Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
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30,902 | C2MSNet: A Novel approach for single image haze removal | cs.CV | Degradation of image quality due to the presence of haze is a very common
phenomenon. Existing DehazeNet [3], MSCNN [11] tackled the drawbacks of hand
crafted haze relevant features. However, these methods have the problem of
color distortion in gloomy (poor illumination) environment. In this paper, a
cardinal (red, gr... | computer science |
30,903 | Convolutional Invasion and Expansion Networks for Tumor Growth
Prediction | cs.CV | Tumor growth is associated with cell invasion and mass-effect, which are
traditionally formulated by mathematical models, namely reaction-diffusion
equations and biomechanics. Such models can be personalized based on clinical
measurements to build the predictive models for tumor growth. In this paper, we
investigate th... | computer science |
30,904 | Self-Learning to Detect and Segment Cysts in Lung CT Images without
Manual Annotation | cs.CV | Image segmentation is a fundamental problem in medical image analysis. In
recent years, deep neural networks achieve impressive performances on many
medical image segmentation tasks by supervised learning on large manually
annotated data. However, expert annotations on big medical datasets are
tedious, expensive or som... | computer science |
30,905 | Unmixing urban hyperspectral imagery with a Gaussian mixture model on
endmember variability | cs.CV | In this paper, we model a pixel as a linear combination of endmembers sampled
from probability distributions of Gaussian mixture models (GMM). The parameters
of the GMM distributions are estimated using spectral libraries. Abundances are
estimated based on the distribution parameters. The advantage of this algorithm
is... | computer science |
30,906 | Deep Learning for End-to-End Automatic Target Recognition from Synthetic
Aperture Radar Imagery | cs.CV | The standard architecture of synthetic aperture radar (SAR) automatic target
recognition (ATR) consists of three stages: detection, discrimination, and
classification. In recent years, convolutional neural networks (CNNs) for SAR
ATR have been proposed, but most of them classify target classes from a target
chip extrac... | computer science |
30,907 | Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic
Tumors on CT scans | cs.CV | This paper reports Deep LOGISMOS approach to 3D tumor segmentation by
incorporating boundary information derived from deep contextual learning to
LOGISMOS - layered optimal graph image segmentation of multiple objects and
surfaces. Accurate and reliable tumor segmentation is essential to tumor growth
analysis and treat... | computer science |
30,908 | A Rapidly Deployable Classification System using Visual Data for the
Application of Precision Weed Management | cs.CV | In this work we demonstrate a rapidly deployable weed classification system
that uses visual data to enable autonomous precision weeding without making
prior assumptions about which weed species are present in a given field.
Previous work in this area relies on having prior knowledge of the weed species
present in the ... | computer science |
30,909 | Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans:
Self-Paced 3D Mask Generation from RECIST | cs.CV | Volumetric lesion segmentation via medical imaging is a powerful means to
precisely assess multiple time-point lesion/tumor changes. Because manual 3D
segmentation is prohibitively time consuming and requires radiological
experience, current practices rely on an imprecise surrogate called response
evaluation criteria i... | computer science |
30,910 | DeepPap: Deep Convolutional Networks for Cervical Cell Classification | cs.CV | Automation-assisted cervical screening via Pap smear or liquid-based cytology
(LBC) is a highly effective cell imaging based cancer detection tool, where
cells are partitioned into "abnormal" and "normal" categories. However, the
success of most traditional classification methods relies on the presence of
accurate cell... | computer science |
30,911 | Generating Handwritten Chinese Characters using CycleGAN | cs.CV | Handwriting of Chinese has long been an important skill in East Asia.
However, automatic generation of handwritten Chinese characters poses a great
challenge due to the large number of characters. Various machine learning
techniques have been used to recognize Chinese characters, but few works have
studied the handwrit... | computer science |
30,912 | Cloud Detection From RGB Color Remote Sensing Images With Deep Pyramid
Networks | cs.CV | Cloud detection from remotely observed data is a critical pre-processing step
for various remote sensing applications. In particular, this problem becomes
even harder for RGB color images, since there is no distinct spectral pattern
for clouds, which is directly separable from the Earth surface. In this paper,
we adapt... | computer science |
30,913 | Weakly Supervised Object Detection with Pointwise Mutual Information | cs.CV | In this work a novel approach for weakly supervised object detection that
incorporates pointwise mutual information is presented. A fully convolutional
neural network architecture is applied in which the network learns one filter
per object class. The resulting feature map indicates the location of objects
in an image,... | computer science |
30,914 | Generating Instance Segmentation Annotation by Geometry-guided GAN | cs.CV | Instance segmentation is a problem of significance in computer vision.
