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28,302 | Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning | cs.CV | Recent progress has been made in using attention based encoder-decoder
framework for video captioning. However, most existing decoders apply the
attention mechanism to every generated word including both visual words (e.g.,
"gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these
non-visual words ca... | computer science |
28,303 | Learning Structured Semantic Embeddings for Visual Recognition | cs.CV | Numerous embedding models have been recently explored to incorporate semantic
knowledge into visual recognition. Existing methods typically focus on
minimizing the distance between the corresponding images and texts in the
embedding space but do not explicitly optimize the underlying structure. Our
key observation is t... | computer science |
28,304 | ToPs: Ensemble Learning with Trees of Predictors | cs.CV | We present a new approach to ensemble learning. Our approach constructs a
tree of subsets of the feature space and associates a predictor (predictive
model) - determined by training one of a given family of base learners on an
endogenously determined training set - to each node of the tree; we call the
resulting object... | computer science |
28,305 | Visual Interaction Networks | cs.CV | From just a glance, humans can make rich predictions about the future state
of a wide range of physical systems. On the other hand, modern approaches from
engineering, robotics, and graphics are often restricted to narrow domains and
require direct measurements of the underlying states. We introduce the Visual
Interact... | computer science |
28,306 | Visual attention models for scene text recognition | cs.CV | In this paper we propose an approach to lexicon-free recognition of text in
scene images. Our approach relies on a LSTM-based soft visual attention model
learned from convolutional features. A set of feature vectors are derived from
an intermediate convolutional layer corresponding to different areas of the
image. This... | computer science |
28,307 | Geometric Multi-Model Fitting with a Convex Relaxation Algorithm | cs.CV | We propose a novel method to fit and segment multi-structural data via convex
relaxation. Unlike greedy methods --which maximise the number of inliers-- this
approach efficiently searches for a soft assignment of points to models by
minimising the energy of the overall classification. Our approach is similar to
state-o... | computer science |
28,308 | Global-Local Airborne Mapping (GLAM): Reconstructing a City from Aerial
Videos | cs.CV | Monocular visual SLAM has become an attractive practical approach for robot
localization and 3D environment mapping, since cameras are small, lightweight,
inexpensive, and produce high-rate, high-resolution data streams. Although
numerous robust tools have been developed, most existing systems are designed
to operate i... | computer science |
28,309 | Volume Calculation of CT lung Lesions based on Halton Low-discrepancy
Sequences | cs.CV | Volume calculation from the Computed Tomography (CT) lung lesions data is a
significant parameter for clinical diagnosis. The volume is widely used to
assess the severity of the lung nodules and track its progression, however, the
accuracy and efficiency of previous studies are not well achieved for clinical
uses. It r... | computer science |
28,310 | A Minimal Solution for Two-view Focal-length Estimation using Two Affine
Correspondences | cs.CV | A minimal solution using two affine correspondences is presented to estimate
the common focal length and the fundamental matrix between two semi-calibrated
cameras - known intrinsic parameters except a common focal length. To the best
of our knowledge, this problem is unsolved. The proposed approach extends point
corre... | computer science |
28,311 | Compression Fractures Detection on CT | cs.CV | The presence of a vertebral compression fracture is highly indicative of
osteoporosis and represents the single most robust predictor for development of
a second osteoporotic fracture in the spine or elsewhere. Less than one third
of vertebral compression fractures are diagnosed clinically. We present an
automated meth... | computer science |
28,312 | Deep Alignment Network: A convolutional neural network for robust face
alignment | cs.CV | In this paper, we propose Deep Alignment Network (DAN), a robust face
alignment method based on a deep neural network architecture. DAN consists of
multiple stages, where each stage improves the locations of the facial
landmarks estimated by the previous stage. Our method uses entire face images
at all stages, contrary... | computer science |
28,313 | Understanding and Eliminating the Large-kernel Effect in Blind
Deconvolution | cs.CV | Blind deconvolution consists of recovering a clear version of an observed
blurry image without specific knowledge of the degradation kernel. The kernel
size, however, is a required hyper-parameter that defines the range of the
support domain. In this study, we experimentally and theoretically show how
large kernel size... | computer science |
28,314 | SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image
Segmentation | cs.CV | Inspired by classic generative adversarial networks (GAN), we propose a novel
end-to-end adversarial neural network, called SegAN, for the task of medical
image segmentation. Since image segmentation requires dense, pixel-level
labeling, the single scalar real/fake output of a classic GAN's discriminator
may be ineffec... | computer science |
28,315 | Face Alignment Using K-Cluster Regression Forests With Weighted
Splitting | cs.CV | In this work we present a face alignment pipeline based on two novel methods:
weighted splitting for K-cluster Regression Forests and 3D Affine Pose
Regression for face shape initialization. Our face alignment method is based on
the Local Binary Feature framework, where instead of standard regression
forests and pixel ... | computer science |
28,316 | Added value of morphological features to breast lesion diagnosis in
ultrasound | cs.CV | Ultrasound imaging plays an important role in breast lesion differentiation.
