popularity due to its potential to scale
recognition models without requiring additional training data. This is usually
achieved by associating categories with their semantic information like
attributes. However, we believe that the potential offered by this paradigm is
not yet fully explo... | computer science |
31,410 | Leveraging Unlabeled Data for Crowd Counting by Learning to Rank | cs.CV | We propose a novel crowd counting approach that leverages abundantly
available unlabeled crowd imagery in a learning-to-rank framework. To induce a
ranking of cropped images , we use the observation that any sub-image of a
crowded scene image is guaranteed to contain the same number or fewer persons
than the super-imag... | computer science |
31,411 | Domain Adaptive Faster R-CNN for Object Detection in the Wild | cs.CV | Object detection typically assumes that training and test data are drawn from
an identical distribution, which, however, does not always hold in practice.
Such a distribution mismatch will lead to a significant performance drop. In
this work, we aim to improve the cross-domain robustness of object detection.
We tackle ... | computer science |
31,412 | Generalization in Metric Learning: Should the Embedding Layer be the
Embedding Layer? | cs.CV | Many recent works advancing deep learning tend to focus on large scale
setting with the goal of more effective training and better fitting. This goal
might be less applicable to the case of small to medium scale. Studying deep
metric learning under such setting, we reason that better generalization could
be a big contr... | computer science |
31,413 | Analysis of Hand Segmentation in the Wild | cs.CV | A large number of works in egocentric vision have concentrated on action and
object recognition. Detection and segmentation of hands in first person videos,
however, has less been explored. For many applications in this domain, it is
necessary to accurately segment not only hands of the camera wearer but also
the hands... | computer science |
31,414 | Motion deblurring of faces | cs.CV | Face analysis is a core part of computer vision, in which remarkable progress
has been observed in the past decades. Current methods achieve recognition and
tracking with invariance to fundamental modes of variation such as
illumination, 3D pose, expressions. Notwithstanding, a much less standing mode
of variation is m... | computer science |
31,415 | Adversarial Training for Adverse Conditions: Robust Metric Localisation
using Appearance Transfer | cs.CV | We present a method of improving visual place recognition and metric
localisation under very strong appear- ance change. We learn an invertable
generator that can trans- form the conditions of images, e.g. from day to
night, summer to winter etc. This image transforming filter is explicitly
designed to aid and abet fea... | computer science |
31,416 | Deep Semantic Face Deblurring | cs.CV | In this paper, we present an effective and efficient face deblurring
algorithm by exploiting semantic cues via deep convolutional neural networks
(CNNs). As face images are highly structured and share several key semantic
components (e.g., eyes and mouths), the semantic information of a face provides
a strong prior for... | computer science |
31,417 | Tracking by Prediction: A Deep Generative Model for Mutli-Person
localisation and Tracking | cs.CV | Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking... | computer science |
31,418 | Indoor Scene Understanding in 2.5/3D: A Survey | cs.CV | With the availability of low-cost and compact 2.5/3D visual sensing devices,
computer vision community is experiencing a growing interest in visual scene
understanding. This survey paper provides a comprehensive background to this
research topic. We begin with a historical perspective, followed by popular 3D
data repre... | computer science |
31,419 | Task Specific Visual Saliency Prediction with Memory Augmented
Conditional Generative Adversarial Networks | cs.CV | Visual saliency patterns are the result of a variety of factors aside from
the image being parsed, however existing approaches have ignored these. To
address this limitation, we propose a novel saliency estimation model which
leverages the semantic modelling power of conditional generative adversarial
networks together... | computer science |
31,420 | Learning a Discriminative Prior for Blind Image Deblurring | cs.CV | We present an effective blind image deblurring method based on a data-driven
discriminative prior.Our work is motivated by the fact that a good image prior
should favor clear images over blurred images.In this work, we formulate the
image prior as a binary classifier which can be achieved by a deep
convolutional neural... | computer science |
31,421 | Review of Visual Saliency Detection with Comprehensive Information | cs.CV | Visual saliency detection model simulates the human visual system to perceive
the scene, and has been widely used in many vision tasks. With the acquisition
technology development, more comprehensive information, such as depth cue,
inter-image correspondence, or temporal relationship, is available to extend
image salie... | computer science |
31,422 | Cross-View Image Synthesis using Conditional GANs | cs.CV | Learning to generate natural scenes has always been a challenging task in
