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28,102 | Deep-LK for Efficient Adaptive Object Tracking | cs.CV | In this paper we present a new approach for efficient regression based object
tracking which we refer to as Deep- LK. Our approach is closely related to the
Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et
al. We make the following contributions. First, we demonstrate that there is a
theo... | computer science |
28,103 | A Predictive Account of Cafe Wall Illusions Using a Quantitative Model | cs.CV | This paper explores the tilt illusion effect in the Cafe Wall pattern using a
classical Gaussian Receptive Field model. In this illusion, the mortar lines
are misperceived as diverging or converging rather than horizontal. We examine
the capability of a simple bioplausible filtering model to recognize different
degrees... | computer science |
28,104 | Online Signature Verification using Recurrent Neural Network and
Length-normalized Path Signature | cs.CV | Inspired by the great success of recurrent neural networks (RNNs) in
sequential modeling, we introduce a novel RNN system to improve the performance
of online signature verification. The training objective is to directly
minimize intra-class variations and to push the distances between skilled
forgeries and genuine sam... | computer science |
28,105 | Prediction of Sea Surface Temperature using Long Short-Term Memory | cs.CV | This letter adopts long short-term memory(LSTM) to predict sea surface
temperature(SST), which is the first attempt, to our knowledge, to use
recurrent neural network to solve the problem of SST prediction, and to make
one week and one month daily prediction. We formulate the SST prediction
problem as a time series reg... | computer science |
28,106 | ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI | cs.CV | Compressive sensing (CS) is an effective approach for fast Magnetic Resonance
Imaging (MRI). It aims at reconstructing MR images from a small number of
under-sampled data in k-space, and accelerating the data acquisition in MRI. To
improve the current MRI system in reconstruction accuracy and speed, in this
paper, we p... | computer science |
28,107 | Fiber Orientation Estimation Guided by a Deep Network | cs.CV | Diffusion magnetic resonance imaging (dMRI) is currently the only tool for
noninvasively imaging the brain's white matter tracts. The fiber orientation
(FO) is a key feature computed from dMRI for fiber tract reconstruction.
Because the number of FOs in a voxel is usually small, dictionary-based sparse
reconstruction h... | computer science |
28,108 | Affine-Gradient Based Local Binary Pattern Descriptor for Texture
Classiffication | cs.CV | We present a novel Affine-Gradient based Local Binary Pattern (AGLBP)
descriptor for texture classification. It is very hard to describe complicated
texture using single type information, such as Local Binary Pattern (LBP),
which just utilizes the sign information of the difference between the pixel
and its local neigh... | computer science |
28,109 | Local Shape Spectrum Analysis for 3D Facial Expression Recognition | cs.CV | We investigate the problem of facial expression recognition using 3D data.
Building from one of the most successful frameworks for facial analysis using
exclusively 3D geometry, we extend the analysis from a curve-based
representation into a spectral representation, which allows a complete
description of the underlying... | computer science |
28,110 | Hyperspectral Band Selection Using Unsupervised Non-Linear Deep Auto
Encoder to Train External Classifiers | cs.CV | Hyperspectral image classification often requires selecting the most
informative bands instead of processing the whole data without losing the
geometrical representation of the data. Existing dimensionality reduction and
band selection methods have the capability to reveal the nonlinear properties
exhibited in the data... | computer science |
28,111 | The Kinetics Human Action Video Dataset | cs.CV | We describe the DeepMind Kinetics human action video dataset. The dataset
contains 400 human action classes, with at least 400 video clips for each
action. Each clip lasts around 10s and is taken from a different YouTube video.
