Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
28,802 | Deep Feature Learning via Structured Graph Laplacian Embedding for
Person Re-Identification | cs.CV | Learning the distance metric between pairs of examples is of great importance
for visual recognition, especially for person re-identification (Re-Id).
Recently, the contrastive and triplet loss are proposed to enhance the
discriminative power of the deeply learned features, and have achieved
remarkable success. As can ... | computer science |
28,803 | Graph-Theoretic Spatiotemporal Context Modeling for Video Saliency
Detection | cs.CV | As an important and challenging problem in computer vision, video saliency
detection is typically cast as a spatiotemporal context modeling problem over
consecutive frames. As a result, a key issue in video saliency detection is how
to effectively capture the intrinsical properties of atomic video structures as
well as... | computer science |
28,804 | Detecting Semantic Parts on Partially Occluded Objects | cs.CV | In this paper, we address the task of detecting semantic parts on partially
occluded objects. We consider a scenario where the model is trained using
non-occluded images but tested on occluded images. The motivation is that there
are infinite number of occlusion patterns in real world, which cannot be fully
covered in ... | computer science |
28,805 | Multiple-Kernel Local-Patch Descriptor | cs.CV | We propose a multiple-kernel local-patch descriptor based on efficient match
kernels of patch gradients. It combines two parametrizations of gradient
position and direction, each parametrization provides robustness to a different
type of patch miss-registration: polar parametrization for noise in the patch
dominant ori... | computer science |
28,806 | Improving Robustness of Feature Representations to Image Deformations
using Powered Convolution in CNNs | cs.CV | In this work, we address the problem of improvement of robustness of feature
representations learned using convolutional neural networks (CNNs) to image
deformation. We argue that higher moment statistics of feature distributions
could be shifted due to image deformations, and the shift leads to degrade of
performance ... | computer science |
28,807 | ssEMnet: Serial-section Electron Microscopy Image Registration using a
Spatial Transformer Network with Learned Features | cs.CV | The alignment of serial-section electron microscopy (ssEM) images is critical
for efforts in neuroscience that seek to reconstruct neuronal circuits.
However, each ssEM plane contains densely packed structures that vary from one
section to the next, which makes matching features across images a challenge.
Advances in d... | computer science |
28,808 | Motion-Appearance Interactive Encoding for Object Segmentation in
Unconstrained Videos | cs.CV | We present a novel method of integrating motion and appearance cues for
foreground object segmentation in unconstrained videos. Unlike conventional
methods encoding motion and appearance patterns individually, our method puts
particular emphasis on their mutual assistance. Specifically, we propose using
an interactivel... | computer science |
28,809 | Analyzing First-Person Stories Based on Socializing, Eating and
Sedentary Patterns | cs.CV | First-person stories can be analyzed by means of egocentric pictures acquired
throughout the whole active day with wearable cameras. This manuscript presents
an egocentric dataset with more than 45,000 pictures from four people in
different environments such as working or studying. All the images were
manually labeled ... | computer science |
28,810 | Spatiotemporal Modeling for Crowd Counting in Videos | cs.CV | Region of Interest (ROI) crowd counting can be formulated as a regression
problem of learning a mapping from an image or a video frame to a crowd density
map. Recently, convolutional neural network (CNN) models have achieved
promising results for crowd counting. However, even when dealing with video
data, CNN-based met... | computer science |
28,811 | Enhancing Convolutional Neural Networks for Face Recognition with
Occlusion Maps and Batch Triplet Loss | cs.CV | Despite the recent success of convolutional neural networks for computer
vision applications, unconstrained face recognition remains a challenge. In
this work, we make two contributions to the field. Firstly, we consider the
problem of face recognition with partial occlusions and show how current
approaches might suffe... | computer science |
28,812 | Residual Conv-Deconv Grid Network for Semantic Segmentation | cs.CV | This paper presents GridNet, a new Convolutional Neural Network (CNN)
architecture for semantic image segmentation (full scene labelling). Classical
neural networks are implemented as one stream from the input to the output with
