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28,702 | Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks
(MSNets) with Backward Skip Connections for Semantic Segmentation | cs.CV | Recent deep CNNs contain forward shortcut connections; i.e. skip connections
from low to high layers. Reusing features from lower layers that have higher
resolution (location information) benefit higher layers to recover lost details
and mitigate information degradation. However, during inference the lower
layers do no... | computer science |
28,703 | Spectral Filter Tracking | cs.CV | Visual object tracking is a challenging computer vision task with numerous
real-world applications. Here we propose a simple but efficient Spectral Filter
Tracking (SFT)method. To characterize rotational and translation invariance of
tracking targets, the candidate image region is models as a pixelwise grid
graph. Inst... | computer science |
28,704 | Batch based Monocular SLAM for Egocentric Videos | cs.CV | Simultaneous Localization and Mapping (SLAM) from a monocular camera has been
a well researched area. However, estimating camera pose and 3d geometry
reliably for egocentric videos still remain a challenge. Some of the common
causes of failures are dominant 3D rotations and low parallax between
successive frames, resul... | computer science |
28,705 | Fast Feature Fool: A data independent approach to universal adversarial
perturbations | cs.CV | State-of-the-art object recognition Convolutional Neural Networks (CNNs) are
shown to be fooled by image agnostic perturbations, called universal
adversarial perturbations. It is also observed that these perturbations
generalize across multiple networks trained on the same target data. However,
these algorithms require... | computer science |
28,706 | One-shot Face Recognition by Promoting Underrepresented Classes | cs.CV | In this paper, we study the problem of training large-scale face
identification model with imbalanced training data. This problem naturally
exists in many real scenarios including large-scale celebrity recognition,
movie actor annotation, etc. Our solution contains two components. First, we
build a face feature extract... | computer science |
28,707 | Domain Adaptation for Resume Classification Using Convolutional Neural
Networks | cs.CV | We propose a novel method for classifying resume data of job applicants into
27 different job categories using convolutional neural networks. Since resume
data is costly and hard to obtain due to its sensitive nature, we use domain
adaptation. In particular, we train a classifier on a large number of freely
available j... | computer science |
28,708 | Fast Screening Algorithm for Rotation and Scale Invariant Template
Matching | cs.CV | This paper presents a generic pre-processor for expediting conventional
template matching techniques. Instead of locating the best matched patch in the
reference image to a query template via exhaustive search, the proposed
algorithm rules out regions with no possible matches with minimum computational
efforts. While w... | computer science |
28,709 | Faster Than Real-time Facial Alignment: A 3D Spatial Transformer Network
Approach in Unconstrained Poses | cs.CV | Facial alignment involves finding a set of landmark points on an image with a
known semantic meaning. However, this semantic meaning of landmark points is
often lost in 2D approaches where landmarks are either moved to visible
boundaries or ignored as the pose of the face changes. In order to extract
consistent alignme... | computer science |
28,710 | Exploiting Convolutional Representations for Multiscale Human Settlement
Detection | cs.CV | We test this premise and explore representation spaces from a single deep
convolutional network and their visualization to argue for a novel unified
feature extraction framework. The objective is to utilize and re-purpose
trained feature extractors without the need for network retraining on three
remote sensing tasks i... | computer science |
28,711 | Hashed Binary Search Sampling for Convolutional Network Training with
Large Overhead Image Patches | cs.CV | Very large overhead imagery associated with ground truth maps has the
potential to generate billions of training image patches for machine learning
algorithms. However, random sampling selection criteria often leads to
redundant and noisy-image patches for model training. With minimal research
efforts behind this chall... | computer science |
28,712 | Learning Fashion Compatibility with Bidirectional LSTMs | cs.CV | The ubiquity of online fashion shopping demands effective recommendation
services for customers. In this paper, we study two types of fashion
recommendation: (i) suggesting an item that matches existing components in a
set to form a stylish outfit (a collection of fashion items), and (ii)
generating an outfit with mult... | computer science |
28,713 | Skeleton-Based Human Action Recognition with Global Context-Aware
Attention LSTM Networks | cs.CV | Human action recognition in 3D skeleton sequences has attracted a lot of
research attention. Recently, Long Short-Term Memory (LSTM) networks have shown
promising performance in this task due to their strengths in modeling the
dependencies and dynamics in sequential data. As not all skeletal joints are
informative for ... | computer science |
28,714 | Transitioning between Convolutional and Fully Connected Layers in Neural
Networks | cs.CV | Digital pathology has advanced substantially over the last decade however
tumor localization continues to be a challenging problem due to highly complex
patterns and textures in the underlying tissue bed. The use of convolutional
neural networks (CNNs) to analyze such complex images has been well adopted in
digital pat... | computer science |
28,715 | A Novel Deep Learning Architecture for Testis Histology Image
Classification | cs.CV | Unlike other histology analysis, classification of tubule status in testis