The actions are human focussed and cover a broad range of classes including
human-object int... | computer science |
28,112 | Segmentation of 3D High-frequency Ultrasound Images of Human Lymph Nodes
Using Graph Cut with Energy Functional Adapted to Local Intensity
Distribution | cs.CV | Previous studies by our group have shown that three-dimensional
high-frequency quantitative ultrasound methods have the potential to
differentiate metastatic lymph nodes from cancer-free lymph nodes dissected
from human cancer patients. To successfully perform these methods inside the
lymph node parenchyma, an automati... | computer science |
28,113 | What are the Receptive, Effective Receptive, and Projective Fields of
Neurons in Convolutional Neural Networks? | cs.CV | In this work, we explain in detail how receptive fields, effective receptive
fields, and projective fields of neurons in different layers, convolution or
pooling, of a Convolutional Neural Network (CNN) are calculated. While our
focus here is on CNNs, the same operations, but in the reverse order, can be
used to calcul... | computer science |
28,114 | MRI-PET Registration with Automated Algorithm in Pre-clinical Studies | cs.CV | Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET)
automatic 3-D registration is implemented and validated for small animal image
volumes so that the high-resolution anatomical MRI information can be fused
with the low spatial resolution of functional PET information for the
localization of lesion ... | computer science |
28,115 | Bitwise Operations of Cellular Automaton on Gray-scale Images | cs.CV | Cellular Automata (CA) theory is a discrete model that represents the state
of each of its cells from a finite set of possible values which evolve in time
according to a pre-defined set of transition rules. CA have been applied to a
number of image processing tasks such as Convex Hull Detection, Image Denoising
etc. bu... | computer science |
28,116 | CNN-Based Joint Clustering and Representation Learning with Feature
Drift Compensation for Large-Scale Image Data | cs.CV | Given a large unlabeled set of images, how to efficiently and effectively
group them into clusters based on extracted visual representations remains a
challenging problem. To address this problem, we propose a convolutional neural
network (CNN) to jointly solve clustering and representation learning in an
iterative man... | computer science |
28,117 | Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry
and Semantics | cs.CV | Numerous deep learning applications benefit from multi-task learning with
multiple regression and classification objectives. In this paper we make the
observation that the performance of such systems is strongly dependent on the
relative weighting between each task's loss. Tuning these weights by hand is a
difficult an... | computer science |
28,118 | Deep De-Aliasing for Fast Compressive Sensing MRI | cs.CV | Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical
applications in order to reduce the scanning cost and improve the patient
experience. This can also potentially increase the image quality by reducing
the motion artefacts and contrast washout. However, once an image field of view
and the desir... | computer science |
28,119 | Simultaneous Multiple Surface Segmentation Using Deep Learning | cs.CV | The task of automatically segmenting 3-D surfaces representing boundaries of
objects is important for quantitative analysis of volumetric images, and plays
a vital role in biomedical image analysis. Recently, graph-based methods with a
global optimization property have been developed and optimized for various
medical i... | computer science |
28,120 | A New 3D Segmentation Methodology for Lumbar Vertebral Bodies for the
Measurement of BMD and Geometry | cs.CV | In this paper a new technique is presented that extracts the geometry of
lumbar vertebral bodies from spiral CT scans. Our new multi-step segmentation
approach yields highly accurate and precise measurement of the bone mineral
density (BMD) in different volumes of interest which are defined relative to a
local anatomic... | computer science |
28,121 | Sparse Coding on Stereo Video for Object Detection | cs.CV | Deep Convolutional Neural Networks (DCNN) require millions of labeled
training examples for image classification and object detection tasks, which
restrict these models to domains where such datasets are available. In this
paper, we explore the use of unsupervised sparse coding applied to stereo-video
data to help alle... | computer science |
28,122 | A New 3D Method to Segment the Lumbar Vertebral Bodies and to Determine
Bone Mineral Density and Geometry | cs.CV | In this paper we present a new 3D segmentation approach for the vertebrae of
the lower thoracic and the lumbar spine in spiral computed tomography datasets.
We implemented a multi-step procedure. Its main components are deformable
models, volume growing, and morphological operations. The performance analysis
that inclu... | computer science |
28,123 | A Lightweight Approach for On-the-Fly Reflectance Estimation | cs.CV | Estimating surface reflectance (BRDF) is one key component for complete 3D
scene capture, with wide applications in virtual reality, augmented reality,
and human computer interaction. Prior work is either limited to controlled
environments (\eg gonioreflectometers, light stages, or multi-camera domes), or
requires the ... | computer science |
28,124 | Multiple-Human Parsing in the Wild | cs.CV | Human parsing is attracting increasing research attention. In this work, we
aim to push the frontier of human parsing by introducing the problem of
multi-human parsing in the wild. Existing works on human parsing mainly tackle
single-person scenarios, which deviates from real-world applications where
multiple persons a... | computer science |
28,125 | Quadruplet Network with One-Shot Learning for Fast Visual Object
Tracking | cs.CV | In the same vein of discriminative one-shot learning, Siamese networks allow
recognizing an object from a single exemplar with the same class label.