computer vision. It is even more painstaking when the generation is conditioned
on images with drastically different views. This is mainly because
understanding, corresponding, and transforming appearance and semantic
information across views is ... | computer science |
31,423 | Fusing Hierarchical Convolutional Features for Human Body Segmentation
and Clothing Fashion Classification | cs.CV | The clothing fashion reflects the common aesthetics that people share with
each other in dressing. To recognize the fashion time of a clothing is
meaningful for both an individual and the industry. In this paper, under the
assumption that the clothing fashion changes year by year, the fashion-time
recognition problem i... | computer science |
31,424 | Robust Landmark Detection for Alignment of Mouse Brain Section Images | cs.CV | Brightfield and fluorescent imaging of whole brain sections are funda- mental
tools of research in mouse brain study. As sectioning and imaging become more
efficient, there is an increasing need to automate the post-processing of sec-
tions for alignment and three dimensional visualization. There is a further
need to f... | computer science |
31,425 | Single Shot TextSpotter with Explicit Alignment and Attention | cs.CV | Text detection and recognition in natural images have long been considered as
two separate tasks that are processed sequentially. Training of two tasks in a
unified framework is non-trivial due to significant dif- ferences in
optimisation difficulties. In this work, we present a conceptually simple yet
efficient framew... | computer science |
31,426 | Breast Tumor Classification Based on Decision Information Genes and
Inverse Projection Sparse Representation | cs.CV | Microarray gene expression data-based breast tumor classification is an
active and challenging issue. In this paper, a robust breast tumor recognition
framework is presented based on considering reducing clinical misdiagnosis rate
and exploiting available information in existing samples. A wrapper gene
selection method... | computer science |
31,427 | Intentions of Vulnerable Road Users - Detection and Forecasting by Means
of Machine Learning | cs.CV | Avoiding collisions with vulnerable road users (VRUs) using sensor-based
early recognition of critical situations is one of the manifold opportunities
provided by the current development in the field of intelligent vehicles. As
especially pedestrians and cyclists are very agile and have a variety of
movement options, m... | computer science |
31,428 | Local Kernels that Approximate Bayesian Regularization and Proximal
Operators | cs.CV | In this work, we broadly connect kernel-based filtering (e.g. approaches such
as the bilateral filters and nonlocal means, but also many more) with general
variational formulations of Bayesian regularized least squares, and the related
concept of proximal operators. The latter set of variational/Bayesian/proximal
formu... | computer science |
31,429 | A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination
Algorithms for Buried Threat Detection in Ground Penetrating Radar | cs.CV | In this paper we consider the development of algorithms for the automatic
detection of buried threats using ground penetrating radar (GPR) measurements.
GPR is one of the most studied and successful modalities for automatic buried
threat detection (BTD), and a large variety of BTD algorithms have been
proposed for it. ... | computer science |
31,430 | Driving Scene Perception Network: Real-time Joint Detection, Depth
Estimation and Semantic Segmentation | cs.CV | As the demand for enabling high-level autonomous driving has increased in
recent years and visual perception is one of the critical features to enable
fully autonomous driving, in this paper, we introduce an efficient approach for
simultaneous object detection, depth estimation and pixel-level semantic
segmentation usi... | computer science |
31,431 | ShuffleSeg: Real-time Semantic Segmentation Network | cs.CV | Real-time semantic segmentation is of significant importance for mobile and
robotics related applications. We propose a computationally efficient
segmentation network which we term as ShuffleSeg. The proposed architecture is
based on grouped convolution and channel shuffling in its encoder for improving
the performance... | computer science |
31,432 | Sample-Relaxed Two-Dimensional Color Principal Component Analysis for
Face Recognition and Image Reconstruction | cs.CV | A sample-relaxed two-dimensional color principal component analysis
(SR-2DCPCA) approach is presented for face recognition and image reconstruction
based on quaternion models. A relaxation vector is automatically generated
according to the variances of training color face images with the same label. A
sample-relaxed, l... | computer science |
31,433 | A Deep Learning Approach for Pose Estimation from Volumetric OCT Data | cs.CV | Tracking the pose of instruments is a central problem in image-guided
surgery. For microscopic scenarios, optical coherence tomography (OCT) is
increasingly used as an imaging modality. OCT is suitable for accurate pose
estimation due to its micrometer range resolution and volumetric field of view.