histology is very challenging due to their high similarity of texture and
shape. Traditional deep learning networks have difficulties to capture nuance
details among different tubule categories. In this paper, we propose a novel
deep learning ar... | computer science |
28,716 | Discovering Class-Specific Pixels for Weakly-Supervised Semantic
Segmentation | cs.CV | We propose an approach to discover class-specific pixels for the
weakly-supervised semantic segmentation task. We show that properly combining
saliency and attention maps allows us to obtain reliable cues capable of
significantly boosting the performance. First, we propose a simple yet powerful
hierarchical approach to... | computer science |
28,717 | The Devil is in the Decoder | cs.CV | Many machine vision applications require predictions for every pixel of the
input image (for example semantic segmentation, boundary detection). Models for
such problems usually consist of encoders which decreases spatial resolution
while learning a high-dimensional representation, followed by decoders who
recover the ... | computer science |
28,718 | Recognizing and Curating Photo Albums via Event-Specific Image
Importance | cs.CV | Automatic organization of personal photos is a problem with many real world
ap- plications, and can be divided into two main tasks: recognizing the event
type of the photo collection, and selecting interesting images from the
collection. In this paper, we attempt to simultaneously solve both tasks:
album-wise event rec... | computer science |
28,719 | Learning Unified Embedding for Apparel Recognition | cs.CV | In apparel recognition, specialized models (e.g. models trained for a
particular vertical like dresses) can significantly outperform general models
(i.e. models that cover a wide range of verticals). Therefore, deep neural
network models are often trained separately for different verticals. However,
using specialized m... | computer science |
28,720 | Face Alignment Robust to Pose, Expressions and Occlusions | cs.CV | We propose an Ensemble of Robust Constrained Local Models for alignment of
faces in the presence of significant occlusions and of any unknown pose and
expression. To account for partial occlusions we introduce, Robust Constrained
Local Models, that comprises of a deformable shape and local landmark
appearance model and... | computer science |
28,721 | Image Projective Invariants | cs.CV | In this paper, we propose relative projective differential invariants (RPDIs)
which are invariant to general projective transformations. By using RPDIs and
the structural frame of integral invariant, projective weighted moment
invariants (PIs) can be constructed very easily. It is first proved that a kind
of projective... | computer science |
28,722 | When Unsupervised Domain Adaptation Meets Tensor Representations | cs.CV | Domain adaption (DA) allows machine learning methods trained on data sampled
from one distribution to be applied to data sampled from another. It is thus of
great practical importance to the application of such methods. Despite the fact
that tensor representations are widely used in Computer Vision to capture
multi-lin... | computer science |
28,723 | Drone-based Object Counting by Spatially Regularized Regional Proposal
Network | cs.CV | Existing counting methods often adopt regression-based approaches and cannot
precisely localize the target objects, which hinders the further analysis
(e.g., high-level understanding and fine-grained classification). In addition,
most of prior work mainly focus on counting objects in static environments with
fixed came... | computer science |
28,724 | Orthogonal and Idempotent Transformations for Learning Deep Neural
Networks | cs.CV | Identity transformations, used as skip-connections in residual networks,
directly connect convolutional layers close to the input and those close to the
output in deep neural networks, improving information flow and thus easing the
training. In this paper, we introduce two alternative linear transforms,
orthogonal tran... | computer science |
28,725 | Detecting Parts for Action Localization | cs.CV | In this paper, we propose a new framework for action localization that tracks
people in videos and extracts full-body human tubes, i.e., spatio-temporal
regions localizing actions, even in the case of occlusions or truncations. This
is achieved by training a novel human part detector that scores visible parts
while reg... | computer science |
28,726 | Supervising Neural Attention Models for Video Captioning by Human Gaze
Data | cs.CV | The attention mechanisms in deep neural networks are inspired by human's
attention that sequentially focuses on the most relevant parts of the
information over time to generate prediction output. The attention parameters
in those models are implicitly trained in an end-to-end manner, yet there have
been few trials to e... | computer science |
28,727 | Modeling the Intra-class Variability for Liver Lesion Detection using a
Multi-class Patch-based CNN | cs.CV | Automatic detection of liver lesions in CT images poses a great challenge for
researchers. In this work we present a deep learning approach that models
explicitly the variability within the non-lesion class, based on prior
knowledge of the data, to support an automated lesion detection system. A
multi-class convolution... | computer science |
28,728 | Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end
Model | cs.CV | Pedestrian attribute inference is a demanding problem in visual surveillance
that can facilitate person retrieval, search and indexing. To exploit semantic
relations between attributes, recent research treats it as a multi-label image
classification task. The visual cues hinting at attributes can be strongly
localized ... | computer science |
28,729 | Discriminative convolutional Fisher vector network for action