However, diagnostic accuracy depends on ultrasonographer experience. Various
computer aided diagnosis systems has been developed to improve breast cancer
detection and reduce the number of unnecessary biopsies. In this study, our aim
was to im... | computer science |
28,317 | StreetStyle: Exploring world-wide clothing styles from millions of
photos | cs.CV | Each day billions of photographs are uploaded to photo-sharing services and
social media platforms. These images are packed with information about how
people live around the world. In this paper we exploit this rich trove of data
to understand fashion and style trends worldwide. We present a framework for
visual discov... | computer science |
28,318 | Full Quantification of Left Ventricle via Deep Multitask Learning
Network Respecting Intra- and Inter-Task Relatedness | cs.CV | Cardiac left ventricle (LV) quantification is among the most clinically
important tasks for identification and diagnosis of cardiac diseases, yet still
a challenge due to the high variability of cardiac structure and the complexity
of temporal dynamics. Full quantification, i.e., to simultaneously quantify all
LV indic... | computer science |
28,319 | Deep Convolutional Decision Jungle for Image Classification | cs.CV | We propose a novel method called deep convolutional decision jungle (CDJ) and
its learning algorithm for image classification. The CDJ maintains the
structure of standard convolutional neural networks (CNNs), i.e. multiple
layers of multiple response maps fully connected. Each response map-or node-in
both the convoluti... | computer science |
28,320 | Imposing Hard Constraints on Deep Networks: Promises and Limitations | cs.CV | Imposing constraints on the output of a Deep Neural Net is one way to improve
the quality of its predictions while loosening the requirements for labeled
training data. Such constraints are usually imposed as soft constraints by
adding new terms to the loss function that is minimized during training. An
alternative is ... | computer science |
28,321 | Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images | cs.CV | A method for automatically quantifying emphysema regions using
High-Resolution Computed Tomography (HRCT) scans of patients with chronic
obstructive pulmonary disease (COPD) that does not require manually annotated
scans for training is presented. HRCT scans of controls and of COPD patients
with diverse disease severit... | computer science |
28,322 | Unsupervised Place Discovery for Place-Specific Change Classifier | cs.CV | In this study, we address the problem of supervised change detection for
robotic map learning applications, in which the aim is to train a
place-specific change classifier (e.g., support vector machine (SVM)) to
predict changes from a robot's view image. An open question is the manner in
which to partition a robot's wo... | computer science |
28,323 | Early Experiences with Crowdsourcing Airway Annotations in Chest CT | cs.CV | Measuring airways in chest computed tomography (CT) images is important for
characterizing diseases such as cystic fibrosis, yet very time-consuming to
perform manually. Machine learning algorithms offer an alternative, but need
large sets of annotated data to perform well. We investigate whether
crowdsourcing can be u... | computer science |
28,324 | DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data | cs.CV | A class of recent approaches for generating images, called Generative
Adversarial Networks (GAN), have been used to generate impressively realistic
images of objects, bedrooms, handwritten digits and a variety of other image
modalities. However, typical GAN-based approaches require large amounts of
training data to cap... | computer science |
28,325 | BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with
Fully Convolutional Networks | cs.CV | We present a simple and effective framework for simultaneous semantic
segmentation and instance segmentation with Fully Convolutional Networks
(FCNs). The method, called BiSeg, predicts instance segmentation as a posterior
in Bayesian inference, where semantic segmentation is used as a prior. We
extend the idea of posi... | computer science |
28,326 | Synthesizing Filamentary Structured Images with GANs | cs.CV | This paper aims at synthesizing filamentary structured images such as retinal
fundus images and neuronal images, as follows: Given a ground-truth, to
generate multiple realistic looking phantoms. A ground-truth could be a binary
segmentation map containing the filamentary structured morphology, while the
synthesized ou... | computer science |
28,327 | Incorporating Network Built-in Priors in Weakly-supervised Semantic
Segmentation | cs.CV | Pixel-level annotations are expensive and time consuming to obtain. Hence,
weak supervision using only image tags could have a significant impact in
semantic segmentation. Recently, CNN-based methods have proposed to fine-tune
pre-trained networks using image tags. Without additional information, this
leads to poor loc... | computer science |
28,328 | CoMaL Tracking: Tracking Points at the Object Boundaries | cs.CV | Traditional point tracking algorithms such as the KLT use local 2D
information aggregation for feature detection and tracking, due to which their
performance degrades at the object boundaries that separate multiple objects.