subsampling operators applied in the stream in order to reduce the feature maps
size and to... | computer science |
28,813 | Bottom-Up and Top-Down Attention for Image Captioning and Visual
Question Answering | cs.CV | Top-down visual attention mechanisms have been used extensively in image
captioning and visual question answering (VQA) to enable deeper image
understanding through fine-grained analysis and even multiple steps of
reasoning. In this work, we propose a combined bottom-up and top-down attention
mechanism that enables att... | computer science |
28,814 | Automatic Liver Segmentation Using an Adversarial Image-to-Image Network | cs.CV | Automatic liver segmentation in 3D medical images is essential in many
clinical applications, such as pathological diagnosis of hepatic diseases,
surgical planning, and postoperative assessment. However, it is still a very
challenging task due to the complex background, fuzzy boundary, and various
appearance of liver. ... | computer science |
28,815 | Relative Depth Order Estimation Using Multi-scale Densely Connected
Convolutional Networks | cs.CV | We study the problem of estimating the relative depth order of point pairs in
a monocular image. Recent advances mainly focus on using deep convolutional
neural networks (DCNNs) to learn and infer the ordinal information from
multiple contextual information of the points pair such as global scene
context, local context... | computer science |
28,816 | Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks | cs.CV | Convolutional Neural Networks (CNNs) are well established models capable of
achieving state-of-the-art classification accuracy for various computer vision
tasks. However, they are becoming increasingly larger, using millions of
parameters, while they are restricted to handling images of fixed size. In this
paper, a qua... | computer science |
28,817 | Emotional Filters: Automatic Image Transformation for Inducing Affect | cs.CV | Current image transformation and recoloring algorithms try to introduce
artistic effects in the photographed images, based on user input of target
image(s) or selection of pre-designed filters. These manipulations, although
intended to enhance the impact of an image on the viewer, do not include the
option of image tra... | computer science |
28,818 | Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images
- A Cross-Site Robustness Assessment | cs.CV | Deep learning technologies such as convolutional neural networks (CNN)
provide powerful methods for image recognition and have recently been employed
in the field of automated carcinoma detection in confocal laser endomicroscopy
(CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches
magnificati... | computer science |
28,819 | A Unified Joint Matrix Factorization Framework for Data Integration | cs.CV | Nonnegative matrix factorization (NMF) is a powerful tool in data exploratory
analysis by discovering the hidden features and part-based patterns from
high-dimensional data. NMF and its variants have been successfully applied into
diverse fields such as pattern recognition, signal processing, data mining,
bioinformatic... | computer science |
28,820 | Efficient Yet Deep Convolutional Neural Networks for Semantic
Segmentation | cs.CV | Semantic Segmentation using deep convolutional neural network pose more
complex challenge for any GPU intensive work, as it has to compute million of
parameters resulting to huge consumption of memory. Moreover, extracting finer
features and conducting supervised training tends to increase the complexity
furthermore. W... | computer science |
28,821 | Fast Deep Matting for Portrait Animation on Mobile Phone | cs.CV | Image matting plays an important role in image and video editing. However,
the formulation of image matting is inherently ill-posed. Traditional methods
usually employ interaction to deal with the image matting problem with trimaps
and strokes, and cannot run on the mobile phone in real-time. In this paper, we
propose ... | computer science |
28,822 | Cascaded Scene Flow Prediction using Semantic Segmentation | cs.CV | Given two consecutive frames from a pair of stereo cameras, 3D scene flow
methods simultaneously estimate the 3D geometry and motion of the observed
scene. Many existing approaches use superpixels for regularization, but may
predict inconsistent shapes and motions inside rigidly moving objects. We
instead assume that s... | computer science |
28,823 | Structure-Preserving Image Super-resolution via Contextualized
Multi-task Learning | cs.CV | Single image super resolution (SR), which refers to reconstruct a
higher-resolution (HR) image from the observed low-resolution (LR) image, has
received substantial attention due to its tremendous application potentials.