recognition | cs.CV | In this work we propose a novel neural network architecture for the problem
of human action recognition in videos. The proposed architecture expresses the
processing steps of classical Fisher vector approaches, that is dimensionality
reduction by principal component analysis (PCA) projection, Gaussian mixture
model (GM... | computer science |
28,730 | Channel Pruning for Accelerating Very Deep Neural Networks | cs.CV | In this paper, we introduce a new channel pruning method to accelerate very
deep convolutional neural networks.Given a trained CNN model, we propose an
iterative two-step algorithm to effectively prune each layer, by a LASSO
regression based channel selection and least square reconstruction. We further
generalize this ... | computer science |
28,731 | Object-Extent Pooling for Weakly Supervised Single-Shot Localization | cs.CV | In the face of scarcity in detailed training annotations, the ability to
perform object localization tasks in real-time with weak-supervision is very
valuable. However, the computational cost of generating and evaluating region
proposals is heavy. We adapt the concept of Class Activation Maps (CAM) into
the very first ... | computer science |
28,732 | Domain-adversarial neural networks to address the appearance variability
of histopathology images | cs.CV | Preparing and scanning histopathology slides consists of several steps, each
with a multitude of parameters. The parameters can vary between pathology labs
and within the same lab over time, resulting in significant variability of the
tissue appearance that hampers the generalization of automatic image analysis
methods... | computer science |
28,733 | Pose-Invariant Face Alignment with a Single CNN | cs.CV | Face alignment has witnessed substantial progress in the last decade. One of
the recent focuses has been aligning a dense 3D face shape to face images with
large head poses. The dominant technology used is based on the cascade of
regressors, e.g., CNN, which has shown promising results. Nonetheless, the
cascade of CNNs... | computer science |
28,734 | STag: A Stable Fiducial Marker System | cs.CV | In this paper, we propose STag, a fiducial marker system that provides stable
pose estimation. The outer square border of the marker is used for detection
and pose estimation. This is followed by a novel pose refinement step using the
inner circular border. The refined pose is more stable and robust across
viewing cond... | computer science |
28,735 | Automatic Segmentation of Retinal Vasculature | cs.CV | Segmentation of retinal vessels from retinal fundus images is the key step in
the automatic retinal image analysis. In this paper, we propose a new
unsupervised automatic method to segment the retinal vessels from retinal
fundus images. Contrast enhancement and illumination correction are carried out
through a series o... | computer science |
28,736 | Multi-Branch Fully Convolutional Network for Face Detection | cs.CV | Face detection is a fundamental problem in computer vision. It is still a
challenging task in unconstrained conditions due to significant variations in
scale, pose, expressions, and occlusion. In this paper, we propose a
multi-branch fully convolutional network (MB-FCN) for face detection, which
considers both efficien... | computer science |
28,737 | Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute
Recognition | cs.CV | The difficulty of image recognition has gradually increased from general
category recognition to fine-grained recognition and to the recognition of some
subtle attributes such as temperature and geolocation. In this paper, we try to
focus on the classification between sunrise and sunset and hope to give a hint
about ho... | computer science |
28,738 | ThiNet: A Filter Level Pruning Method for Deep Neural Network
Compression | cs.CV | We propose an efficient and unified framework, namely ThiNet, to
simultaneously accelerate and compress CNN models in both training and
inference stages. We focus on the filter level pruning, i.e., the whole filter
would be discarded if it is less important. Our method does not change the
original network structure, th... | computer science |
28,739 | Unsupervised Object Discovery and Co-Localization by Deep Descriptor
Transforming | cs.CV | Reusable model design becomes desirable with the rapid expansion of computer
vision and machine learning applications. In this paper, we focus on the
reusability of pre-trained deep convolutional models. Specifically, different
from treating pre-trained models as feature extractors, we reveal more
treasures beneath con... | computer science |
28,740 | Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively
Combining Object Detectors | cs.CV | Object detection aims at high speed and accuracy simultaneously. However,
fast models are usually less accurate, while accurate models cannot satisfy our
need for speed. A fast model can be 10 times faster but 50\% less accurate than
an accurate model. In this paper, we propose Adaptive Feeding (AF) to combine a
fast (... | computer science |
28,741 | Semantic Segmentation with Reverse Attention | cs.CV | Recent development in fully convolutional neural network enables efficient
end-to-end learning of semantic segmentation. Traditionally, the convolutional
classifiers are taught to learn the representative semantic features of labeled
semantic objects. In this work, we propose a reverse attention network (RAN)
architect... | computer science |
28,742 | Scalable Full Flow with Learned Binary Descriptors | cs.CV | We propose a method for large displacement optical flow in which local
matching costs are learned by a convolutional neural network (CNN) and a
smoothness prior is imposed by a conditional random field (CRF). We tackle the
computation- and memory-intensive operations on the 4D cost volume by a
min-projection which redu... | computer science |