Recently, CoMaL Features have been proposed that handle such a case. However,
they proposed a si... | computer science |
28,329 | Active Learning for Structured Prediction from Partially Labeled Data | cs.CV | We propose a general purpose active learning algorithm for structured
prediction, gathering labeled data for training a model that outputs a set of
related labels for an image or video. Active learning starts with a limited
initial training set, then iterates querying a user for labels on unlabeled
data and retraining ... | computer science |
28,330 | PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric
Space | cs.CV | Few prior works study deep learning on point sets. PointNet by Qi et al. is a
pioneer in this direction. However, by design PointNet does not capture local
structures induced by the metric space points live in, limiting its ability to
recognize fine-grained patterns and generalizability to complex scenes. In this
work,... | computer science |
28,331 | Evaluating (and improving) the correspondence between deep neural
networks and human representations | cs.CV | Decades of psychological research have been aimed at modeling how people
learn features and categories. The empirical validation of these theories is
often based on artificial stimuli with simple representations. Recently, deep
neural networks have reached or surpassed human accuracy on tasks such as
identifying object... | computer science |
28,332 | C-arm Tomographic Imaging Technique for Nephrolithiasis and Detection of
Kidney Stones | cs.CV | In this paper, we investigated a C-arm tomographic technique as a new three
dimensional (3D) kidney imaging method for nephrolithiasis and kidney stone
detection over view angle less than 180o. Our C-arm tomographic technique
provides a series of two dimensional (2D) images with a single scan over 40o
view angle. Exper... | computer science |
28,333 | Image Captioning with Object Detection and Localization | cs.CV | Automatically generating a natural language description of an image is a task
close to the heart of image understanding. In this paper, we present a
multi-model neural network method closely related to the human visual system
that automatically learns to describe the content of images. Our model consists
of two sub-mod... | computer science |
28,334 | Automatic tracking of vessel-like structures from a single starting
point | cs.CV | The identification of vascular networks is an important topic in the medical
image analysis community. While most methods focus on single vessel tracking,
the few solutions that exist for tracking complete vascular networks are
usually computationally intensive and require a lot of user interaction. In
this paper we pr... | computer science |
28,335 | Learning Deep Representations for Scene Labeling with Semantic Context
Guided Supervision | cs.CV | Scene labeling is a challenging classification problem where each input image
requires a pixel-level prediction map. Recently, deep-learning-based methods
have shown their effectiveness on solving this problem. However, we argue that
the large intra-class variation provides ambiguous training information and
hinders th... | computer science |
28,336 | ToxTrac: a fast and robust software for tracking organisms | cs.CV | 1. Behavioral analysis based on video recording is becoming increasingly
popular within research fields such as; ecology, medicine, ecotoxicology, and
toxicology. However, the programs available to analyze the data, which are;
free of cost, user-friendly, versatile, robust, fast and provide reliable
statistics for diff... | computer science |
28,337 | An Efficient Approach for Object Detection and Tracking of Objects in a
Video with Variable Background | cs.CV | This paper proposes a novel approach to create an automated visual
surveillance system which is very efficient in detecting and tracking moving
objects in a video captured by moving camera without any apriori information
about the captured scene. Separating foreground from the background is
challenging job in videos ca... | computer science |
28,338 | Structured Light Phase Measuring Profilometry Pattern Design for Binary
Spatial Light Modulators | cs.CV | Structured light illumination is an active 3-D scanning technique based on
projecting/capturing a set of striped patterns and measuring the warping of the
patterns as they reflect off a target object's surface. In the case of phase
measuring profilometry (PMP), the projected patterns are composed of a rolling
sinusoida... | computer science |
28,339 | Causes and Corrections for Bimodal Multipath Scanning with Structured
Light | cs.CV | Structured light illumination is an active 3-D scanning technique based on
projecting/capturing a set of striped patterns and measuring the warping of the
patterns as they reflect off a target object's surface. As designed, each pixel
in the camera sees exactly one pixel from the projector; however, there are
exception... | computer science |
28,340 | CortexNet: a Generic Network Family for Robust Visual Temporal
Representations | cs.CV | In the past five years we have observed the rise of incredibly well
performing feed-forward neural networks trained supervisedly for vision related
tasks. These models have achieved super-human performance on object