However, preparing annotated data for this task is extremely time-consuming and
costly. By combining the advantages of 3D scanning, physical reasoning, and GAN
techniques, we introduce a novel pipeline named Geometry-guided GAN (GeoGAN) to
obtain la... | computer science |
30,915 | Efficient Hierarchical Graph-Based Segmentation of RGBD Videos | cs.CV | We present an efficient and scalable algorithm for segmenting 3D RGBD point
clouds by combining depth, color, and temporal information using a multistage,
hierarchical graph-based approach. Our algorithm processes a moving window over
several point clouds to group similar regions over a graph, resulting in an
initial o... | computer science |
30,916 | A Two-point Method for PTZ Camera Calibration in Sports | cs.CV | Calibrating narrow field of view soccer cameras is challenging because there
are very few field markings in the image. Unlike previous solutions, we propose
a two-point method, which requires only two point correspondences given the
prior knowledge of base location and orientation of a pan-tilt-zoom (PTZ)
camera. We de... | computer science |
30,917 | Tell-and-Answer: Towards Explainable Visual Question Answering using
Attributes and Captions | cs.CV | Visual Question Answering (VQA) has attracted attention from both computer
vision and natural language processing communities. Most existing approaches
adopt the pipeline of representing an image via pre-trained CNNs, and then
using the uninterpretable CNN features in conjunction with the question to
predict the answer... | computer science |
30,918 | Image2GIF: Generating Cinemagraphs using Recurrent Deep Q-Networks | cs.CV | Given a still photograph, one can imagine how dynamic objects might move
against a static background. This idea has been actualized in the form of
cinemagraphs, where the motion of particular objects within a still image is
repeated, giving the viewer a sense of animation. In this paper, we learn
computational models t... | computer science |
30,919 | Ear Recognition With Score-Level Fusion Based On CMC In Long-Wave
Infrared Spectrum | cs.CV | Only a few studies have been reported regarding human ear recognition in long
wave infrared band. Thus, we have created ear database based on long wave
infrared band. We have called that the database is long wave infrared band
MIDAS consisting of 2430 records of 81 subjects. Thermal band provides seamless
operation bot... | computer science |
30,920 | A Multi-Biometrics for Twins Identification Based Speech and Ear | cs.CV | The development of technology biometrics becomes crucial more. To define
human characteristic biometric systems are used but because of inability of
traditional biometric systems to recognize twins, multimodal biometric systems
are developed. In this study a multimodal biometric recognition system is
proposed to recogn... | computer science |
30,921 | Fine-grained Visual Categorization using PAIRS: Pose and Appearance
Integration for Recognizing Subcategories | cs.CV | Fine-grained Visual Categorization (FGVC) saw a tremendous boost between 2013
and 2016 with the incorporation of deep learning, however, progress has
recently begun to slow. In this work, we postulate that one key to continued
advances in fine-grained recognition performance is a better, and specifically,
a more explic... | computer science |
30,922 | Interactive Deep Colorization With Simultaneous Global and Local Inputs | cs.CV | Colorization methods using deep neural networks have become a recent trend.
However, most of them do not allow user inputs, or only allow limited user
inputs (only global inputs or only local inputs), to control the output
colorful images. The possible reason is that it's difficult to differentiate
the influence of dif... | computer science |
30,923 | A Generative Approach to Zero-Shot and Few-Shot Action Recognition | cs.CV | We present a generative framework for zero-shot action recognition where some
of the possible action classes do not occur in the training data. Our approach
is based on modeling each action class using a probability distribution whose
parameters are functions of the attribute vector representing that action
class. In p... | computer science |
30,924 | Interactive Generative Adversarial Networks for Facial Expression
Generation in Dyadic Interactions | cs.CV | A social interaction is a social exchange between two or more
individuals,where individuals modify and adjust their behaviors in response to
their interaction partners. Our social interactions are one of most fundamental
aspects of our lives and can profoundly affect our mood, both positively and
negatively. With growi... | computer science |
30,925 | Towards an Understanding of Neural Networks in Natural-Image Spaces | cs.CV | Two major uncertainties, dataset bias and perturbation, prevail in
state-of-the-art AI algorithms with deep neural networks. In this paper, we
present an intuitive explanation for these issues as well as an interpretation
of the performance of deep networks in a natural-image space. The explanation
consists of two part... | computer science |
30,926 | Understanding Deep Architectures by Interpretable Visual Summaries | cs.CV | A consistent body of research investigates the recurrent visual patterns
exploited by deep networks for object classification with the help of diverse
visualization techniques. Unfortunately, no effort has been spent in showing
that these techniques are effective in leading researchers to univocal and
exhaustive explan... | computer science |
30,927 | Deep Neural Networks In Fully Connected CRF For Image Labeling With
Social Network Metadata | cs.CV | We propose a novel method for predicting image labels by fusing image content
descriptors with the social media context of each image. An image uploaded to a
social media site such as Flickr often has meaningful, associated information,