Despite the breakthroughs of recently proposed SR methods using convolutional
neural networks (CNNs... | computer science |
28,824 | RankIQA: Learning from Rankings for No-reference Image Quality
Assessment | cs.CV | We propose a no-reference image quality assessment (NR-IQA) approach that
learns from rankings (RankIQA). To address the problem of limited IQA dataset
size, we train a Siamese Network to rank images in terms of image quality by
using synthetically generated distortions for which relative image quality is
known. These ... | computer science |
28,825 | Modelling the Scene Dependent Imaging in Cameras with a Deep Neural
Network | cs.CV | We present a novel deep learning framework that models the scene dependent
image processing inside cameras. Often called as the radiometric calibration,
the process of recovering RAW images from processed images (JPEG format in the
sRGB color space) is essential for many computer vision tasks that rely on
physically ac... | computer science |
28,826 | Deep Interactive Region Segmentation and Captioning | cs.CV | With recent innovations in dense image captioning, it is now possible to
describe every object of the scene with a caption while objects are determined
by bounding boxes. However, interpretation of such an output is not trivial due
to the existence of many overlapping bounding boxes. Furthermore, in current
captioning ... | computer science |
28,827 | Product recognition in store shelves as a sub-graph isomorphism problem | cs.CV | The arrangement of products in store shelves is carefully planned to maximize
sales and keep customers happy. However, verifying compliance of real shelves
to the ideal layout is a costly task routinely performed by the store
personnel. In this paper, we propose a computer vision pipeline to recognize
products on shelv... | computer science |
28,828 | A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla
Numerals using Convolutional Neural Networks | cs.CV | Increased accuracy in predictive models for handwritten character recognition
will open up new frontiers for optical character recognition. Major drawbacks
of predictive machine learning models are headed by the elongated training time
taken by some models, and the requirement that training and test data be in the
same... | computer science |
28,829 | Reduction of Overfitting in Diabetes Prediction Using Deep Learning
Neural Network | cs.CV | Augmented accuracy in prediction of diabetes will open up new frontiers in
health prognostics. Data overfitting is a performance-degrading issue in
diabetes prognosis. In this study, a prediction system for the disease of
diabetes is pre-sented where the issue of overfitting is minimized by using the
dropout method. De... | computer science |
28,830 | A Harmony Search Based Wrapper Feature Selection Method for Holistic
Bangla word Recognition | cs.CV | A lot of search approaches have been explored for the selection of features
in pattern classification domain in order to discover significant subset of the
features which produces better accuracy. In this paper, we introduced a Harmony
Search (HS) algorithm based feature selection method for feature dimensionality
redu... | computer science |
28,831 | Detecting and classifying lesions in mammograms with Deep Learning | cs.CV | In the last two decades Computer Aided Diagnostics (CAD) systems were
developed to help radiologists analyze screening mammograms. The benefits of
current CAD technologies appear to be contradictory and they should be improved
to be ultimately considered useful. Since 2012 deep convolutional neural
networks (CNN) have ... | computer science |
28,832 | A Guided Spatial Transformer Network for Histology Cell Differentiation | cs.CV | Identification and counting of cells and mitotic figures is a standard task
in diagnostic histopathology. Due to the large overall cell count on
histological slides and the potential sparse prevalence of some relevant cell
types or mitotic figures, retrieving annotation data for sufficient statistics
is a tedious task ... | computer science |
28,833 | Interpatient Respiratory Motion Model Transfer for Virtual Reality
Simulations of Liver Punctures | cs.CV | Current virtual reality (VR) training simulators of liver punctures often
rely on static 3D patient data and use an unrealistic (sinusoidal) periodic
animation of the respiratory movement. Existing methods for the animation of
breathing motion support simple mathematical or patient-specific, estimated
breathing models.... | computer science |
28,834 | Optimizing Filter Size in Convolutional Neural Networks for Facial
Action Unit Recognition | cs.CV | Recognizing facial action units (AUs) during spontaneous facial displays is a
challenging problem. Most recently, Convolutional Neural Networks (CNNs) have
shown promise for facial AU recognition, where predefined and fixed convolution
filter sizes are employed. In order to achieve the best performance, the
optimal fil... | computer science |
28,835 | Learning a Target Sample Re-Generator for Cross-Database
Micro-Expression Recognition | cs.CV | In this paper, we investigate the cross-database micro-expression recognition
problem, where the training and testing samples are from two different
micro-expression databases. Under this setting, the training and testing
samples would have different feature distributions and hence the performance of
most existing micr... | computer science |
28,836 | Context-aware Single-Shot Detector | cs.CV | SSD is one of the state-of-the-art object detection algorithms, and it
combines high detection accuracy with real-time speed. However, it is widely
recognized that SSD is less accurate in detecting small objects compared to
large objects, because it ignores the context from outside the proposal boxes.
In this paper, we... | computer science |
28,837 | A Jointly Learned Deep Architecture for Facial Attribute Analysis and
Face Detection in the Wild | cs.CV | Facial attribute analysis in the real world scenario is very challenging
mainly because of complex face variations. Existing works of analyzing face
attributes are mostly based on the cropped and aligned face images. However,
this result in the capability of attribute prediction heavily relies on the
preprocessing of f... | computer science |
28,838 | Exploiting Web Images for Weakly Supervised Object Detection | cs.CV | In recent years, the performance of object detection has advanced
significantly with the evolving deep convolutional neural networks. However,
the state-of-the-art object detection methods still rely on accurate bounding
box annotations that require extensive human labelling. Object detection
without bounding box annot... | computer science |
28,839 | Algebraic Relations and Triangulation of Unlabeled Image Points | cs.CV | In multiview geometry when correspondences among multiple views are unknown
the image points can be understood as being unlabeled. This is a common problem
in computer vision. We give a novel approach to handle such a situation by
regarding unlabeled point configurations as points on the Chow variety
$\text{Sym}_m(\mat... | computer science |
28,840 | A Comparative Study of the Clinical use of Motion Analysis from Kinect
Skeleton Data | cs.CV | The analysis of human motion as a clinical tool can bring many benefits such
as the early detection of disease and the monitoring of recovery, so in turn
helping people to lead independent lives. However, it is currently under used.