However, OCT image p... | computer science |
31,434 | Webly Supervised Learning with Category-level Semantic Information | cs.CV | As tons of photos are being uploaded to public websites (e.g., Flickr, Bing,
and Google) every day, learning from web data has become an increasingly
popular research direction because of freely available web resources, which is
also referred to as webly supervised learning. Nevertheless, the performance
gap between we... | computer science |
31,435 | Knowledge Aided Consistency for Weakly Supervised Phrase Grounding | cs.CV | Given a natural language query, a phrase grounding system aims to localize
mentioned objects in an image. In weakly supervised scenario, mapping between
image regions (i.e., proposals) and language is not available in the training
set. Previous methods address this deficiency by training a grounding system
via learning... | computer science |
31,436 | Unsupervised Learning of Monocular Depth Estimation and Visual Odometry
with Deep Feature Reconstruction | cs.CV | Despite learning based methods showing promising results in single view depth
estimation and visual odometry, most existing approaches treat the tasks in a
supervised manner. Recent approaches to single view depth estimation explore
the possibility of learning without full supervision via minimizing photometric
error. ... | computer science |
31,437 | Cubic Range Error Model for Stereo Vision with Illuminators | cs.CV | Use of low-cost depth sensors, such as a stereo camera setup with
illuminators, is of particular interest for numerous applications ranging from
robotics and transportation to mixed and augmented reality. The ability to
quantify noise is crucial for these applications, e.g., when the sensor is used
for map generation o... | computer science |
31,438 | Deeply supervised neural network with short connections for retinal
vessel segmentation | cs.CV | The condition of vessel of the human eye is a fundamental factor for the
diagnosis of ophthalmological diseases. Vessel segmentation in fundus image is
a challenging task due to low contrast, the presence of microaneurysms and
hemorrhages. In this paper, we present a multi-scale and multi-level deeply
supervised convol... | computer science |
31,439 | Deep Dictionary Learning: A PARametric NETwork Approach | cs.CV | Deep dictionary learning seeks multiple dictionaries at different image
scales to capture complementary coherent characteristics. We propose a method
for learning a hierarchy of synthesis dictionaries with an image classification
goal. The dictionaries and classification parameters are trained by a
classification objec... | computer science |
31,440 | Cascade context encoder for improved inpainting | cs.CV | In this paper, we analyze if cascade usage of the context encoder with
increasing input can improve the results of the inpainting. For this purpose,
we train context encoder for 64x64 pixels images in a standard way and use its
resized output to fill in the missing input region of the 128x128 context
encoder, both in t... | computer science |
31,441 | Multiple Instance Choquet Integral Classifier Fusion and Regression for
Remote Sensing Applications | cs.CV | In classifier (or regression) fusion the aim is to combine the outputs of
several algorithms to boost overall performance. Standard supervised fusion
algorithms often require accurate and precise training labels. However,
accurate labels may be difficult to obtain in many remote sensing applications.
This paper propose... | computer science |
31,442 | Two-Stage Convolutional Neural Network for Breast Cancer Histology Image
Classification | cs.CV | This paper explores the problem of breast tissue classification of microscopy
images. Based on the predominant cancer type the goal is to classify images
into four categories of normal, benign, in situ carcinoma, and invasive
carcinoma. Given a suitable training dataset, we utilize deep learning
techniques to address t... | computer science |
31,443 | Full Reference Objective Quality Assessment for Reconstructed Background
Images | cs.CV | With an increased interest in applications that require a clean background
image, such as video surveillance, object tracking, street view imaging and
location-based services on web-based maps, multiple algorithms have been
developed to reconstruct a background image from cluttered scenes.