such as comments and other images the user has uploaded, that is complementary
to p... | computer science |
30,928 | Robust Multi-subspace Analysis Using Novel Column L0-norm Constrained
Matrix Factorization | cs.CV | We study the underlying structure of data (approximately) generated from a
union of independent subspaces. Traditional methods learn only one subspace,
failing to discover the multi-subspace structure, while state-of-the-art
methods analyze the multi-subspace structure using data themselves as the
dictionary, which can... | computer science |
30,929 | Contextual Multi-Scale Region Convolutional 3D Network for Activity
Detection | cs.CV | Activity detection is a fundamental problem in computer vision. Detecting
activities of different temporal scales is particularly challenging. In this
paper, we propose the contextual multi-scale region convolutional 3D network
(CMS-RC3D) for activity detection. To deal with the inherent temporal scale
variability of a... | computer science |
30,930 | Improved Training of Generative Adversarial Networks Using
Representative Features | cs.CV | Despite of the success of Generative Adversarial Networks (GANs) for image
generation tasks, the trade-off between image diversity and visual quality are
an well-known issue. Conventional techniques achieve either visual quality or
image diversity; the improvement in one side is often the result of sacrificing
the degr... | computer science |
30,931 | Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark
Localization | cs.CV | 3D face shape is more expressive and viewpoint-consistent than its 2D
counterpart. However, 3D facial landmark localization in a single image is
challenging due to the ambiguous nature of landmarks under 3D perspective.
Existing approaches typically adopt a suboptimal two-step strategy, performing
2D landmark localizat... | computer science |
30,932 | Comparative Study of ECO and CFNet Trackers in Noisy Environment | cs.CV | Object tracking is one of the most challenging task and has secured
significant attention of computer vision researchers in the past two decades.
Recent deep learning based trackers have shown good performance on various
tracking challenges. A tracking method should track objects in sequential
frames accurately in chal... | computer science |
30,933 | Shift-Net: Image Inpainting via Deep Feature Rearrangement | cs.CV | Deep convolutional networks (CNNs) have exhibited their potential in image
inpainting for producing plausible results.However, in most existing methods,
e.g., context encoder, the missing parts are predicted by propagating the
surrounding convolutional features through a fully connected layer, which
intends to produce ... | computer science |
30,934 | CosFace: Large Margin Cosine Loss for Deep Face Recognition | cs.CV | Face recognition has achieved revolutionary advancement owing to the
advancement of the deep convolutional neural network (CNN). The central task of
face recognition, including face verification and identification, involves face
feature discrimination. However, traditional softmax loss of deep CNN usually
lacks the pow... | computer science |
30,935 | Local Visual Microphones: Improved Sound Extraction from Silent Video | cs.CV | Sound waves cause small vibrations in nearby objects. A few techniques exist
in the literature that can extract sound from video. In this paper we study
local vibration patterns at different image locations. We show that different
locations in the image vibrate differently. We carefully aggregate local
vibrations and p... | computer science |
30,936 | TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D
Segmentation using Sparse and Binary Convolutions | cs.CV | Deep convolutional neural networks (DCNN) are currently ubiquitous in medical
imaging. While their versatility and high quality results for common image
analysis tasks including segmentation, localisation and prediction is
astonishing, the large representational power comes at the cost of highly
demanding computational... | computer science |
30,937 | Hierarchical Spatial Transformer Network | cs.CV | Computer vision researchers have been expecting that neural networks have
spatial transformation ability to eliminate the interference caused by
geometric distortion for a long time. Emergence of spatial transformer network
makes dream come true. Spatial transformer network and its variants can handle
global displaceme... | computer science |
30,938 | DeepSIC: Deep Semantic Image Compression | cs.CV | Incorporating semantic information into the codecs during image compression
can significantly reduce the repetitive computation of fundamental semantic
analysis (such as object recognition) in client-side applications. The same
practice also enable the compressed code to carry the image semantic
information during stor... | computer science |
30,939 | Histogram of Oriented Depth Gradients for Action Recognition | cs.CV | In this paper, we report on experiments with the use of local measures for
depth motion for visual action recognition from MPEG encoded RGBD video
sequences. We show that such measures can be combined with local space-time
video descriptors for appearance to provide a computationally efficient method
for recognition of... | computer science |
30,940 | Learning-based Image Reconstruction via Parallel Proximal Algorithm | cs.CV | In the past decade, sparsity-driven regularization has led to advancement of
image reconstruction algorithms. Traditionally, such regularizers rely on
analytical models of sparsity (e.g. total variation (TV)). However, more recent
methods are increasingly centered around data-driven arguments inspired by deep
learning.... | computer science |
30,941 | End-to-End Fine-Grained Action Segmentation and Recognition Using