Developments in depth cameras, such as Kinect, have opened up the use of motion
analysi... | computer science |
28,841 | Representation-Aggregation Networks for Segmentation of Multi-Gigapixel
Histology Images | cs.CV | Convolutional Neural Network (CNN) models have become the state-of-the-art
for most computer vision tasks with natural images. However, these are not best
suited for multi-gigapixel resolution Whole Slide Images (WSIs) of histology
slides due to large size of these images. Current approaches construct smaller
patches f... | computer science |
28,842 | Food Ingredients Recognition through Multi-label Learning | cs.CV | Automatically constructing a food diary that tracks the ingredients consumed
can help people follow a healthy diet. We tackle the problem of food
ingredients recognition as a multi-label learning problem. We propose a method
for adapting a highly performing state of the art CNN in order to act as a
multi-label predicto... | computer science |
28,843 | Serious Games Application for Memory Training Using Egocentric Images | cs.CV | Mild cognitive impairment is the early stage of several neurodegenerative
diseases, such as Alzheimer's. In this work, we address the use of lifelogging
as a tool to obtain pictures from a patient's daily life from an egocentric
point of view. We propose to use them in combination with serious games as a
way to provide... | computer science |
28,844 | STN-OCR: A single Neural Network for Text Detection and Text Recognition | cs.CV | Detecting and recognizing text in natural scene images is a challenging, yet
not completely solved task. In re- cent years several new systems that try to
solve at least one of the two sub-tasks (text detection and text recognition)
have been proposed. In this paper we present STN-OCR, a step towards
semi-supervised ne... | computer science |
28,845 | Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration | cs.CV | The field of connectomics has recently produced neuron wiring diagrams from
relatively large brain regions from multiple animals. Most of these neural
reconstructions were computed from isotropic (e.g., FIBSEM) or near isotropic
(e.g., SBEM) data. In spite of the remarkable progress on algorithms in recent
years, autom... | computer science |
28,846 | Concise Radiometric Calibration Using The Power of Ranking | cs.CV | Compared with raw images, the more common JPEG images are less useful for
machine vision algorithms and professional photographers because JPEG-sRGB does
not preserve a linear relation between pixel values and the light measured from
the scene. A camera is said to be radiometrically calibrated if there is a
computation... | computer science |
28,847 | Handwritten character recognition using some (anti)-diagonal structural
features | cs.CV | In this paper, we present a methodology for off-line handwritten character
recognition. The proposed methodology relies on a new feature extraction
technique based on structural characteristics, histograms and profiles. As
novelty, we propose the extraction of new eight histograms and four profiles
from the $32\times 3... | computer science |
28,848 | Building Detection from Satellite Images on a Global Scale | cs.CV | In the last several years, remote sensing technology has opened up the
possibility of performing large scale building detection from satellite
imagery. Our work is some of the first to create population density maps from
building detection on a large scale. The scale of our work on population
density estimation via hig... | computer science |
28,849 | Understanding Aesthetics in Photography using Deep Convolutional Neural
Networks | cs.CV | Evaluating aesthetic value of digital photographs is a challenging task,
mainly due to numerous factors that need to be taken into account and
subjective manner of this process. In this paper, we propose to approach this
problem using deep convolutional neural networks. Using a dataset of over 1.7
million photos collec... | computer science |
28,850 | Efficient Deformable Shape Correspondence via Kernel Matching | cs.CV | We present a method to match three dimensional shapes under non-isometric
deformations, topology changes and partiality. We formulate the problem as
matching between a set of pair-wise and point-wise descriptors, imposing a
continuity prior on the mapping, and propose a projected descent optimization
procedure inspired... | computer science |
28,851 | A Locally Adapting Technique for Boundary Detection using Image
Segmentation | cs.CV | Rapid growth in the field of quantitative digital image analysis is paving
the way for researchers to make precise measurements about objects in an image.