However, they do not take advantage of the underlying structure of the data and
the relationship among the multitude of samples as they only rely on pairs of
instances for... | computer science |
28,126 | Recurrent Scene Parsing with Perspective Understanding in the Loop | cs.CV | Objects may appear at arbitrary scales in perspective images of a scene,
posing a challenge for recognition systems that process images at a fixed
resolution. We propose a depth-aware gating module that adaptively selects the
pooling field size in a convolutional network architecture according to the
object scale (inve... | computer science |
28,127 | Non-Linear Phase-Shifting of Haar Wavelets for Run-Time All-Frequency
Lighting | cs.CV | This paper focuses on real-time all-frequency image-based rendering using an
innovative solution for run-time computation of light transport. The approach
is based on new results derived for non-linear phase shifting in the Haar
wavelet domain. Although image-based methods for real-time rendering of dynamic
glossy obje... | computer science |
28,128 | Gaze Distribution Analysis and Saliency Prediction Across Age Groups | cs.CV | Knowledge of the human visual system helps to develop better computational
models of visual attention. State-of-the-art models have been developed to
mimic the visual attention system of young adults that, however, largely ignore
the variations that occur with age. In this paper, we investigated how visual
scene proces... | computer science |
28,129 | Forecasting Hands and Objects in Future Frames | cs.CV | This paper presents an approach to forecast future presence and location of
human hands and objects. Given an image frame, the goal is to predict what
objects will appear in the future frame (e.g., 5 seconds later) and where they
will be located at, even when they are not visible in the current frame. The
key idea is t... | computer science |
28,130 | Critical Contours: An Invariant Linking Image Flow with Salient Surface
Organization | cs.CV | We exploit a key result from visual psychophysics -- that individuals
perceive shape qualitatively -- to develop a geometrical/topological invariant
(the Morse-Smale complex) relating image structure with surface structure.
Differences across individuals are minimal near certain configurations such as
ridges and bounda... | computer science |
28,131 | Phase-Shifting Separable Haar Wavelets and Applications | cs.CV | This paper presents a new approach for tackling the shift-invariance problem
in the discrete Haar domain, without trading off any of its desirable
properties, such as compression, separability, orthogonality, and symmetry. The
paper presents several key theoretical contributions. First, we derive closed
form expression... | computer science |
28,132 | Structural Compression of Convolutional Neural Networks Based on Greedy
Filter Pruning | cs.CV | Convolutional neural networks (CNNs) have state-of-the-art performance on
many problems in machine vision. However, networks with superior performance
often have millions of weights so that it is difficult or impossible to use
CNNs on computationally limited devices or to humanly interpret them. A myriad
of CNN compres... | computer science |
28,133 | Incorporating Depth into both CNN and CRF for Indoor Semantic
Segmentation | cs.CV | To improve segmentation performance, a novel neural network architecture
(termed DFCN-DCRF) is proposed, which combines an RGB-D fully convolutional
neural network (DFCN) with a depth-sensitive fully-connected conditional random
field (DCRF). First, a DFCN architecture which fuses depth information into the
early layer... | computer science |
28,134 | Large-Scale Classification of Structured Objects using a CRF with Deep
Class Embedding | cs.CV | This paper presents a novel deep learning architecture to classify structured
objects in datasets with a large number of visually similar categories. We
model sequences of images as linear-chain CRFs, and jointly learn the
parameters from both local-visual features and neighboring classes. The visual
features are compu... | computer science |
28,135 | Generative Partition Networks for Multi-Person Pose Estimation | cs.CV | This paper proposes a new Generative Partition Network (GPN) to address the
challenging multi-person pose estimation problem. Different from existing
models that are either completely top-down or bottom-up, the proposed GPN
introduces a novel strategy--it generates partitions for multiple persons from
their global join... | computer science |
28,136 | The Do's and Don'ts for CNN-based Face Verification | cs.CV | While the research community appears to have developed a consensus on the
methods of acquiring annotated data, design and training of CNNs, many
questions still remain to be answered. In this paper, we explore the following
questions that are critical to face recognition research: (i) Can we train on
still images and e... | computer science |
28,137 | Image Segmentation by Iterative Inference from Conditional Score
Estimation | cs.CV | Inspired by the combination of feedforward and iterative computations in the
virtual cortex, and taking advantage of the ability of denoising autoencoders
to estimate the score of a joint distribution, we propose a novel approach to
iterative inference for capturing and exploiting the complex joint distribution
of outp... | computer science |
28,138 | Classification and Retrieval of Digital Pathology Scans: A New Dataset | cs.CV | In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image
classification and retrieval in digital pathology. We use the whole scan images
of 24 different tissue textures to generate 1,325 test patches of size
1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according
to preference... | computer science |
28,139 | Boosting the accuracy of multi-spectral image pan-sharpening by learning
a deep residual network | cs.CV | In the field of fusing multi-spectral and panchromatic images
(Pan-sharpening), the impressive effectiveness of deep neural networks has been
recently employed to overcome the drawbacks of traditional linear models and
boost the fusing accuracy. However, to the best of our knowledge, existing
research works are mainly ... | computer science |
28,140 | Learning Robust Object Recognition Using Composed Scenes from Generative
Models | cs.CV | Recurrent feedback connections in the mammalian visual system have been
hypothesized to play a role in synthesizing input in the theoretical framework
of analysis by synthesis. The comparison of internally synthesized
representation with that of the input provides a validation mechanism during
perceptual inference and ... | computer science |
28,141 | View-Invariant Recognition of Action Style Self-Dissimilarity | cs.CV | Self-similarity was recently introduced as a measure of inter-class
congruence for classification of actions. Herein, we investigate the dual
problem of intra-class dissimilarity for classification of action styles. We
introduce self-dissimilarity matrices that discriminate between same actions
performed by different s... | computer science |
28,142 | Computer vision-based food calorie estimation: dataset, method, and
experiment | cs.CV | Computer vision has been introduced to estimate calories from food images.