28,743 | cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600
Papers Survey | cs.CV | The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV. | computer science |
28,744 | A Novel Space-Time Representation on the Positive Semidefinite Con for
Facial Expression Recognition | cs.CV | In this paper, we study the problem of facial expression recognition using a
novel space-time geometric representation. We describe the temporal evolution
of facial landmarks as parametrized trajectories on the Riemannian manifold of
positive semidefinite matrices of fixed-rank. Our representation has the
advantage to ... | computer science |
28,745 | Video Object Segmentation using Tracked Object Proposals | cs.CV | We present an approach to semi-supervised video object segmentation, in the
context of the DAVIS 2017 challenge. Our approach combines category-based
object detection, category-independent object appearance segmentation and
temporal object tracking. We are motivated by the fact that the objects
semantic category tends ... | computer science |
28,746 | Pictures of Combinatorial Cubes | cs.CV | We prove that the 8-point algorithm always fails to reconstruct a unique
fundamental matrix $F$ independent on the camera positions, when its input are
image point configurations that are perspective projections of the vertices of
a combinatorial cube in $\mathbb{R}^3$. We give an algorithm that improves the
7- and 8-p... | computer science |
28,747 | The iNaturalist Challenge 2017 Dataset | cs.CV | Existing image classification datasets used in computer vision tend to have
an even number of images for each object category. In contrast, the natural
world is heavily imbalanced, as some species are more abundant and easier to
photograph than others. To encourage further progress in challenging real world
conditions ... | computer science |
28,748 | Local Geometry Inclusive Global Shape Representation | cs.CV | Knowledge of shape geometry plays a pivotal role in many shape analysis
applications. In this paper we introduce a local geometry-inclusive global
representation of 3D shapes based on computation of the shortest quasi-geodesic
paths between all possible pairs of points on the 3D shape manifold. In the
proposed represen... | computer science |
28,749 | Temporal Convolution Based Action Proposal: Submission to ActivityNet
2017 | cs.CV | In this notebook paper, we describe our approach in the submission to the
temporal action proposal (task 3) and temporal action localization (task 4) of
ActivityNet Challenge hosted at CVPR 2017. Since the accuracy in action
classification task is already very high (nearly 90% in ActivityNet dataset),
we believe that t... | computer science |
28,750 | Improved Bilinear Pooling with CNNs | cs.CV | Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23], and
their compact variants [10], have been shown to be effective at fine-grained
recognition, scene categorization, texture recognition, and visual
question-answering tasks among others. The resulting representation captures
second-order statisti... | computer science |
28,751 | Neural Person Search Machines | cs.CV | We investigate the problem of person search in the wild in this work. Instead
of comparing the query against all candidate regions generated in a query-blind
manner, we propose to recursively shrink the search area from the whole image
till achieving precise localization of the target person, by fully exploiting
inform... | computer science |
28,752 | 3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic
Parsing of Large-scale 3D Point Clouds | cs.CV | Semantic parsing of large-scale 3D point clouds is an important research
topic in computer vision and remote sensing fields. Most existing approaches
utilize hand-crafted features for each modality independently and combine them
in a heuristic manner. They often fail to consider the consistency and
complementary inform... | computer science |
28,753 | Head Detection with Depth Images in the Wild | cs.CV | Head detection and localization is a demanding task and a key element for
many computer vision applications, like video surveillance, Human Computer
Interaction and face analysis. The stunning amount of work done for detecting
faces on RGB images, together with the availability of huge face datasets,
allowed to setup v... | computer science |
28,754 | Recurrent Neural Networks for Online Video Popularity Prediction | cs.CV | In this paper, we address the problem of popularity prediction of online
videos shared in social media. We prove that this challenging task can be
approached using recently proposed deep neural network architectures. We cast
the popularity prediction problem as a classification task and we aim to solve
it using only vi... | computer science |
28,755 | Text Recognition in Scene Image and Video Frame using Color Channel
Selection | cs.CV | In recent years, recognition of text from natural scene image and video frame
has got increased attention among the researchers due to its various
complexities and challenges. Because of low resolution, blurring effect,
complex background, different fonts, color and variant alignment of text within
images and video fra... | computer science |
28,756 | Evaluation of Hashing Methods Performance on Binary Feature Descriptors | cs.CV | In this paper we evaluate performance of data-dependent hashing methods on
binary data. The goal is to find a hashing method that can effectively produce
lower dimensional binary representation of 512-bit FREAK descriptors. A
representative sample of recent unsupervised, semi-supervised and supervised
hashing methods w... | computer science |
28,757 | HMM-based Writer Identification in Music Score Documents without
Staff-Line Removal | cs.CV | Writer identification from musical score documents is a challenging task due
to its inherent problem of overlapping of musical symbols with staff lines.