Conditional Random Field Models and Discriminative Sparse Coding | cs.CV | Fine-grained action segmentation and recognition is an important yet
challenging task. Given a long, untrimmed sequence of kinematic data, the task
is to classify the action at each time frame and segment the time series into
the correct sequence of actions. In this paper, we propose a novel framework
that combines a t... | computer science |
30,942 | Improving Multiple Object Tracking with Optical Flow and Edge
Preprocessing | cs.CV | In this paper, we present a new method for detecting road users in an urban
environment which leads to an improvement in multiple object tracking. Our
method takes as an input a foreground image and improves the object detection
and segmentation. This new image can be used as an input to trackers that use
foreground bl... | computer science |
30,943 | Denoising Arterial Spin Labeling Cerebral Blood Flow Images Using Deep
Learning | cs.CV | Arterial spin labeling perfusion MRI is a noninvasive technique for measuring
quantitative cerebral blood flow (CBF), but the measurement is subject to a low
signal-to-noise-ratio(SNR). Various post-processing methods have been proposed
to denoise ASL MRI but only provide moderate improvement. Deep learning (DL) is
an ... | computer science |
30,944 | Object-based reasoning in VQA | cs.CV | Visual Question Answering (VQA) is a novel problem domain where multi-modal
inputs must be processed in order to solve the task given in the form of a
natural language. As the solutions inherently require to combine visual and
natural language processing with abstract reasoning, the problem is considered
as AI-complete... | computer science |
30,945 | Deep Learning based Retinal OCT Segmentation | cs.CV | Our objective is to evaluate the efficacy of methods that use deep learning
(DL) for the automatic fine-grained segmentation of optical coherence
tomography (OCT) images of the retina. OCT images from 10 patients with mild
non-proliferative diabetic retinopathy were used from a public (U. of Miami)
dataset. For each pa... | computer science |
30,946 | Object Detection in Videos by Short and Long Range Object Linking | cs.CV | We address the problem of detecting objects in videos with the interest in
exploring temporal contexts. Our core idea is to link objects in the short and
long ranges for improving the classification quality. Our approach first
proposes a set of candidate spatio-temporal cuboids, each of which serves as a
container asso... | computer science |
30,947 | Structured Memory based Deep Model to Detect as well as Characterize
Novel Inputs | cs.CV | While deep learning has pushed the boundaries in various machine learning
tasks, the current models are still far away from replicating many functions
that a normal human brain can do. Explicit memorization based deep architecture
have been recently proposed with the objective to understand and predict
better. In this ... | computer science |
30,948 | E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene
Text | cs.CV | An end-to-end method for multi-language scene text localization, recognition
and script identification is proposed. The approach is based on a set of
convolutional neural nets. The method, called E2E-MLT, achieves
state-of-the-art performance for both joint localization and script
identification in natural images and i... | computer science |
30,949 | Diagnose like a Radiologist: Attention Guided Convolutional Neural
Network for Thorax Disease Classification | cs.CV | This paper considers the task of thorax disease classification on chest X-ray
images. Existing methods generally use the global image as input for network
learning. Such a strategy is limited in two aspects. 1) A thorax disease
usually happens in (small) localized areas which are disease specific. Training
CNNs using g... | computer science |
30,950 | Sliding Line Point Regression for Shape Robust Scene Text Detection | cs.CV | Traditional text detection methods mostly focus on quadrangle text. In this
study we propose a novel method named sliding line point regression (SLPR) in
order to detect arbitrary-shape text in natural scene. SLPR regresses multiple
points on the edge of text line and then utilizes these points to sketch the
outlines o... | computer science |
30,951 | An Iterative Spanning Forest Framework for Superpixel Segmentation | cs.CV | Superpixel segmentation has become an important research problem in image
processing. In this paper, we propose an Iterative Spanning Forest (ISF)
framework, based on sequences of Image Foresting Transforms, where one can
choose i) a seed sampling strategy, ii) a connectivity function, iii) an
adjacency relation, and i... | computer science |
30,952 | Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation:
the Benefit of Target Expectation Maximization | cs.CV | In this paper we make two contributions to unsupervised domain adaptation in
the convolutional neural network. First, our approach transfers knowledge in
the deep side of neural networks for all convolutional layers. Previous methods
usually do so by directly aligning higher-level representations, e.g., aligning
the ac... | computer science |
30,953 | SegDenseNet: Iris Segmentation for Pre and Post Cataract Surgery | cs.CV | Cataract is caused due to various factors such as age, trauma, genetics,
smoking and substance consumption, and radiation. It is one of the major common
ophthalmic diseases worldwide which can potentially affect iris-based biometric
systems. India, which hosts the largest biometrics project in the world, has
about 8 mi... | computer science |
30,954 | Trajectory-based Radical Analysis Network for Online Handwritten Chinese
Character Recognition | cs.CV | Recently, great progress has been made for online handwritten Chinese
character recognition due to the emergence of deep learning techniques.