But current food image data sets don't contain volume and mass records of
foods, which leads to an incomplete calorie estimation. In this paper, we
present a novel food image data set with volume and mass records of foods, and
a deep learning me... | computer science |
28,143 | Semantic Softmax Loss for Zero-Shot Learning | cs.CV | A typical pipeline for Zero-Shot Learning (ZSL) is to integrate the visual
features and the class semantic descriptors into a multimodal framework with a
linear or bilinear model. However, the visual features and the class semantic
descriptors locate in different structural spaces, a linear or bilinear model
can not ca... | computer science |
28,144 | Learning to Associate Words and Images Using a Large-scale Graph | cs.CV | We develop an approach for unsupervised learning of associations between
co-occurring perceptual events using a large graph. We applied this approach to
successfully solve the image captcha of China's railroad system. The approach
is based on the principle of suspicious coincidence. In this particular
problem, a user i... | computer science |
28,145 | Convolutional Networks with MuxOut Layers as Multi-rate Systems for
Image Upscaling | cs.CV | We interpret convolutional networks as adaptive filters and combine them with
so-called MuxOut layers to efficiently upscale low resolution images. We
formalize this interpretation by deriving a linear and space-variant structure
of a convolutional network when its activations are fixed. We introduce general
purpose al... | computer science |
28,146 | Robust Localized Multi-view Subspace Clustering | cs.CV | In multi-view clustering, different views may have different confidence
levels when learning a consensus representation. Existing methods usually
address this by assigning distinctive weights to different views. However, due
to noisy nature of real-world applications, the confidence levels of samples in
the same view m... | computer science |
28,147 | TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for
Video Action Segmentation | cs.CV | Action segmentation as a milestone towards building automatic systems to
understand untrimmed videos has received considerable attention in the recent
years. It is typically being modeled as a sequence labeling problem but
contains intrinsic and sufficient differences than text parsing or speech
processing. In this pap... | computer science |
28,148 | DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable
Channels | cs.CV | In the context of scene understanding, a variety of methods exists to
estimate different information channels from mono or stereo images, including
disparity, depth, and normals. Although several advances have been reported in
the recent years for these tasks, the estimated information is often imprecise
particularly n... | computer science |
28,149 | Facial Expression Recognition Using Enhanced Deep 3D Convolutional
Neural Networks | cs.CV | Deep Neural Networks (DNNs) have shown to outperform traditional methods in
various visual recognition tasks including Facial Expression Recognition (FER).
In spite of efforts made to improve the accuracy of FER systems using DNN,
existing methods still are not generalizable enough in practical applications.
This paper... | computer science |
28,150 | Facial Affect Estimation in the Wild Using Deep Residual and
Convolutional Networks | cs.CV | Automated affective computing in the wild is a challenging task in the field
of computer vision. This paper presents three neural network-based methods
proposed for the task of facial affect estimation submitted to the First
Affect-in-the-Wild challenge. These methods are based on Inception-ResNet
modules redesigned sp... | computer science |
28,151 | Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader
Evaluations | cs.CV | We present the design and manufacturing of high fidelity universal 3D
fingerprint targets, which can be imaged on a variety of fingerprint sensing
technologies, namely capacitive, contact-optical, and contactless-optical.