recognition, localisation, and detection in still images. However, there is a
need to identify the best ... | computer science |
28,341 | Face Detection through Scale-Friendly Deep Convolutional Networks | cs.CV | In this paper, we share our experience in designing a convolutional
network-based face detector that could handle faces of an extremely wide range
of scales. We show that faces with different scales can be modeled through a
specialized set of deep convolutional networks with different structures. These
detectors can be... | computer science |
28,342 | Class-specific Poisson denoising by patch-based importance sampling | cs.CV | In this paper, we address the problem of recovering images degraded by
Poisson noise, where the image is known to belong to a specific class. In the
proposed method, a dataset of clean patches from images of the class of
interest is clustered using multivariate Gaussian distributions. In order to
recover the noisy imag... | computer science |
28,343 | Learning to Learn from Noisy Web Videos | cs.CV | Understanding the simultaneously very diverse and intricately fine-grained
set of possible human actions is a critical open problem in computer vision.
Manually labeling training videos is feasible for some action classes but
doesn't scale to the full long-tailed distribution of actions. A promising way
to address this... | computer science |
28,344 | DCCO: Towards Deformable Continuous Convolution Operators | cs.CV | Discriminative Correlation Filter (DCF) based methods have shown competitive
performance on tracking benchmarks in recent years. Generally, DCF based
trackers learn a rigid appearance model of the target. However, this reliance
on a single rigid appearance model is insufficient in situations where the
target undergoes ... | computer science |
28,345 | MirBot, a collaborative object recognition system for smartphones using
convolutional neural networks | cs.CV | MirBot is a collaborative application for smartphones that allows users to
perform object recognition. This app can be used to take a photograph of an
object, select the region of interest and obtain the most likely class (dog,
chair, etc.) by means of similarity search using features extracted from a
convolutional neu... | computer science |
28,346 | An Ensemble Deep Learning Based Approach for Red Lesion Detection in
Fundus Images | cs.CV | Diabetic retinopathy is one of the leading causes of preventable blindness in
the world. Its earliest sign are red lesions, a general term that groups both
microaneurysms and hemorrhages. In daily clinical practice, these lesions are
manually detected by physicians using fundus photographs. However, this task is
tediou... | computer science |
28,347 | Manifold Regularized Slow Feature Analysis for Dynamic Texture
Recognition | cs.CV | Dynamic textures exist in various forms, e.g., fire, smoke, and traffic jams,
but recognizing dynamic texture is challenging due to the complex temporal
variations. In this paper, we present a novel approach stemmed from slow
feature analysis (SFA) for dynamic texture recognition. SFA extracts slowly
varying features f... | computer science |
28,348 | Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action
Detection | cs.CV | Despite significant progress in the development of human action detection
datasets and algorithms, no current dataset is representative of real-world
aerial view scenarios. We present Okutama-Action, a new video dataset for
aerial view concurrent human action detection. It consists of 43 minute-long
fully-annotated seq... | computer science |
28,349 | Unsupervised Adaptive Re-identification in Open World Dynamic Camera
Networks | cs.CV | Person re-identification is an open and challenging problem in computer
vision. Existing approaches have concentrated on either designing the best
feature representation or learning optimal matching metrics in a static setting
where the number of cameras are fixed in a network. Most approaches have
neglected the dynami... | computer science |
28,350 | Collaborative Summarization of Topic-Related Videos | cs.CV | Large collections of videos are grouped into clusters by a topic keyword,
such as Eiffel Tower or Surfing, with many important visual concepts repeating
across them. Such a topically close set of videos have mutual influence on each
other, which could be used to summarize one of them by exploiting information
from othe... | computer science |
28,351 | Multi-View Surveillance Video Summarization via Joint Embedding and
Sparse Optimization | cs.CV | Most traditional video summarization methods are designed to generate
effective summaries for single-view videos, and thus they cannot fully exploit
the complicated intra and inter-view correlations in summarizing multi-view
videos in a camera network. In this paper, with the aim of summarizing
multi-view videos, we in... | computer science |
28,352 | Diversity-aware Multi-Video Summarization | cs.CV | Most video summarization approaches have focused on extracting a summary from
a single video; we propose an unsupervised framework for summarizing a
collection of videos. We observe that each video in the collection may contain
some information that other videos do not have, and thus exploring the