Traditionally, statistical me... | computer science |
31,444 | Style Aggregated Network for Facial Landmark Detection | cs.CV | Recent advances in facial landmark detection achieve success by learning
discriminative features from rich deformation of face shapes and poses. Besides
the variance of faces themselves, the intrinsic variance of image styles, e.g.,
grayscale vs. color images, light vs. dark, intense vs. dull, and so on, has
constantly... | computer science |
31,445 | Innovative Texture Database Collecting Approach and Feature Extraction
Method based on Combination of Gray Tone Difference Matrixes, Local Binary
Patterns,and K-means Clustering | cs.CV | Texture analysis and classification are some of the problems which have been
paid much attention by image processing scientists since late 80s. If texture
analysis is done accurately, it can be used in many cases such as object
tracking, visual pattern recognition, and face recognition.Since now, so many
methods are of... | computer science |
31,446 | Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise
Loss | cs.CV | Deep supervised hashing has emerged as an influential solution to large-scale
semantic image retrieval problems in computer vision. In the light of recent
progress, convolutional neural network based hashing methods typically seek
pair-wise or triplet labels to conduct the similarity preserving learning.
However, compl... | computer science |
31,447 | Video Object Segmentation with Joint Re-identification and
Attention-Aware Mask Propagation | cs.CV | The problem of video object segmentation can become extremely challenging
when multiple instances co-exist. While each instance may exhibit large scale
and pose variations, the problem is compounded when instances occlude each
other causing failures in tracking. In this study, we formulate a deep
recurrent network that... | computer science |
31,448 | SO-Net: Self-Organizing Network for Point Cloud Analysis | cs.CV | This paper presents SO-Net, a permutation invariant architecture for deep
learning with orderless point clouds. The SO-Net models the spatial
distribution of point cloud by building a Self-Organizing Map (SOM). Based on
the SOM, SO-Net performs hierarchical feature extraction on individual points
and SOM nodes, and ult... | computer science |
31,449 | In-depth Assessment of an Interactive Graph-based Approach for the
Segmentation for Pancreatic Metastasis in Ultrasound Acquisitions of the
Liver with two Specialists in Internal Medicine | cs.CV | The manual outlining of hepatic metastasis in (US) ultrasound acquisitions
from patients suffering from pancreatic cancer is common practice. However,
such pure manual measurements are often very time consuming, and the results
repeatedly differ between the raters. In this contribution, we study the
in-depth assessment... | computer science |
31,450 | Replication study: Development and validation of deep learning algorithm
for detection of diabetic retinopathy in retinal fundus photographs | cs.CV | We have replicated some experiments in 'Development and validation of a deep
learning algorithm for detection of diabetic retinopathy in retinal fundus
photographs' that was published in JAMA 2016; 316(22). We re-implemented the
methods since the source code is not available.
The original study used fundus images fro... | computer science |
31,451 | idtracker.ai: Tracking all individuals in large collectives of unmarked
animals | cs.CV | Our understanding of collective animal behavior is limited by our ability to
track each of the individuals. We describe an algorithm and software,
idtracker.ai, that extracts from video all trajectories with correct identities
at a high accuracy for collectives of up to 100 individuals. It uses two deep
networks, one d... | computer science |
31,452 | Beyond Gröbner Bases: Basis Selection for Minimal Solvers | cs.CV | Many computer vision applications require robust estimation of the underlying
geometry, in terms of camera motion and 3D structure of the scene. These robust
methods often rely on running minimal solvers in a RANSAC framework. In this
paper we show how we can make polynomial solvers based on the action matrix
method fa... | computer science |
31,453 | Discriminability objective for training descriptive captions | cs.CV | One property that remains lacking in image captions generated by contemporary
methods is discriminability: being able to tell two images apart given the
caption for one of them. We propose a way to improve this aspect of caption
generation. By incorporating into the captioning training objective a loss
component direct... | computer science |
31,454 | Dissimilarity-based representation for radiomics applications | cs.CV | Radiomics is a term which refers to the analysis of the large amount of
quantitative tumor features extracted from medical images to find useful
predictive, diagnostic or prognostic information. Many recent studies have
proved that radiomics can offer a lot of useful information that physicians
cannot extract from the ... | computer science |
31,455 | An Introduction to Image Synthesis with Generative Adversarial Nets | cs.CV | There has been a drastic growth of research in Generative Adversarial Nets
(GANs) in the past few years. Proposed in 2014, GAN has been applied to various
applications such as computer vision and natural language processing, and
achieves impressive performance. Among the many applications of GAN, image
synthesis is the... | computer science |
31,456 | Correction by Projection: Denoising Images with Generative Adversarial
Networks | cs.CV | Generative adversarial networks (GANs) transform low-dimensional latent
vectors into visually plausible images. If the real dataset contains only clean