Universal 3D fingerprint targets enable, for the first time, not only a
repeatable and controlled ... | computer science |
28,152 | GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network | cs.CV | We propose a novel convolutional neural network for lesion detection from
weak labels. Only a single, global label per image - the lesion count - is
needed for training. We train a regression network with a fully convolutional
architecture combined with a global pooling layer to aggregate the 3D output
into a scalar in... | computer science |
28,153 | Training with Confusion for Fine-Grained Visual Classification | cs.CV | Research in Fine-Grained Visual Classification has focused on tackling the
variations in pose, lighting, and viewpoint using sophisticated localization
and segmentation techniques, and the usage of robust texture features to
improve performance. In this work, we look at the fundamental optimization of
neural network tr... | computer science |
28,154 | Unrolled Optimization with Deep Priors | cs.CV | A broad class of problems at the core of computational imaging, sensing, and
low-level computer vision reduces to the inverse problem of extracting latent
images that follow a prior distribution, from measurements taken under a known
physical image formation model. Traditionally, hand-crafted priors along with
iterativ... | computer science |
28,155 | Multiple Images Recovery Using a Single Affine Transformation | cs.CV | In many real-world applications, image data often come with noises,
corruptions or large errors. One approach to deal with noise image data is to
use data recovery techniques which aim to recover the true uncorrupted signals
from the observed noise images. In this paper, we first introduce a novel
corruption recovery t... | computer science |
28,156 | Patchnet: Interpretable Neural Networks for Image Classification | cs.CV | The ability to visually understand and interpret learned features from
complex predictive models is crucial for their acceptance in sensitive areas
such as health care. To move closer to this goal of truly interpretable complex
models, we present PatchNet, a network that restricts global context for image
classificatio... | computer science |
28,157 | Universal Style Transfer via Feature Transforms | cs.CV | Universal style transfer aims to transfer arbitrary visual styles to content
images. Existing feed-forward based methods, while enjoying the inference
efficiency, are mainly limited by inability of generalizing to unseen styles or
compromised visual quality. In this paper, we present a simple yet effective
method that ... | computer science |
28,158 | Towards seamless multi-view scene analysis from satellite to
street-level | cs.CV | In this paper, we discuss and review how combined multi-view imagery from
satellite to street-level can benefit scene analysis. Numerous works exist that
merge information from remote sensing and images acquired from the ground for
tasks like land cover mapping, object detection, or scene understanding. What
makes the ... | computer science |
28,159 | Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action
Recognition | cs.CV | It remains a challenge to efficiently extract spatialtemporal information
from skeleton sequences for 3D human action recognition. Although most recent
action recognition methods are based on Recurrent Neural Networks which present
outstanding performance, one of the shortcomings of these methods is the
tendency to ove... | computer science |
28,160 | A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical
Data | cs.CV | With the availability of big medical image data, the selection of an adequate
training set is becoming more important to address the heterogeneity of
different datasets. Simply including all the data does not only incur high
processing costs but can even harm the prediction. We formulate the smart and
efficient selecti... | computer science |
28,161 | Correlation Alignment by Riemannian Metric for Domain Adaptation | cs.CV | Domain adaptation techniques address the problem of reducing the sensitivity
of machine learning methods to the so-called domain shift, namely the
difference between source (training) and target (test) data distributions. In
particular, unsupervised domain adaptation assumes no labels are available in
the target domain... | computer science |
28,162 | Unmasking the abnormal events in video | cs.CV | We propose a novel framework for abnormal event detection in video that
requires no training sequences. Our framework is based on unmasking, a
technique previously used for authorship verification in text documents, which
we adapt to our task. We iteratively train a binary classifier to distinguish
between two consecut... | computer science |
28,163 | Salient Object Detection with Semantic Priors | cs.CV | Salient object detection has increasingly become a popular topic in cognitive
and computational sciences, including computer vision and artificial
intelligence research. In this paper, we propose integrating \textit{semantic
priors} into the salient object detection process. Our algorithm consists of
three basic steps.... | computer science |
28,164 | On the mathematics of beauty: beautiful images | cs.CV | In this paper, we will study the simplest kind of beauty that can be found in
a simple visual pattern and can be appreciated universally. The proposed model
suggest that there is a link between beautiful pattern and a deeper