To compute quantities from the image such as the density of compressed
materials or the velocity of a shockwave, we must determine object boundaries.
Images containi... | computer science |
28,852 | Learning from Video and Text via Large-Scale Discriminative Clustering | cs.CV | Discriminative clustering has been successfully applied to a number of
weakly-supervised learning tasks. Such applications include person and action
recognition, text-to-video alignment, object co-segmentation and colocalization
in videos and images. One drawback of discriminative clustering, however, is
its limited sc... | computer science |
28,853 | Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions | cs.CV | Visual localization enables autonomous vehicles to navigate in their
surroundings and augmented reality applications to link virtual to real worlds.
Practical visual localization approaches need to be robust to a wide variety of
viewing condition, including day-night changes, as well as weather and seasonal
variations,... | computer science |
28,854 | Object Detection of Satellite Images Using Multi-Channel Higher-order
Local Autocorrelation | cs.CV | The Earth observation satellites have been monitoring the earth's surface for
a long time, and the images taken by the satellites contain large amounts of
valuable data. However, it is extremely hard work to manually analyze such huge
data. Thus, a method of automatic object detection is needed for satellite
images to ... | computer science |
28,855 | MixedPeds: Pedestrian Detection in Unannotated Videos using
Synthetically Generated Human-agents for Training | cs.CV | We present a new method for training pedestrian detectors on an unannotated
set of images. We produce a mixed reality dataset that is composed of
real-world background images and synthetically generated static human-agents.
Our approach is general, robust, and makes no other assumptions about the
unannotated dataset re... | computer science |
28,856 | Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional
Network with Bayesian Optimization | cs.CV | When approaching a novel visual recognition problem in a specialized image
domain, a common strategy is to start with a pre-trained deep neural network
and fine-tune it to the specialized domain. If the target domain covers a
smaller visual space than the source domain used for pre-training (e.g.
ImageNet), the fine-tu... | computer science |
28,857 | Deep Co-Space: Sample Mining Across Feature Transformation for
Semi-Supervised Learning | cs.CV | Aiming at improving performance of visual classification in a cost-effective
manner, this paper proposes an incremental semi-supervised learning paradigm
called Deep Co-Space (DCS). Unlike many conventional semi-supervised learning
methods usually performing within a fixed feature space, our DCS gradually
propagates in... | computer science |
28,858 | Learning Pixel-Distribution Prior with Wider Convolution for Image
Denoising | cs.CV | In this work, we explore an innovative strategy for image denoising by using
convolutional neural networks (CNN) to learn pixel-distribution from noisy
data. By increasing CNN's width with large reception fields and more channels
in each layer, CNNs can reveal the ability to learn pixel-distribution, which
is a prior e... | computer science |
28,859 | Localizing Actions from Video Labels and Pseudo-Annotations | cs.CV | The goal of this paper is to determine the spatio-temporal location of
actions in video. Where training from hard to obtain box annotations is the
norm, we propose an intuitive and effective algorithm that localizes actions
from their class label only. We are inspired by recent work showing that
unsupervised action pro... | computer science |
28,860 | Spatial-Aware Object Embeddings for Zero-Shot Localization and
Classification of Actions | cs.CV | We aim for zero-shot localization and classification of human actions in
video. Where traditional approaches rely on global attribute or object
classification scores for their zero-shot knowledge transfer, our main
contribution is a spatial-aware object embedding. To arrive at spatial
awareness, we build our embedding ... | computer science |
28,861 | Group Re-Identification via Unsupervised Transfer of Sparse Features
Encoding | cs.CV | Person re-identification is best known as the problem of associating a single
person that is observed from one or more disjoint cameras. The existing
literature has mainly addressed such an issue, neglecting the fact that people
usually move in groups, like in crowded scenarios. We believe that the
additional informati... | computer science |
28,862 | A weighting strategy for Active Shape Models | cs.CV | Active Shape Models (ASM) are an iterative segmentation technique to find a
landmark-based contour of an object. In each iteration, a least-squares fit of
a plausible shape to some detected target landmarks is determined. Finding
these targets is a critical step: some landmarks are more reliably detected
than others, a... | computer science |
28,863 | The WILDTRACK Multi-Camera Person Dataset | cs.CV | People detection methods are highly sensitive to the perpetual occlusions
among the targets. As multi-camera set-ups become more frequently encountered,
joint exploitation of the across views information would allow for improved
detection performances. We provide a large-scale HD dataset named WILDTRACK
which finally m... | computer science |
28,864 | Sparse Deep Nonnegative Matrix Factorization | cs.CV | Nonnegative matrix factorization is a powerful technique to realize dimension
reduction and pattern recognition through single-layer data representation
learning. Deep learning, however, with its carefully designed hierarchical
structure, is able to combine hidden features to form more representative
features for patte... | computer science |
28,865 | FontCode: Embedding Information in Text Documents using Glyph
Perturbation | cs.CV | We introduce FontCode, an information embedding technique for text documents.