However, previous research mostly treated each Chinese character as one class
without explicitly considering its inherent structure, namely the radical
components with complicate... | computer science |
30,955 | Video-based Sign Language Recognition without Temporal Segmentation | cs.CV | Millions of hearing impaired people around the world routinely use some
variants of sign languages to communicate, thus the automatic translation of a
sign language is meaningful and important. Currently, there are two
sub-problems in Sign Language Recognition (SLR), i.e., isolated SLR that
recognizes word by word and ... | computer science |
30,956 | Riemannian Walk for Incremental Learning: Understanding Forgetting and
Intransigence | cs.CV | We study incremental learning for the classification task, a key component
for life-long learning systems. For an incremental learning algorithm, the main
challenges are to update the classifier whilst preserving previous knowledge.
In addition to forgetting, a well-known issue while preserving knowledge, we
observe th... | computer science |
30,957 | Image Captioning at Will: A Versatile Scheme for Effectively Injecting
Sentiments into Image Descriptions | cs.CV | Automatic image captioning has recently approached human-level performance
due to the latest advances in computer vision and natural language
understanding. However, most of the current models can only generate plain
factual descriptions about the content of a given image. However, for human
beings, image caption writi... | computer science |
30,958 | Learning Video-Story Composition via Recurrent Neural Network | cs.CV | In this paper, we propose a learning-based method to compose a video-story
from a group of video clips that describe an activity or experience. We learn
the coherence between video clips from real videos via the Recurrent Neural
Network (RNN) that jointly incorporates the spatial-temporal semantics and
motion dynamics ... | computer science |
30,959 | Netizen-Style Commenting on Fashion Photos: Dataset and Diversity
Measures | cs.CV | Recently, deep neural network models have achieved promising results in image
captioning task. Yet, "vanilla" sentences, only describing shallow appearances
(e.g., types, colors), generated by current works are not satisfied netizen
style resulting in lacking engagements, contexts, and user intentions. To
tackle this p... | computer science |
30,960 | Action Recognition with Visual Attention on Skeleton Images | cs.CV | Action recognition with 3D skeleton sequences is becoming popular due to its
speed and robustness. The recently proposed Convolutional Neural Networks (CNN)
based methods have shown good performance in learning spatio-temporal
representations for skeleton sequences. Despite the good recognition accuracy
achieved by pre... | computer science |
30,961 | A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360
Video | cs.CV | We address the problem of highlight detection from a 360 degree video by
summarizing it both spatially and temporally. Given a long 360 degree video, we
spatially select pleasantly-looking normal field-of-view (NFOV) segments from
unlimited field of views (FOV) of the 360 degree video, and temporally
summarize it into ... | computer science |
30,962 | SESR: Single Image Super Resolution with Recursive Squeeze and
Excitation Networks | cs.CV | Single image super resolution is a very important computer vision task, with
a wide range of applications. In recent years, the depth of the
super-resolution model has been constantly increasing, but with a small
increase in performance, it has brought a huge amount of computation and memory
consumption. In this work, ... | computer science |
30,963 | ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep
Learning | cs.CV | Compressive sensing (CS), aiming to reconstruct an image/signal from a small
set of random measurements has attracted considerable attentions in recent
years. Due to the high dimensionality of images, previous CS methods mainly
work on image blocks to avoid the huge requirements of memory and computation,
i.e., image b... | computer science |
30,964 | Fast and Accurate Reconstruction of Compressed Color Light Field | cs.CV | Light field photography has been studied thoroughly in recent years. One of
its drawbacks is the need for multilens in the imaging. To compensate that,
compressed light field photography has been proposed to tackle the tradeoffs
between the spatial and angular resolutions. It obtains using only one lens, a
compressed v... | computer science |
30,965 | A CNN-based Spatial Feature Fusion Algorithm for Hyperspectral Imagery
Classification | cs.CV | The shortage of training samples remains one of the main obstacles in
applying the artificial neural networks (ANN) to the hyperspectral images
classification. To fuse the spatial and spectral information, pixel patches are
often utilized to train a model, which may further aggregate this problem. In
the existing works... | computer science |
30,966 | From Benedict Cumberbatch to Sherlock Holmes: Character Identification
in TV series without a Script | cs.CV | The goal of this paper is the automatic identification of characters in TV
and feature film material. In contrast to standard approaches to this task,
which rely on the weak supervision afforded by transcripts and subtitles, we
propose a new method requiring only a cast list. This list is used to obtain
images of actor... | computer science |