optimisation process between randomness and regularity. Then we show that
beautiful patterns ne... | computer science |
28,165 | Distributed Algorithms for Feature Extraction Off-loading in
Multi-Camera Visual Sensor Networks | cs.CV | Real-time visual analysis tasks, like tracking and recognition, require swift
execution of computationally intensive algorithms. Visual sensor networks can
be enabled to perform such tasks by augmenting the sensor network with
processing nodes and distributing the computational burden in a way that the
cameras contend ... | computer science |
28,166 | Isomorphism between Differential and Moment Invariants under Affine
Transform | cs.CV | The invariant is one of central topics in science, technology and
engineering. The differential invariant is essential in understanding or
describing some important phenomena or procedures in mathematics, physics,
chemistry, biology or computer science etc. The derivation of differential
invariants is usually difficult... | computer science |
28,167 | A New 3D Segmentation Technique for QCT Scans of the Lumbar Spine to
Determine BMD and Vertebral Geometry | cs.CV | Quantitative computed tomography (QCT) is a standard method to determine bone
mineral density (BMD) in the spine. Traditionally single 8 - 10 mm thick slices
have been analyzed only. Current spiral CT scanners provide true 3D acquisition
schemes allowing for a more differential BMD analysis and an assessment of
geometr... | computer science |
28,168 | How hard can it be? Estimating the difficulty of visual search in an
image | cs.CV | We address the problem of estimating image difficulty defined as the human
response time for solving a visual search task. We collect human annotations of
image difficulty for the PASCAL VOC 2012 data set through a crowd-sourcing
platform. We then analyze what human interpretable image properties can have an
impact on ... | computer science |
28,169 | An Invariant Model of the Significance of Different Body Parts in
Recognizing Different Actions | cs.CV | In this paper, we show that different body parts do not play equally
important roles in recognizing a human action in video data. We investigate to
what extent a body part plays a role in recognition of different actions and
hence propose a generic method of assigning weights to different body points.
The approach is i... | computer science |
28,170 | Anatomically Constrained Neural Networks (ACNN): Application to Cardiac
Image Enhancement and Segmentation | cs.CV | Incorporation of prior knowledge about organ shape and location is key to
improve performance of image analysis approaches. In particular, priors can be
useful in cases where images are corrupted and contain artefacts due to
limitations in image acquisition. The highly constrained nature of anatomical
objects can be we... | computer science |
28,171 | A Novel Multi-Detector Fusion Framework for Multi-Object Tracking | cs.CV | In order to track all persons in a scene, the tracking-by-detection paradigm
has proven to be a very effective approach. Yet, relying solely on a single
detector is also a major limitation, as useful image information might be
ignored. This work demonstrates how to incorporate several detectors into a
tracking system, ... | computer science |
28,172 | Classification of Aerial Photogrammetric 3D Point Clouds | cs.CV | We present a powerful method to extract per-point semantic class labels from
aerialphotogrammetry data. Labeling this kind of data is important for tasks
such as environmental modelling, object classification and scene understanding.
Unlike previous point cloud classification methods that rely exclusively on
geometric ... | computer science |
28,173 | AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual
Actions | cs.CV | This paper introduces a video dataset of spatio-temporally localized Atomic
Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual
actions in 192 15-minute video clips, where actions are localized in space and
time, resulting in 740k action labels with multiple labels per person occurring
frequently. ... | computer science |
28,174 | Input Fast-Forwarding for Better Deep Learning | cs.CV | This paper introduces a new architectural framework, known as input
fast-forwarding, that can enhance the performance of deep networks. The main
idea is to incorporate a parallel path that sends representations of input
values forward to deeper network layers. This scheme is substantially different
from "deep supervisi... | computer science |
28,175 | Sequence Summarization Using Order-constrained Kernelized Feature
Subspaces | cs.CV | Representations that can compactly and effectively capture temporal evolution
of semantic content are important to machine learning algorithms that operate
on multi-variate time-series data. We investigate such representations
motivated by the task of human action recognition. Here each data instance is
encoded by a mu... | computer science |
28,176 | Generative Model with Coordinate Metric Learning for Object Recognition
Based on 3D Models | cs.CV | Given large amount of real photos for training, Convolutional neural network
shows excellent performance on object recognition tasks. However, the process
of collecting data is so tedious and the background are also limited which
makes it hard to establish a perfect database. In this paper, our generative
model trained... | computer science |