underlying complement... | computer science |
28,353 | Measurement-Adaptive Sparse Image Sampling and Recovery | cs.CV | This paper presents an adaptive and intelligent sparse model for digital
image sampling and recovery. In the proposed sampler, we adaptively determine
the number of required samples for retrieving image based on
space-frequency-gradient information content of image patches. By leveraging
texture in space, sparsity loca... | computer science |
28,354 | Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D
Electron Microscopy | cs.CV | The most sophisticated existing methods to generate 3D isotropic
super-resolution (SR) from non-isotropic electron microscopy (EM) are based on
learned dictionaries. Unfortunately, none of the existing methods generate
practically satisfying results. For 2D natural images, recently developed
super-resolution methods th... | computer science |
28,355 | Deep Adaptive Feature Embedding with Local Sample Distributions for
Person Re-identification | cs.CV | Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feat... | computer science |
28,356 | Direct detection of pixel-level myocardial infarction areas via a
deep-learning algorithm | cs.CV | Accurate detection of the myocardial infarction (MI) area is crucial for
early diagnosis planning and follow-up management. In this study, we propose an
end-to-end deep-learning algorithm framework (OF-RNN ) to accurately detect the
MI area at the pixel level. Our OF-RNN consists of three different function
layers: the... | computer science |
28,357 | Generate Identity-Preserving Faces by Generative Adversarial Networks | cs.CV | Generating identity-preserving faces aims to generate various face images
keeping the same identity given a target face image. Although considerable
generative models have been developed in recent years, it is still challenging
to simultaneously acquire high quality of facial images and preserve the
identity. Here we p... | computer science |
28,358 | Recovering 6D Object Pose: Multi-modal Analyses on Challenges | cs.CV | A large number of studies analyse object detection and pose estimation at
visual level in 2D, discussing the effects of challenges such as occlusion,
clutter, texture, etc., on the performances of the methods, which work in the
context of RGB modality. Interpreting the depth data, the study in this paper
presents thoro... | computer science |
28,359 | Bicycle Detection Based On Multi-feature and Multi-frame Fusion in
low-resolution traffic videos | cs.CV | As a major type of transportation equipments, bicycles, including electrical
bicycles, are distributed almost everywhere in China. The accidents caused by
bicycles have become a serious threat to the public safety. So bicycle
detection is one major task of traffic video surveillance systems in China. In
this paper, a m... | computer science |
28,360 | Style Transfer for Anime Sketches with Enhanced Residual U-net and
Auxiliary Classifier GAN | cs.CV | Recently, with the revolutionary neural style transferring methods,
creditable paintings can be synthesized automatically from content images and
style images. However, when it comes to the task of applying a painting's style
to an anime sketch, these methods will just randomly colorize sketch lines as
outputs and fail... | computer science |
28,361 | A dynamic graph-cuts method with integrated multiple feature maps for
segmenting kidneys in ultrasound images | cs.CV | Purpose: To improve kidney segmentation in clinical ultrasound (US) images,
we develop a new graph cuts based method to segment kidney US images by
integrating original image intensity information and texture feature maps
extracted using Gabor filters. Methods: To handle large appearance variation
within kidney images ... | computer science |
28,362 | PatternNet: A Benchmark Dataset for Performance Evaluation of Remote
Sensing Image Retrieval | cs.CV | Remote sensing image retrieval(RSIR), which aims to efficiently retrieve data
of interest from large collections of remote sensing data, is a fundamental
task in remote sensing. Over the past several decades, there has been
significant effort to extract powerful feature representations for this task
since the retrieval... | computer science |
28,363 | Modeling Multi-Object Configurations via Medial/Skeletal Linking
Structures | cs.CV | We introduce a method for modeling a configuration of objects in 2D or 3D
images using a mathematical "skeletal linking structure" which will
simultaneously capture the individual shape features of the objects and their
positional information relative to one another. The objects may either have
smooth boundaries and be... | computer science |
28,364 | Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model | cs.CV | With the goal of making high-resolution forecasts of regional rainfall,
precipitation nowcasting has become an important and fundamental technology
underlying various public services ranging from rainstorm warnings to flight
safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to
outperform traditio... | computer science |
28,365 | Few-Shot Image Recognition by Predicting Parameters from Activations | cs.CV | In this paper, we are interested in the few-shot learning problem. In
particular, we focus on a challenging scenario where the number of categories