30,967 | Counting Cells in Time-Lapse Microscopy using Deep Neural Networks | cs.CV | An automatic approach to counting any kind of cells could alleviate work of
the experts and boost the research in fields such as regenerative medicine. In
this paper, a method for microscopy cell counting using multiple frames (hence
temporal information) is proposed. Unlike previous approaches where the cell
counting ... | computer science |
30,968 | Recovering from Random Pruning: On the Plasticity of Deep Convolutional
Neural Networks | cs.CV | Recently there has been a lot of work on pruning filters from deep
convolutional neural networks (CNNs) with the intention of reducing
computations. The key idea is to rank the filters based on a certain criterion
(say, $l_1$-norm, average percentage of zeros, etc) and retain only the top
ranked filters. Once the low s... | computer science |
30,969 | Parallel Tracking and Verifying | cs.CV | Being intensively studied, visual object tracking has witnessed great
advances in either speed (e.g., with correlation filters) or accuracy (e.g.,
with deep features). Real-time and high accuracy tracking algorithms, however,
remain scarce. In this paper we study the problem from a new perspective and
present a novel p... | computer science |
30,970 | Densely Dilated Spatial Pooling Convolutional Network using benign loss
functions for imbalanced volumetric prostate segmentation | cs.CV | The high incidence rate of prostate disease poses a requirement in early
detection for diagnosis. As one of the main imaging methods used for prostate
cancer detection, Magnetic Resonance Imaging (MRI) has wide range of appearance
and imbalance problems, making automated prostate segmentation fundamental but
challengin... | computer science |
30,971 | Inference, Learning and Attention Mechanisms that Exploit and Preserve
Sparsity in Convolutional Networks | cs.CV | While CNNs naturally lend themselves to densely sampled data, and
sophisticated implementations are available, they lack the ability to
efficiently process sparse data. In this work we introduce a suite of tools
that exploit sparsity in both the feature maps and the filter weights, and
thereby allow for significantly l... | computer science |
30,972 | In Defense of Classical Image Processing: Fast Depth Completion on the
CPU | cs.CV | With the rise of data driven deep neural networks as a realization of
universal function approximators, most research on computer vision problems has
moved away from hand crafted classical image processing algorithms. This paper
shows that with a well designed algorithm, we are capable of outperforming
neural network b... | computer science |
30,973 | Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver
Prediction | cs.CV | The study and modeling of driver's gaze dynamics is important because, if and
how the driver is monitoring the driving environment is vital for driver
assistance in manual mode, for take-over requests in highly automated mode and
for semantic perception of the surround in fully autonomous mode. We developed
a machine v... | computer science |
30,974 | Improved Image Segmentation via Cost Minimization of Multiple Hypotheses | cs.CV | Image segmentation is an important component of many image understanding
systems. It aims to group pixels in a spatially and perceptually coherent
manner. Typically, these algorithms have a collection of parameters that
control the degree of over-segmentation produced. It still remains a challenge
to properly select su... | computer science |
30,975 | Cross-domain CNN for Hyperspectral Image Classification | cs.CV | In this paper, we address the dataset scarcity issue with the hyperspectral
image classification. As only a few thousands of pixels are available for
training, it is difficult to effectively learn high-capacity Convolutional
Neural Networks (CNNs). To cope with this problem, we propose a novel
cross-domain CNN containi... | computer science |
30,976 | Single Image Reflection Removal Using Deep Encoder-Decoder Network | cs.CV | Image of a scene captured through a piece of transparent and reflective
material, such as glass, is often spoiled by a superimposed layer of reflection
image. While separating the reflection from a familiar object in an image is
mentally not difficult for humans, it is a challenging, ill-posed problem in
computer visio... | computer science |
30,977 | Interpreting CNNs via Decision Trees | cs.CV | This paper presents a method to learn a decision tree to quantitatively
explain the logic of each prediction of a pre-trained convolutional neural
networks (CNNs). Our method boosts the following two aspects of network
interpretability. 1) In the CNN, each filter in a high conv-layer must
represent a specific object pa... | computer science |
30,978 | Semantic White Balance: Semantic Color Constancy Using Convolutional
Neural Network | cs.CV | The goal of computational color constancy is to preserve the perceptive
colors of objects under different lighting conditions by removing the effect of
color casts caused by the scene's illumination. With the rapid development of
deep learning based techniques, significant progress has been made in image
semantic segme... | computer science |
30,979 | Perceptual Compressive Sensing | cs.CV | This paper proposes perceptual compressive sensing. The network is composed
of a fully convolutional measurement and reconstruction network. For the
following contributions, the proposed framework is a breakthrough work.