28,177 | Deep Learning Improves Template Matching by Normalized Cross Correlation | cs.CV | Template matching by normalized cross correlation (NCC) is widely used for
finding image correspondences. We improve the robustness of this algorithm by
preprocessing images with "siamese" convolutional networks trained to maximize
the contrast between NCC values of true and false matches. The improvement is
quantified... | computer science |
28,178 | Robust Data Geometric Structure Aligned Close yet Discriminative Domain
Adaptation | cs.CV | Domain adaptation (DA) is transfer learning which aims to leverage labeled
data in a related source domain to achieve informed knowledge transfer and help
the classification of unlabeled data in a target domain. In this paper, we
propose a novel DA method, namely Robust Data Geometric Structure Aligned,
Close yet Discr... | computer science |
28,179 | Deep Rotation Equivariant Network | cs.CV | Recently, learning equivariant representations has attracted considerable
research attention. Dieleman et al. introduce four operations which can be
inserted into convolutional neural network to learn deep representations
equivariant to rotation. However, feature maps should be copied and rotated
four times in each lay... | computer science |
28,180 | VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning
Approach | cs.CV | In this paper, we design a multimodal framework for object detection,
recognition and mapping based on the fusion of stereo camera frames, point
cloud Velodyne Lidar scans, and Vehicle-to-Vehicle (V2V) Basic Safety Messages
(BSMs) exchanged using Dedicated Short Range Communication (DSRC). We merge the
key features of ... | computer science |
28,181 | Self-supervised learning of visual features through embedding images
into text topic spaces | cs.CV | End-to-end training from scratch of current deep architectures for new
computer vision problems would require Imagenet-scale datasets, and this is not
always possible. In this paper we present a method that is able to take
advantage of freely available multi-modal content to train computer vision
algorithms without hum... | computer science |
28,182 | Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence
Models for Fill-in-the-Blank Image Captioning | cs.CV | We develop the first approximate inference algorithm for 1-Best (and M-Best)
decoding in bidirectional neural sequence models by extending Beam Search (BS)
to reason about both forward and backward time dependencies. Beam Search (BS)
is a widely used approximate inference algorithm for decoding sequences from
unidirect... | computer science |
28,183 | Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit
Training for Contextual Video Recognition | cs.CV | Based on the progress of image recognition, video recognition has been
extensively studied recently. However, most of the existing methods are focused
on short-term but not long-term video recognition, called contextual video
recognition. To address contextual video recognition, we use convolutional
recurrent neural ne... | computer science |
28,184 | Optimization of the Jaccard index for image segmentation with the
Lovász hinge | cs.CV | The Jaccard loss, commonly referred to as the intersection-over-union loss,
is commonly employed in the evaluation of segmentation quality due to its
better perceptual quality and scale invariance, which lends appropriate
relevance to small objects compared with per-pixel losses. We present a method
for direct optimiza... | computer science |
28,185 | From source to target and back: symmetric bi-directional adaptive GAN | cs.CV | The effectiveness of generative adversarial approaches in producing images
according to a specific style or visual domain has recently opened new
directions to solve the unsupervised domain adaptation problem. It has been
shown that source labeled images can be modified to mimic target samples making
it possible to tra... | computer science |
28,186 | Attention-based Natural Language Person Retrieval | cs.CV | Following the recent progress in image classification and captioning using
deep learning, we develop a novel natural language person retrieval system
based on an attention mechanism. More specifically, given the description of a
person, the goal is to localize the person in an image. To this end, we first
construct a b... | computer science |
28,187 | GridNet with automatic shape prior registration for automatic MRI
cardiac segmentation | cs.CV | In this paper, we propose a fully automatic MRI cardiac segmentation method
based on a novel deep convolutional neural network (CNN) designed for the 2017
ACDC MICCAI challenge. The novelty of our network comes with its embedded shape
prior and its loss function tailored to the cardiac anatomy. Our model includes
a car... | computer science |
28,188 | Extraction and Classification of Diving Clips from Continuous Video
Footage | cs.CV | Due to recent advances in technology, the recording and analysis of video
data has become an increasingly common component of athlete training
programmes. Today it is incredibly easy and affordable to set up a fixed camera
and record athletes in a wide range of sports, such as diving, gymnastics,
golf, tennis, etc. How... | computer science |
28,189 | Weakly Supervised Semantic Segmentation Based on Web Image
Co-segmentation | cs.CV | Training a Fully Convolutional Network (FCN) for semantic segmentation
requires a large number of masks with pixel level labelling, which involves a