Provided a text document with specific fonts, our method embeds user-specified
information in the text by perturbing the glyphs of text characters while
preserving the text content. We devise an algorithm to chooses unobtrusive yet
machine-re... | computer science |
28,866 | Visual Relationship Detection with Internal and External Linguistic
Knowledge Distillation | cs.CV | Understanding visual relationships involves identifying the subject, the
object, and a predicate relating them. We leverage the strong correlations
between the predicate and the (subj,obj) pair (both semantically and spatially)
to predict the predicates conditioned on the subjects and the objects. Modeling
the three en... | computer science |
28,867 | Weakly-supervised learning of visual relations | cs.CV | This paper introduces a novel approach for modeling visual relations between
pairs of objects. We call relation a triplet of the form (subject, predicate,
object) where the predicate is typically a preposition (eg. 'under', 'in front
of') or a verb ('hold', 'ride') that links a pair of objects (subject, object).
Learni... | computer science |
28,868 | FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in
City Cameras | cs.CV | In this paper, we develop deep spatio-temporal neural networks to
sequentially count vehicles from low quality videos captured by city cameras
(citycams). Citycam videos have low resolution, low frame rate, high occlusion
and large perspective, making most existing methods lose their efficacy. To
overcome limitations o... | computer science |
28,869 | Deep Feature Consistent Deep Image Transformations: Downscaling,
Decolorization and HDR Tone Mapping | cs.CV | Building on crucial insights into the determining factors of the visual
integrity of an image and the property of deep convolutional neural network
(CNN), we have developed the Deep Feature Consistent Deep Image Transformation
(DFC-DIT) framework which unifies challenging one-to-many mapping image
processing problems s... | computer science |
28,870 | Recurrent Scale Approximation for Object Detection in CNN | cs.CV | Since convolutional neural network (CNN) lacks an inherent mechanism to
handle large scale variations, we always need to compute feature maps multiple
times for multi-scale object detection, which has the bottleneck of
computational cost in practice. To address this, we devise a recurrent scale
approximation (RSA) to c... | computer science |
28,871 | Synthetic Database for Evaluation of General, Fundamental Biometric
Principles | cs.CV | We create synthetic biometric databases to study general, fundamental,
biometric principles. First, we check the validity of the synthetic database
design by comparing it to real data in terms of biometric performance. The real
data used for this validity check was from an eye-movement related biometric
database. Next,... | computer science |
28,872 | Improved Adversarial Systems for 3D Object Generation and Reconstruction | cs.CV | This paper describes a new approach for training generative adversarial
networks (GAN) to understand the detailed 3D shape of objects. While GANs have
been used in this domain previously, they are notoriously hard to train,
especially for the complex joint data distribution over 3D objects of many
categories and orient... | computer science |
28,873 | Discover and Learn New Objects from Documentaries | cs.CV | Despite the remarkable progress in recent years, detecting objects in a new
context remains a challenging task. Detectors learned from a public dataset can
only work with a fixed list of categories, while training from scratch usually
requires a large amount of training data with detailed annotations. This work
aims to... | computer science |
28,874 | ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast
Cancer Detection from Whole-Slide Images | cs.CV | Lymph node metastasis is one of the most significant diagnostic indicators in
breast cancer, which is traditionally observed under the microscope by
pathologists. In recent years, computerized histology diagnosis has become one
of the most rapidly expanding fields in medical image computing, which
alleviates pathologis... | computer science |
28,875 | Occlusion Handling using Semantic Segmentation and Visibility-Based
Rendering for Mixed Reality | cs.CV | Real-time occlusion handling is a major problem in outdoor mixed reality
system because it requires great computational cost mainly due to the
complexity of the scene. Using only segmentation, it is difficult to accurately
render a virtual object occluded by complex objects such as trees, bushes etc.