Firstly, the fully-convolutional network measures the full image which
preserves structure informat... | computer science |
30,980 | Full Image Recover for Block-Based Compressive Sensing | cs.CV | Recent years, compressive sensing (CS) has improved greatly for the
application of deep learning technology. For convenience, the input image is
usually measured and reconstructed block by block. This usually causes block
effect in reconstructed images. In this paper, we present a novel CNN-based
network to solve this ... | computer science |
30,981 | Face Aging with Contextual Generative Adversarial Nets | cs.CV | Face aging, which renders aging faces for an input face, has attracted
extensive attention in the multimedia research. Recently, several conditional
Generative Adversarial Nets (GANs) based methods have achieved great success.
They can generate images fitting the real face distributions conditioned on
each individual a... | computer science |
30,982 | HoloFace: Augmenting Human-to-Human Interactions on HoloLens | cs.CV | We present HoloFace, an open-source framework for face alignment, head pose
estimation and facial attribute retrieval for Microsoft HoloLens. HoloFace
implements two state-of-the-art face alignment methods which can be used
interchangeably: one running locally and one running on a remote backend. Head
pose estimation i... | computer science |
30,983 | Annotation-Free and One-Shot Learning for Instance Segmentation of
Homogeneous Object Clusters | cs.CV | We propose a novel approach for instance segmen- tation given an image of
homogeneous object clus- ter (HOC). Our learning approach is one-shot be- cause
a single video of an object instance is cap- tured and it requires no human
annotation. Our in- tuition is that images of homogeneous objects can be
effectively synth... | computer science |
30,984 | A Fusion of Appearance based CNNs and Temporal evolution of Skeleton
with LSTM for Daily Living Action Recognition | cs.CV | In this paper, we propose efficient method which combines skeleton
information and appearance features for daily-living action recognition. Many
RGB methods focus only on short term temporal information obtained from optical
flow. Skeleton based methods on the other hand show that modeling long term
skeleton evolution ... | computer science |
30,985 | DensePose: Dense Human Pose Estimation In The Wild | cs.CV | In this work, we establish dense correspondences between RGB image and a
surface-based representation of the human body, a task we refer to as dense
human pose estimation. We first gather dense correspondences for 50K persons
appearing in the COCO dataset by introducing an efficient annotation pipeline.
We then use our... | computer science |
30,986 | APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework | cs.CV | Particle picking is a crucial first step in the computational pipeline of
single-particle cryo-electron microscopy (cryo-EM). Selecting particles from
the micrographs is difficult especially for small particles with low contrast.