large amount of human labour and time for annotation. In contrast, web images
and their image-level labels are much easier and cheaper to obtain. In this
work, we propose ... | computer science |
28,190 | SLAM based Quasi Dense Reconstruction For Minimally Invasive Surgery
Scenes | cs.CV | Recovering surgical scene structure in laparoscope surgery is crucial step
for surgical guidance and augmented reality applications. In this paper, a
quasi dense reconstruction algorithm of surgical scene is proposed. This is
based on a state-of-the-art SLAM system, and is exploiting the initial
exploration phase that ... | computer science |
28,191 | Deep image representations using caption generators | cs.CV | Deep learning exploits large volumes of labeled data to learn powerful
models. When the target dataset is small, it is a common practice to perform
transfer learning using pre-trained models to learn new task specific
representations. However, pre-trained CNNs for image recognition are provided
with limited information... | computer science |
28,192 | Direct Multitype Cardiac Indices Estimation via Joint Representation and
Regression Learning | cs.CV | Cardiac indices estimation is of great importance during identification and
diagnosis of cardiac disease in clinical routine. However, estimation of
multitype cardiac indices with consistently reliable and high accuracy is still
a great challenge due to the high variability of cardiac structures and
complexity of tempo... | computer science |
28,193 | Plan3D: Viewpoint and Trajectory Optimization for Aerial Multi-View
Stereo Reconstruction | cs.CV | We introduce a new method that efficiently computes a set of rich viewpoints
and trajectories for high-quality 3D reconstructions in outdoor environments.
The input images of the reconstruction are taken with a commodity RGB camera
which is mounted on an autonomously navigated quadcopter, and the obtained
recordings ar... | computer science |
28,194 | Pose Guided Person Image Generation | cs.CV | This paper proposes the novel Pose Guided Person Generation Network (PG$^2$)
that allows to synthesize person images in arbitrary poses, based on an image
of that person and a novel pose. Our generation framework PG$^2$ utilizes the
pose information explicitly and consists of two key stages: pose integration
and image ... | computer science |
28,195 | Unsupervised Feature Learning for Writer Identification and Writer
Retrieval | cs.CV | Deep Convolutional Neural Networks (CNN) have shown great success in
supervised classification tasks such as character classification or dating.
Deep learning methods typically need a lot of annotated training data, which is
not available in many scenarios. In these cases, traditional methods are often
better than or e... | computer science |
28,196 | Text-Independent Speaker Verification Using 3D Convolutional Neural
Networks | cs.CV | In this paper, a novel method using 3D Convolutional Neural Network (3D-CNN)
architecture has been proposed for speaker verification in the text-independent
setting. At the development phase, a CNN is trained to classify speakers at the
utterance-level. In the enrollment stage, the trained network is utilized to
direct... | computer science |
28,197 | Hierarchical Cellular Automata for Visual Saliency | cs.CV | Saliency detection, finding the most important parts of an image, has become
increasingly popular in computer vision. In this paper, we introduce
Hierarchical Cellular Automata (HCA) -- a temporally evolving model to
intelligently detect salient objects. HCA consists of two main components:
Single-layer Cellular Automa... | computer science |
28,198 | Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data
Science Bowl 2017 Challenge | cs.CV | We present a deep learning framework for computer-aided lung cancer
diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans,
determines if each nodule is malignant, and finally assigns a cancer
probability based on these results. We discuss the challenges and advantages of
our framework. In the Kaggle... | computer science |
28,199 | Effective Sampling: Fast Segmentation Using Robust Geometric Model
Fitting | cs.CV | Identifying the underlying models in a set of data points contaminated by
noise and outliers, leads to a highly complex multi-model fitting problem. This
problem can be posed as a clustering problem by the projection of higher order
affinities between data points into a graph, which can then be clustered using
spectral... | computer science |
28,200 | Algorithmic clothing: hybrid recommendation, from street-style-to-shop | cs.CV | In this paper we detail Cortexica's (https://www.cortexica.com)
recommendation framework -- particularly, we describe how a hybrid visual
recommender system can be created by combining conditional random fields for
segmentation and deep neural networks for object localisation and feature
representation. The recommendat... | computer science |
28,201 | Predicting Human Interaction via Relative Attention Model | cs.CV | Predicting human interaction is challenging as the on-going activity has to
be inferred based on a partially observed video. Essentially, a good algorithm
should effectively model the mutual influence between the two interacting
subjects. Also, only a small region in the scene is discriminative for
identifying the on-g... | computer science |
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