images, then ostensibly, the manifold learned by the GAN should contain only
clean images. In this paper, we propose to denoise corrupted images by finding
the nearest ... | computer science |
31,457 | Event-based Moving Object Detection and Tracking | cs.CV | Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are
ideally suited for real-time motion analysis. The unique properties encompassed
in the readings of such sensors provide high temporal resolution, superior
sensitivity to light and low latency. These properties provide the grounds to
estimate motio... | computer science |
31,458 | Target Driven Instance Detection | cs.CV | While state-of-the-art general object detectors are getting better and
better, there are not many systems specifically designed to take advantage of
the instance detection problem. For many applications, such as household
robotics, a system may need to recognize a few very specific instances at a
time. Speed can be cri... | computer science |
31,459 | Learning to Maintain Natural Image Statistics | cs.CV | Maintaining natural image statistics is a crucial factor in restoration and
generation of realistic looking images. When training CNNs, photorealism is
usually attempted by adversarial training (GAN), that pushes the output images
to lie on the manifold of natural images. GANs are very powerful, but not
perfect. They a... | computer science |
31,460 | TOM-Net: Learning Transparent Object Matting from a Single Image | cs.CV | This paper addresses the problem of transparent object matting. Existing
image matting approaches for transparent objects often require tedious
capturing procedures and long processing time, which limit their practical use.
In this paper, we first formulate transparent object matting as a refractive
flow estimation pro... | computer science |
31,461 | Dynamic Vision Sensors for Human Activity Recognition | cs.CV | Unlike conventional cameras which capture video at a fixed frame rate,
Dynamic Vision Sensors (DVS) record only changes in pixel intensity values. The
output of DVS is simply a stream of discrete ON/OFF events based on the
polarity of change in its pixel values. DVS has many attractive features such
as low power consum... | computer science |
31,462 | Multimodal Recurrent Neural Networks with Information Transfer Layers
for Indoor Scene Labeling | cs.CV | This paper proposes a new method called Multimodal RNNs for RGB-D scene
semantic segmentation. It is optimized to classify image pixels given two input
sources: RGB color channels and Depth maps. It simultaneously performs training
of two recurrent neural networks (RNNs) that are crossly connected through
information t... | computer science |
31,463 | Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid
Coding Micro-Texture Features | cs.CV | Robust features are of vital importance to face spoofing detection, because
various situations make feature space extremely complicated to partition. Thus
in this paper, two novel and robust features for anti-spoofing are proposed.
The first one is a binocular camera based depth feature called Template Face
Matched Bin... | computer science |
31,464 | Video Based Reconstruction of 3D People Models | cs.CV | This paper describes how to obtain accurate 3D body models and texture of
arbitrary people from a single, monocular video in which a person is moving.
Based on a parametric body model, we present a robust processing pipeline
achieving 3D model fits with 5mm accuracy also for clothed people. Our main
contribution is a m... | computer science |
31,465 | Learning Monocular 3D Human Pose Estimation from Multi-view Images | cs.CV | Accurate 3D human pose estimation from single images is possible with
sophisticated deep-net architectures that have been trained on very large
datasets. However, this still leaves open the problem of capturing motions for
which no such database exists. Manual annotation is tedious, slow, and
error-prone. In this paper... | computer science |
31,466 | Low Rank Variation Dictionary and Inverse Projection Group Sparse
Representation Model for Breast Tumor Classification | cs.CV | Sparse representation classification achieves good results by addressing
recognition problem with sufficient training samples per subject. However, SRC
performs not very well for small sample data. In this paper, an
inverse-projection group sparse representation model is presented for breast
tumor classification, which... | computer science |
31,467 | A Learning-Based Visual Saliency Fusion Model for High Dynamic Range
Video (LBVS-HDR) | cs.CV | Saliency prediction for Standard Dynamic Range (SDR) videos has been well
explored in the last decade. However, limited studies are available on High
Dynamic Range (HDR) Visual Attention Models (VAMs). Considering that the
characteristic of HDR content in terms of dynamic range and color gamut is
quite different than t... | computer science |
31,468 | Independently Recurrent Neural Network (IndRNN): Building A Longer and
Deeper RNN | cs.CV | Recurrent neural networks (RNNs) have been widely used for processing
sequential data. However, RNNs are commonly difficult to train due to the
well-known gradient vanishing and exploding problems and hard to learn
long-term patterns. Long short-term memory (LSTM) and gated recurrent unit
(GRU) were developed to addres... | computer science |
31,469 | 3D Video Quality Assessment | cs.CV | A key factor in designing 3D systems is to understand how different visual
cues and distortions affect the perceptual quality of 3D video. The ultimate
way to assess video quality is through subjective tests. However, subjective
evaluation is time consuming, expensive, and in most cases not even possible.