is large and the number of examples per novel category is very limited, e.g. 1,
2, or 3. Motivated by the close relationship between the parameters and the
activations in a... | computer science |
28,366 | Exploring the similarity of medical imaging classification problems | cs.CV | Supervised learning is ubiquitous in medical image analysis. In this paper we
consider the problem of meta-learning -- predicting which methods will perform
well in an unseen classification problem, given previous experience with other
classification problems. We investigate the first step of such an approach: how
to q... | computer science |
28,367 | Point Linking Network for Object Detection | cs.CV | Object detection is a core problem in computer vision. With the development
of deep ConvNets, the performance of object detectors has been dramatically
improved. The deep ConvNets based object detectors mainly focus on regressing
the coordinates of bounding box, e.g., Faster-R-CNN, YOLO and SSD. Different
from these me... | computer science |
28,368 | Image Crowd Counting Using Convolutional Neural Network and Markov
Random Field | cs.CV | In this paper, we propose a method called Convolutional Neural Network-Markov
Random Field (CNN-MRF) to estimate the crowd count in a still image. We first
divide the dense crowd visible image into overlapping patches and then use a
deep convolutional neural network to extract features from each patch image,
followed b... | computer science |
28,369 | Progressive and Multi-Path Holistically Nested Neural Networks for
Pathological Lung Segmentation from CT Images | cs.CV | Pathological lung segmentation (PLS) is an important, yet challenging,
medical image application due to the wide variability of pathological lung
appearance and shape. Because PLS is often a pre-requisite for other imaging
analytics, methodological simplicity and generality are key factors in
usability. Along those lin... | computer science |
28,370 | Transferring a Semantic Representation for Person Re-Identification and
Search | cs.CV | Learning semantic attributes for person re-identification and
description-based person search has gained increasing interest due to
attributes' great potential as a pose and view-invariant representation.
However, existing attribute-centric approaches have thus far underperformed
state-of-the-art conventional approache... | computer science |
28,371 | Criteria Sliders: Learning Continuous Database Criteria via Interactive
Ranking | cs.CV | Large databases are often organized by hand-labeled metadata, or criteria,
which are expensive to collect. We can use unsupervised learning to model
database variation, but these models are often high dimensional, complex to
parameterize, or require expert knowledge. We learn low-dimensional continuous
criteria via int... | computer science |
28,372 | Can We See Photosynthesis? Magnifying the Tiny Color Changes of Plant
Green Leaves Using Eulerian Video Magnification | cs.CV | Plant aliveness is proven through laboratory experiments and special
scientific instruments. In this paper, we aim to detect the degree of animation
of plants based on the magnification of the small color changes in the plant's
green leaves using the Eulerian video magnification. Capturing the video under
a controlled ... | computer science |
28,373 | Contrast Enhancement Estimation for Digital Image Forensics | cs.CV | Inconsistency in contrast enhancement can be used to expose image forgeries.
In this work, we describe a new method to estimate contrast enhancement from a
single image. Our method takes advantage of the nature of contrast enhancement
as a mapping between pixel values, and the distinct characteristics it
introduces to ... | computer science |
28,374 | Deep Control - a simple automatic gain control for memory efficient and
high performance training of deep convolutional neural networks | cs.CV | Training a deep convolutional neural net typically starts with a random
initialisation of all filters in all layers which severely reduces the forward
signal and back-propagated error and leads to slow and sub-optimal training.
Techniques that counter that focus on either increasing the signal or
increasing the gradien... | computer science |
28,375 | Long-Term Video Interpolation with Bidirectional Predictive Network | cs.CV | This paper considers the challenging task of long-term video interpolation.
Unlike most existing methods that only generate few intermediate frames between
existing adjacent ones, we attempt to speculate or imagine the procedure of an
episode and further generate multiple frames between two non-consecutive frames
in vi... | computer science |
28,376 | Text Extraction From Texture Images Using Masked Signal Decomposition | cs.CV | Text extraction is an important problem in image processing with applications
from optical character recognition to autonomous driving. Most of the
traditional text segmentation algorithms consider separating text from a simple
background (which usually has a different color from texts). In this work we
consider separa... | computer science |
28,377 | Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment | cs.CV | Obesity treatment requires obese patients to record all food intakes per day.