In this paper, we ... | computer science |
28,876 | CNN-based Cascaded Multi-task Learning of High-level Prior and Density
Estimation for Crowd Counting | cs.CV | Estimating crowd count in densely crowded scenes is an extremely challenging
task due to non-uniform scale variations. In this paper, we propose a novel
end-to-end cascaded network of CNNs to jointly learn crowd count classification
and density map estimation. Classifying crowd count into various groups is
tantamount t... | computer science |
28,877 | A Novel Approach for Image Segmentation based on Histograms computed
from Hue-data | cs.CV | Computer Vision is growing day by day in terms of user specific applications.
The first step of any such application is segmenting an image. In this paper,
we propose a novel and grass-root level image segmentation algorithm for cases
in which the background has uniform color distribution. This algorithm can be
used fo... | computer science |
28,878 | Deep Multi-View Learning with Stochastic Decorrelation Loss | cs.CV | Multi-view learning aims to learn an embedding space where multiple views are
either maximally correlated for cross-view recognition, or decorrelated for
latent factor disentanglement. A key challenge for deep multi-view
representation learning is scalability. To correlate or decorrelate multi-view
signals, the covaria... | computer science |
28,879 | Recurrent 3D Pose Sequence Machines | cs.CV | 3D human articulated pose recovery from monocular image sequences is very
challenging due to the diverse appearances, viewpoints, occlusions, and also
the human 3D pose is inherently ambiguous from the monocular imagery. It is
thus critical to exploit rich spatial and temporal long-range dependencies
among body joints ... | computer science |
28,880 | Scene Graph Generation from Objects, Phrases and Region Captions | cs.CV | Object detection, scene graph generation and region captioning, which are
three scene understanding tasks at different semantic levels, are tied
together: scene graphs are generated on top of objects detected in an image
with their pairwise relationship predicted, while region captioning gives a
language description of... | computer science |
28,881 | Analysis and Optimization of Convolutional Neural Network Architectures | cs.CV | Convolutional Neural Networks (CNNs) dominate various computer vision tasks
since Alex Krizhevsky showed that they can be trained effectively and reduced
the top-5 error from 26.2 % to 15.3 % on the ImageNet large scale visual
recognition challenge. Many aspects of CNNs are examined in various
publications, but literat... | computer science |
28,882 | Camera Relocalization by Computing Pairwise Relative Poses Using
Convolutional Neural Network | cs.CV | We propose a new deep learning based approach for camera relocalization. Our
approach localizes a given query image by using a convolutional neural network
(CNN) for first retrieving similar database images and then predicting the
relative pose between the query and the database images, whose poses are known.
The camer... | computer science |
28,883 | Synthesis of Positron Emission Tomography (PET) Images via Multi-channel
Generative Adversarial Networks (GANs) | cs.CV | Positron emission tomography (PET) image synthesis plays an important role,
which can be used to boost the training data for computer aided diagnosis
systems. However, existing image synthesis methods have problems in
synthesizing the low resolution PET images. To address these limitations, we
propose multi-channel gen... | computer science |
28,884 | Unsupervised Visual Attribute Transfer with Reconfigurable Generative
Adversarial Networks | cs.CV | Learning to transfer visual attributes requires supervision dataset.
Corresponding images with varying attribute values with the same identity are
required for learning the transfer function. This largely limits their
applications, because capturing them is often a difficult task. To address the
issue, we propose an un... | computer science |
28,885 | 2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation | cs.CV | In this paper, we develop a 2D and 3D segmentation pipelines for fully
automated cardiac MR image segmentation using Deep Convolutional Neural
Networks (CNN). Our models are trained end-to-end from scratch using the ACD
Challenge 2017 dataset comprising of 100 studies, each containing Cardiac MR
images in End Diastole ... | computer science |
28,886 | Deep Domain Adaptation by Geodesic Distance Minimization | cs.CV | In this paper, we propose a new approach called Deep LogCORAL for
unsupervised visual domain adaptation. Our work builds on the recently proposed
Deep CORAL method, which proposed to train a convolutional neural network and
simultaneously minimize the Euclidean distance of convariance matrices between
the source and ta... | computer science |
28,887 | Convolution with Logarithmic Filter Groups for Efficient Shallow CNN | cs.CV | In convolutional neural networks (CNNs), the filter grouping in convolution
layers is known to be useful to reduce the network parameter size. In this
paper, we propose a new logarithmic filter grouping which can capture the
nonlinearity of filter distribution in CNNs. The proposed logarithmic filter
grouping is instal... | computer science |
28,888 | Spatially variant PSF modeling in confocal macroscopy | cs.CV | Point spread function (PSF) plays an essential role in image reconstruction.