An alternativ... | computer science |
31,470 | Resource aware design of a deep convolutional-recurrent neural network
for speech recognition through audio-visual sensor fusion | cs.CV | Today's Automatic Speech Recognition systems only rely on acoustic signals
and often don't perform well under noisy conditions. Performing multi-modal
speech recognition - processing acoustic speech signals and lip-reading video
simultaneously - significantly enhances the performance of such systems,
especially in nois... | computer science |
31,471 | A Learning-Based Visual Saliency Prediction Model for Stereoscopic 3D
Video (LBVS-3D) | cs.CV | Over the past decade, many computational saliency prediction models have been
proposed for 2D images and videos. Considering that the human visual system has
evolved in a natural 3D environment, it is only natural to want to design
visual attention models for 3D content. Existing monocular saliency models are
not able ... | computer science |
31,472 | Expert identification of visual primitives used by CNNs during mammogram
classification | cs.CV | This work interprets the internal representations of deep neural networks
trained for classification of diseased tissue in 2D mammograms. We propose an
expert-in-the-loop interpretation method to label the behavior of internal
units in convolutional neural networks (CNNs). Expert radiologists identify
that the visual p... | computer science |
31,473 | Using Convolutional Neural Networks for Determining Reticulocyte
Percentage in Cats | cs.CV | Recent advances in artificial intelligence (AI), specifically in computer
vision (CV) and deep learning (DL), have created opportunities for novel
systems in many fields. In the last few years, deep learning applications have
demonstrated impressive results not only in fields such as autonomous driving
and robotics, bu... | computer science |
31,474 | Quantization of Fully Convolutional Networks for Accurate Biomedical
Image Segmentation | cs.CV | With pervasive applications of medical imaging in health-care, biomedical
image segmentation plays a central role in quantitative analysis, clinical
diagno- sis, and medical intervention. Since manual anno- tation su ers limited
reproducibility, arduous e orts, and excessive time, automatic segmentation is
desired to p... | computer science |
31,475 | Automatic Pixelwise Object Labeling for Aerial Imagery Using Stacked
U-Nets | cs.CV | Automation of objects labeling in aerial imagery is a computer vision task
with numerous practical applications. Fields like energy exploration require an
automated method to process a continuous stream of imagery on a daily basis. In
this paper we propose a pipeline to tackle this problem using a stack of
convolutiona... | computer science |
31,476 | A Framework for Video-Driven Crowd Synthesis | cs.CV | We present a framework for video-driven crowd synthesis. Motion vectors
extracted from input crowd video are processed to compute global motion paths.