Computer vision has been introduced to estimate calories from food images. In
order to increase accuracy of detection and reduce the error of volume
estimation in food calorie estimation, we present our calorie estimation method
in this paper... | computer science |
28,378 | Joint Max Margin and Semantic Features for Continuous Event Detection in
Complex Scenes | cs.CV | In this paper the problem of complex event detection in the continuous domain
(i.e. events with unknown starting and ending locations) is addressed. Existing
event detection methods are limited to features that are extracted from the
local spatial or spatio-temporal patches from the videos. However, this makes
the mode... | computer science |
28,379 | Video Imagination from a Single Image with Transformation Generation | cs.CV | In this work, we focus on a challenging task: synthesizing multiple imaginary
videos given a single image. Major problems come from high dimensionality of
pixel space and the ambiguity of potential motions. To overcome those problems,
we propose a new framework that produce imaginary videos by transformation
generation... | computer science |
28,380 | Online Convolutional Dictionary Learning for Multimodal Imaging | cs.CV | Computational imaging methods that can exploit multiple modalities have the
potential to enhance the capabilities of traditional sensing systems. In this
paper, we propose a new method that reconstructs multimodal images from their
linear measurements by exploiting redundancies across different modalities. Our
method c... | computer science |
28,381 | The "something something" video database for learning and evaluating
visual common sense | cs.CV | Neural networks trained on datasets such as ImageNet have led to major
advances in visual object classification. One obstacle that prevents networks
from reasoning more deeply about complex scenes and situations, and from
integrating visual knowledge with natural language, like humans do, is their
lack of common sense ... | computer science |
28,382 | von Mises-Fisher Mixture Model-based Deep learning: Application to Face
Verification | cs.CV | A number of pattern recognition tasks, \textit{e.g.}, face verification, can
be boiled down to classification or clustering of unit length directional
feature vectors whose distance can be simply computed by their angle. In this
paper, we propose the von Mises-Fisher (vMF) mixture model as the theoretical
foundation fo... | computer science |
28,383 | Action Search: Learning to Search for Human Activities in Untrimmed
Videos | cs.CV | Traditional approaches for action detection use trimmed data to learn
sophisticated action detector models. Although these methods have achieved
great success at detecting human actions, we argue that huge information is
discarded when ignoring the process, through which this trimmed data is
obtained. In this paper, we... | computer science |
28,384 | AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of
Isolated Facial Features and Foggy Faces | cs.CV | Gender classification aims at recognizing a person's gender. Despite the high
accuracy achieved by state-of-the-art methods for this task, there is still
room for improvement in generalized and unrestricted datasets. In this paper,
we advocate a new strategy inspired by the behavior of humans in gender
recognition. Ins... | computer science |
28,385 | When Image Denoising Meets High-Level Vision Tasks: A Deep Learning
Approach | cs.CV | Conventionally, image denoising and high-level vision tasks are handled
separately in computer vision, and their connection is fragile. In this paper,
we cope with the two jointly and explore the mutual influence between them with
the focus on two questions, namely (1) how image denoising can help solving
high-level vi... | computer science |
28,386 | Saliency detection by aggregating complementary background template with
optimization framework | cs.CV | This paper proposes an unsupervised bottom-up saliency detection approach by
aggregating complementary background template with refinement. Feature vectors
are extracted from each superpixel to cover regional color, contrast and
texture information. By using these features, a coarse detection for salient
region is real... | computer science |
28,387 | Accurate Pulmonary Nodule Detection in Computed Tomography Images Using
Deep Convolutional Neural Networks | cs.CV | Early detection of pulmonary cancer is the most promising way to enhance a
patient's chance for survival. Accurate pulmonary nodule detection in computed
tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In
this paper, inspired by the successful use of deep convolutional neural
networks (DCNNs) i... | computer science |
28,388 | Photo-realistic Facial Texture Transfer | cs.CV | Style transfer methods have achieved significant success in recent years with
the use of convolutional neural networks. However, many of these methods
concentrate on artistic style transfer with few constraints on the output image
appearance. We address the challenging problem of transferring face texture
from a style ... | computer science |
28,389 | Hierarchical Gaussian Descriptors with Application to Person
Re-Identification | cs.CV | Describing the color and textural information of a person image is one of the
most crucial aspects of person re-identification (re-id). In this paper, we