As high-resolution reconstruction typically requires hundreds of thousands of
particles, m... | computer science |
30,987 | Learning random-walk label propagation for weakly-supervised semantic
segmentation | cs.CV | Large-scale training for semantic segmentation is challenging due to the
expense of obtaining training data for this task relative to other vision
tasks. We propose a novel training approach to address this difficulty. Given
cheaply-obtained sparse image labelings, we propagate the sparse labels to
produce guessed dens... | computer science |
30,988 | A New Registration Approach for Dynamic Analysis of Calcium Signals in
Organs | cs.CV | Wing disc pouches of fruit flies are a powerful genetic model for studying
physiological intercellular calcium ($Ca^{2+}$) signals for dynamic analysis of
cell signaling in organ development and disease studies. A key to analyzing
spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the
pouches ac... | computer science |
30,989 | Learning Semantic Segmentation with Diverse Supervision | cs.CV | Models based on deep convolutional neural networks (CNN) have significantly
improved the performance of semantic segmentation. However, learning these
models requires a large amount of training images with pixel-level labels,
which are very costly and time-consuming to collect. In this paper, we propose
a method for le... | computer science |
30,990 | Complex Network Classification with Convolutional Neural Network | cs.CV | Classifying large scale networks into several categories and distinguishing
them according to their fine structures is of great importance with several
applications in real life. However, most studies of complex networks focus on
properties of a single network but seldom on classification, clustering, and
comparison be... | computer science |
30,991 | ExpNet: Landmark-Free, Deep, 3D Facial Expressions | cs.CV | We describe a deep learning based method for estimating 3D facial expression
coefficients. Unlike previous work, our process does not relay on facial
landmark detection methods as a proxy step. Recent methods have shown that a
CNN can be trained to regress accurate and discriminative 3D morphable model
(3DMM) represent... | computer science |
30,992 | Detecting Zones and Threat on 3D Body for Security in Airports using
Deep Machine Learning | cs.CV | In this research, it was used a segmentation and classification method to
identify threat recognition in human scanner images of airport security. The
Department of Homeland Security's (DHS) in USA has a higher false alarm,
produced from theirs algorithms using today's scanners at the airports. To
repair this problem t... | computer science |
30,993 | Visual Interpretability for Deep Learning: a Survey | cs.CV | This paper reviews recent studies in understanding neural-network
representations and learning neural networks with interpretable/disentangled
middle-layer representations. Although deep neural networks have exhibited
superior performance in various tasks, the interpretability is always the
Achilles' heel of deep neura... | computer science |
30,994 | Activity-conditioned continuous human pose estimation for performance
analysis of athletes using the example of swimming | cs.CV | In this paper we consider the problem of human pose estimation in real-world
videos of swimmers. Swimming channels allow filming swimmers simultaneously
above and below the water surface with a single stationary camera. These
recordings can be used to quantitatively assess the athletes' performance. The
quantitative ev... | computer science |
30,995 | Handwritten Isolated Bangla Compound Character Recognition: a new
benchmark using a novel deep learning approach | cs.CV | In this work, a novel deep learning technique for the recognition of
handwritten Bangla isolated compound character is presented and a new benchmark
of recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy
layer wise training of Deep Neural Network has helped to make significant
strides in various pa... | computer science |
30,996 | Explaining First Impressions: Modeling, Recognizing, and Explaining
Apparent Personality from Videos | cs.CV | Explainability and interpretability are two critical aspects of decision
support systems. Within computer vision, they are critical in certain tasks
related to human behavior analysis such as in health care applications. Despite
their importance, it is only recently that researchers are starting to explore
these aspect... | computer science |
30,997 | Deep Convolutional Neural Networks for Breast Cancer Histology Image
Analysis | cs.CV | Breast cancer is one of the main causes of cancer death worldwide. Early
diagnostics significantly increases the chances of correct treatment and
survival, but this process is tedious and often leads to a disagreement between
pathologists. Computer-aided diagnosis systems showed potential for improving
the diagnostic a... | computer science |
30,998 | Learning Attribute Representation for Human Activity Recognition | cs.CV | Attribute representations became relevant in image recognition and word
spotting, providing support under the presence of unbalance and disjoint
datasets. However, for human activity recognition using sequential data from
on-body sensors, human-labeled attributes are lacking. This paper introduces a
search for attribut... | computer science |
30,999 | No Modes left behind: Capturing the data distribution effectively using
GANs | cs.CV | Generative adversarial networks (GANs) while being very versatile in
realistic image synthesis, still are sensitive to the input distribution. Given
a set of data that has an imbalance in the distribution, the networks are
susceptible to missing modes and not capturing the data distribution. While
various methods have ... | computer science |
31,000 | Green Stability Assumption: Unsupervised Learning for Statistics-Based
Illumination Estimation | cs.CV | In the image processing pipeline of almost every digital camera there is a
part dedicated to computational color constancy i.e. to removing the influence
of illumination on the colors of the image scene. Some of the best known
illumination estimation methods are the so called statistics-based methods.
They are less acc... | computer science |
31,001 | Incremental Classifier Learning with Generative Adversarial Networks | cs.CV | In this paper, we address the incremental classifier learning problem, which
suffers from catastrophic forgetting. The main reason for catastrophic
forgetting is that the past data are not available during learning. Typical
approaches keep some exemplars for the past classes and use distillation
regularization to retai... | computer science |
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