In the context of confocal microscopy, optical performance degrades towards the
edge of the field of view as astigmatism, coma and vignetting. Thus, one should
expect the related artifacts to be even stronger in macroscopy, where the field
of ... | computer science |
28,889 | Iterative Manifold Embedding Layer Learned by Incomplete Data for
Large-scale Image Retrieval | cs.CV | Existing manifold learning methods are not appropriate for image retrieval
task, because most of them are unable to process query image and they have much
additional computational cost especially for large scale database. Therefore,
we propose the iterative manifold embedding (IME) layer, of which the weights
are learn... | computer science |
28,890 | Generalizing the Convolution Operator in Convolutional Neural Networks | cs.CV | Convolutional neural networks have become a main tool for solving many
machine vision and machine learning problems. A major element of these networks
is the convolution operator which essentially computes the inner product
between a weight vector and the vectorized image patches extracted by sliding a
window in the im... | computer science |
28,891 | Guided Co-training for Large-Scale Multi-View Spectral Clustering | cs.CV | In many real-world applications, we have access to multiple views of the
data, each of which characterizes the data from a distinct aspect. Several
previous algorithms have demonstrated that one can achieve better clustering
accuracy by integrating information from all views appropriately than using
only an individual ... | computer science |
28,892 | A comment on the paper Prediction of Kidney Function from Biopsy Images
using Convolutional Neural Networks | cs.CV | This letter presente a comment on the paper Prediction of Kidney Function
from Biopsy Images using Convolutional Neural Networks by Ledbetter et al.
(2017) | computer science |
28,893 | Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM | cs.CV | Although deep learning models are highly effective for various learning
tasks, their high computational costs prohibit the deployment to scenarios
where either memory or computational resources are limited. In this paper, we
focus on compressing and accelerating deep models with network weights
represented by very smal... | computer science |
28,894 | Representation Learning on Large and Small Data | cs.CV | Deep learning owes its success to three key factors: scale of data, enhanced
models to learn representations from data, and scale of computation. This book
chapter presented the importance of the data-driven approach to learn good
representations from both big data and small data. In terms of big data, it has
been wide... | computer science |
28,895 | A Framework for Super-Resolution of Scalable Video via Sparse
Reconstruction of Residual Frames | cs.CV | This paper introduces a framework for super-resolution of scalable video
based on compressive sensing and sparse representation of residual frames in
reconnaissance and surveillance applications. We exploit efficient compressive
sampling and sparse reconstruction algorithms to super-resolve the video
sequence with resp... | computer science |
28,896 | (k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity
Prior | cs.CV | Advanced diffusion magnetic resonance imaging (dMRI) techniques, like
diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging
(HARDI), remain underutilized compared to diffusion tensor imaging because the
scan times needed to produce accurate estimations of fiber orientation are
significantly lon... | computer science |
28,897 | Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal
Images" | cs.CV | Remote sensing images often suffer from cloud cover. Cloud removal is
required in many applications of remote sensing images. Multitemporal-based
methods are popular and effective to cope with thick clouds. This paper
contributes to a summarization and experimental comparation of the existing
multitemporal-based method... | computer science |
28,898 | Spatio-Temporal Action Detection with Cascade Proposal and Location
Anticipation | cs.CV | In this work, we address the problem of spatio-temporal action detection in
temporally untrimmed videos. It is an important and challenging task as finding
accurate human actions in both temporal and spatial space is important for
analyzing large-scale video data. To tackle this problem, we propose a cascade
proposal a... | computer science |
28,899 | Statistics on the (compact) Stiefel manifold: Theory and Applications | cs.CV | A Stiefel manifold of the compact type is often encountered in many fields of
Engineering including, signal and image processing, machine learning, numerical
optimization and others. The Stiefel manifold is a Riemannian homogeneous space
but not a symmetric space. In previous work, researchers have defined
probability ... | computer science |
28,900 | Towards the Success Rate of One: Real-time Unconstrained Salient Object
Detection | cs.CV | In this work, we propose an efficient and effective approach for
unconstrained salient object detection in images using deep convolutional
neural networks. Instead of generating thousands of candidate bounding boxes
and refining them, our network directly learns to generate the saliency map
containing the exact number ... | computer science |
28,901 | Material Editing Using a Physically Based Rendering Network | cs.CV | The ability to edit materials of objects in images is desirable by many
content creators. However, this is an extremely challenging task as it requires
to disentangle intrinsic physical properties of an image. We propose an
end-to-end network architecture that replicates the forward image formation
process to accomplis... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.