present novel meta-descriptors based on a hierarchical distribution of pixel
features. Although hierarchical covariance descriptors have been successfully
applied to ... | computer science |
28,390 | Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection | cs.CV | We propose a convolution neural network based algorithm for simultaneously
diagnosing diabetic retinopathy and highlighting suspicious regions. Our
contributions are two folds: 1) a network termed Zoom-in-Net which mimics the
zoom-in process of a clinician to examine the retinal images. Trained with only
image-level su... | computer science |
28,391 | Shape-Color Differential Moment Invariants under Affine Transformations | cs.CV | We propose the general construction formula of shape-color primitives by
using partial differentials of each color channel in this paper. By using all
kinds of shape-color primitives, shape-color differential moment invariants can
be constructed very easily, which are invariant to the shape affine and color
affine tran... | computer science |
28,392 | Alignment Distances on Systems of Bags | cs.CV | Recent research in image and video recognition indicates that many visual
processes can be thought of as being generated by a time-varying generative
model. A nearby descriptive model for visual processes is thus a statistical
distribution that varies over time. Specifically, modeling visual processes as
streams of his... | computer science |
28,393 | SalProp: Salient object proposals via aggregated edge cues | cs.CV | In this paper, we propose a novel object proposal generation scheme by
formulating a graph-based salient edge classification framework that utilizes
the edge context. In the proposed method, we construct a Bayesian probabilistic
edge map to assign a saliency value to the edgelets by exploiting low level
edge features. ... | computer science |
28,394 | Large-Scale YouTube-8M Video Understanding with Deep Neural Networks | cs.CV | Video classification problem has been studied many years. The success of
Convolutional Neural Networks (CNN) in image recognition tasks gives a powerful
incentive for researchers to create more advanced video classification
approaches. As video has a temporal content Long Short Term Memory (LSTM)
networks become handy ... | computer science |
28,395 | Learning without Prejudice: Avoiding Bias in Webly-Supervised Action
Recognition | cs.CV | Webly-supervised learning has recently emerged as an alternative paradigm to
traditional supervised learning based on large-scale datasets with manual
annotations. The key idea is that models such as CNNs can be learned from the
noisy visual data available on the web. In this work we aim to exploit web data
for video u... | computer science |
28,396 | A New Adaptive Video Super-Resolution Algorithm With Improved Robustness
to Innovations | cs.CV | In this paper, a new video super-resolution reconstruction (SRR) method with
improved robustness to outliers is proposed. Although the R-LMS is one of the
SRR algorithms with the best reconstruction quality for its computational cost,
and is naturally robust to registration inaccuracies, its performance is known
to deg... | computer science |
28,397 | Recent Progress of Face Image Synthesis | cs.CV | Face synthesis has been a fascinating yet challenging problem in computer
vision and machine learning. Its main research effort is to design algorithms
to generate photo-realistic face images via given semantic domain. It has been
a crucial prepossessing step of main-stream face recognition approaches and an
excellent ... | computer science |
28,398 | Suggestive Annotation: A Deep Active Learning Framework for Biomedical
Image Segmentation | cs.CV | Image segmentation is a fundamental problem in biomedical image analysis.
Recent advances in deep learning have achieved promising results on many
biomedical image segmentation benchmarks. However, due to large variations in
biomedical images (different modalities, image settings, objects, noise, etc),
to utilize deep ... | computer science |
28,399 | Holistic Planimetric prediction to Local Volumetric prediction for 3D
Human Pose Estimation | cs.CV | We propose a novel approach to 3D human pose estimation from a single depth
map. Recently, convolutional neural network (CNN) has become a powerful
paradigm in computer vision. Many of computer vision tasks have benefited from
CNNs, however, the conventional approach to directly regress 3D body joint
locations from an ... | computer science |
28,400 | Arabian Horse Identification Benchmark Dataset | cs.CV | The lack of a standard muzzle print database is a challenge for conducting
researches in Arabian horse identification systems. Therefore, collecting a
muzzle print images database is a crucial decision. The dataset presented in
this paper is an option for the studies that need a dataset for testing and
comparing the al... | computer science |
28,401 | DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and
Cross-Modality Synthesis in MRI | cs.CV | Cross-modal image synthesis is a topical problem in medical image computing.
Existing methods for image synthesis are either tailored to a specific
application, require large scale training sets, or are based on partitioning
images into overlapping patches. In this paper, we propose a novel Dual
cOnvolutional filTer lE... | computer science |
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