These paths encode the dominant motions observed in the input video. These
paths are then fed into a behavior-based crowd simulation framework, which is
responsible for ... | computer science |
31,477 | LCANet: End-to-End Lipreading with Cascaded Attention-CTC | cs.CV | Machine lipreading is a special type of automatic speech recognition (ASR)
which transcribes human speech by visually interpreting the movement of related
face regions including lips, face, and tongue. Recently, deep neural network
based lipreading methods show great potential and have exceeded the accuracy of
experien... | computer science |
31,478 | Revisiting Salient Object Detection: Simultaneous Detection, Ranking,
and Subitizing of Multiple Salient Objects | cs.CV | Salient object detection is a problem that has been considered in detail and
many solutions proposed. In this paper, we argue that work to date has
addressed a problem that is relatively ill-posed. Specifically, there is not
universal agreement about what constitutes a salient object when multiple
observers are queried... | computer science |
31,479 | Topology guaranteed segmentation of the human retina from OCT using
convolutional neural networks | cs.CV | Optical coherence tomography (OCT) is a noninvasive imaging modality which
can be used to obtain depth images of the retina. The changing layer
thicknesses can thus be quantified by analyzing these OCT images, moreover
these changes have been shown to correlate with disease progression in multiple
sclerosis. Recent aut... | computer science |
31,480 | Adversarial Data Programming: Using GANs to Relax the Bottleneck of
Curated Labeled Data | cs.CV | Paucity of large curated hand-labeled training data for every
domain-of-interest forms a major bottleneck in the deployment of machine
learning models in computer vision and other fields. Recent work (Data
Programming) has shown how distant supervision signals in the form of labeling
functions can be used to obtain lab... | computer science |
31,481 | EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo
Matching | cs.CV | Recently convolutional neural network (CNN) promotes the development of
stereo matching greatly. Especially those end-to-end stereo methods achieve
best performance. However less attention is paid on encoding context
information, simplifying two-stage disparity learning pipeline and improving
details in disparity maps.... | computer science |
31,482 | Combining Multi-level Contexts of Superpixel using Convolutional Neural
Networks to perform Natural Scene Labeling | cs.CV | Modern deep learning algorithms have triggered various image segmentation
approaches. However most of them deal with pixel based segmentation. However,
superpixels provide a certain degree of contextual information while reducing
computation cost. In our approach, we have performed superpixel level semantic
segmentatio... | computer science |
31,483 | LivDet 2017 Fingerprint Liveness Detection Competition 2017 | cs.CV | Fingerprint Presentation Attack Detection (FPAD) deals with distinguishing
images coming from artificial replicas of the fingerprint characteristic, made
up of materials like silicone, gelatine or latex, and images coming from alive
fingerprints. Images are captured by modern scanners, typically relying on
solid-state ... | computer science |
31,484 | Deep Image Demosaicking using a Cascade of Convolutional Residual
Denoising Networks | cs.CV | Demosaicking and denoising are among the most crucial steps of modern digital
camera pipelines. Meanwhile, joint image denoising-demosaicking is a highly
ill-posed inverse problem where at-least two-thirds of the information are
missing and the rest are corrupted by noise. This poses a great challenge in
obtaining mean... | computer science |
31,485 | Face-MagNet: Magnifying Feature Maps to Detect Small Faces | cs.CV | In this paper, we introduce the Face Magnifier Network (Face-MageNet), a face
detector based on the Faster-RCNN framework which enables the flow of
discriminative information of small scale faces to the classifier without any
skip or residual connections. To achieve this, Face-MagNet deploys a set of
ConvTranspose, als... | computer science |
31,486 | Rotation-Sensitive Regression for Oriented Scene Text Detection | cs.CV | Text in natural images is of arbitrary orientations, requiring detection in
terms of oriented bounding boxes. Normally, a multi-oriented text detector
often involves two key tasks: 1) text presence detection, which is a
classification problem disregarding text orientation; 2) oriented bounding box
regression, which con... | computer science |
31,487 | On the Ambiguity of Registration Uncertainty | cs.CV | Estimating the uncertainty in image registration is an area of current
research that is aimed at providing information that will enable surgeons to
assess the operative risk based on registered image data and the estimated
registration uncertainty. If they receive inaccurately calculated registration
uncertainty and mi... | computer science |
31,488 | Transparency by Design: Closing the Gap Between Performance and
Interpretability in Visual Reasoning | cs.CV | Visual question answering requires high-order reasoning about an image, which
is a fundamental capability needed by machine systems to follow complex
directives. Recently, modular networks have been shown to be an effective
framework for performing visual reasoning tasks. While modular networks were
initially designed ... | computer science |
31,489 | Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian
Detection | cs.CV | Multispectral images of color-thermal pairs have shown more effective than a
single color channel for pedestrian detection, especially under challenging
illumination conditions. However, there is still a lack of studies on how to
fuse the two modalities effectively. In this paper, we deeply compare six
different convol... | computer science |
31,490 | Image Colorization with Generative Adversarial Networks | cs.CV | Over the last decade, the process of automatic colorization had been studied
thoroughly due to its vast application such as colorization of grayscale images
and restoration of aged and/or degraded images. This problem is highly
ill-posed due to the extremely large degrees of freedom during the assignment
of color infor... | computer science |
31,491 | An application of cascaded 3D fully convolutional networks for medical
image segmentation | cs.CV | Recent advances in 3D fully convolutional networks (FCN) have made it
feasible to produce dense voxel-wise predictions of volumetric images. In this
work, we show that a multi-class 3D FCN trained on manually labeled CT scans of
several anatomical structures (ranging from the large organs to thin vessels)
can achieve c... | computer science |
31,492 | Computer-aided diagnosis of lung carcinoma using deep learning - a pilot
study | cs.CV | Aim: Early detection and correct diagnosis of lung cancer are the most
important steps in improving patient outcome. This study aims to assess which
deep learning models perform best in lung cancer diagnosis. Methods: Non-small
cell lung carcinoma and small cell lung carcinoma biopsy specimens were
consecutively obtain... | computer science |
31,493 | Improving Object Counting with Heatmap Regulation | cs.CV | In this paper, we propose a simple and effective way to improve one-look
regression models for object counting from images. We use class activation map
visualizations to illustrate the drawbacks of learning a pure one-look
regression model for a counting task. Based on these insights, we enhance
one-look regression cou... | computer science |
31,494 | Unpaired Image Captioning by Language Pivoting | cs.CV | Image captioning is a multimodal task involving computer vision and natural
language processing, where the goal is to learn a mapping from the image to its
natural language description. In general, the mapping function is learned from
a training set of image-caption pairs. However, for some language, large scale
image-... | computer science |
31,495 | Self-Supervised Monocular Image Depth Learning and Confidence Estimation | cs.CV | Convolutional Neural Networks (CNNs) need large amounts of data with ground
truth annotation, which is a challenging problem that has limited the
development and fast deployment of CNNs for many computer vision tasks. We
propose a novel framework for depth estimation from monocular images with
corresponding confidence ... | computer science |
31,496 | Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild | cs.CV | This paper investigates the evaluation of dense 3D face reconstruction from a
single 2D image in the wild. To this end, we organise a competition that
provides a new benchmark dataset that contains 2000 2D facial images of 135
subjects as well as their 3D ground truth face scans. In contrast to previous
competitions or... | computer science |
31,497 | Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction | cs.CV | Mixed Reality (MR) is a powerful interactive technology that yields new types
of user experience. We present a semantic based interactive MR framework that
exceeds the current geometry level approaches, a step change in generating
high-level context-aware interactions. Our key insight is to build semantic
understanding... | computer science |
31,498 | Object Detection in Video with Spatiotemporal Sampling Networks | cs.CV | We propose a Spatiotemporal Sampling Network (STSN) that uses deformable
convolutions across time for object detection in videos. Our STSN performs
object detection in a video frame by learning to spatially sample features from
the adjacent frames. This naturally renders the approach robust to occlusion or
motion blur ... | computer science |
31,499 | Facelet-Bank for Fast Portrait Manipulation | cs.CV | Digital face manipulation has become a popular and fascinating way to touch
images due to the prevalence of smart phones and social networks. With a wide
variety of user preferences, facial expressions and accessories, a general and
flexible model is necessary to accommodate different types of facial editing.
In this p... | computer science |
31,500 | Deep Adaptive Attention for Joint Facial Action Unit Detection and Face
Alignment | cs.CV | Facial action unit (AU) detection and face alignment are two highly
correlated tasks since facial landmarks can provide precise AU locations to
facilitate the extraction of meaningful local features for AU detection. Most
existing AU detection works often treat face alignment as a preprocessing and
handle the two tasks... | computer science |
31,501 | Fast End-to-End Trainable Guided Filter | cs.CV | Image processing and pixel-wise dense prediction have been advanced by
harnessing the capabilities of deep learning. One central issue of deep
learning is the limited capacity to handle joint upsampling. We present a deep
learning building block for joint upsampling, namely guided filtering layer.
This layer aims at ef... | computer science |
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