Most of the existing works in the literature of writer identification in
musical score documents were performed after a preprocessing stage of staff
lines removal. In ... | computer science |
28,758 | Date-Field Retrieval in Scene Image and Video Frames using Text
Enhancement and Shape Coding | cs.CV | Text recognition in scene image and video frames is difficult because of low
resolution, blur, background noise, etc. Since traditional OCRs do not perform
well in such images, information retrieval using keywords could be an
alternative way to index/retrieve such text information. Date is a useful piece
of information... | computer science |
28,759 | Neuron Pruning for Compressing Deep Networks using Maxout Architectures | cs.CV | This paper presents an efficient and robust approach for reducing the size of
deep neural networks by pruning entire neurons. It exploits maxout units for
combining neurons into more complex convex functions and it makes use of a
local relevance measurement that ranks neurons according to their activation on
the traini... | computer science |
28,760 | Retinal Microaneurysms Detection using Local Convergence Index Features | cs.CV | Retinal microaneurysms are the earliest clinical sign of diabetic retinopathy
disease. Detection of microaneurysms is crucial for the early diagnosis of
diabetic retinopathy and prevention of blindness. In this paper, a novel and
reliable method for automatic detection of microaneurysms in retinal images is
proposed. I... | computer science |
28,761 | Semantic Image Synthesis via Adversarial Learning | cs.CV | In this paper, we propose a way of synthesizing realistic images directly
with natural language description, which has many useful applications, e.g.
intelligent image manipulation. We attempt to accomplish such synthesis: given
a source image and a target text description, our model synthesizes images to
meet two requ... | computer science |
28,762 | Learning Aerial Image Segmentation from Online Maps | cs.CV | This study deals with semantic segmentation of high-resolution (aerial)
images where a semantic class label is assigned to each pixel via supervised
classification as a basis for automatic map generation. Recently, deep
convolutional neural networks (CNNs) have shown impressive performance and have
quickly become the d... | computer science |
28,763 | What Looks Good with my Sofa: Multimodal Search Engine for Interior
Design | cs.CV | In this paper, we propose a multi-modal search engine for interior design
that combines visual and textual queries. The goal of our engine is to retrieve
interior objects, e.g. furniture or wall clocks, that share visual and
aesthetic similarities with the query. Our search engine allows the user to
take a photo of a r... | computer science |
28,764 | A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram
Classification | cs.CV | Screening mammography is an important front-line tool for the early detection
of breast cancer, and some 39 million exams are conducted each year in the
United States alone. Here, we describe a multi-scale convolutional neural
network (CNN) trained with a curriculum learning strategy that achieves high
levels of accura... | computer science |
28,765 | Persistent-homology-based gait recognition | cs.CV | Gait recognition is an important biometric technique for video surveillance
tasks, due to the advantage of using it at distance. In this paper, we present
a persistent homology-based method to extract topological features (the
so-called {\it topological gait signature}) from the the body silhouettes of a
gait sequence.... | computer science |
28,766 | Memory-Efficient Implementation of DenseNets | cs.CV | The DenseNet architecture is highly computationally efficient as a result of
feature reuse. However, a naive DenseNet implementation can require a
significant amount of GPU memory: If not properly managed, pre-activation batch
normalization and contiguous convolution operations can produce feature maps
that grow quadra... | computer science |
28,767 | Confidence estimation in Deep Neural networks via density modelling | cs.CV | State-of-the-art Deep Neural Networks can be easily fooled into providing
incorrect high-confidence predictions for images with small amounts of
adversarial noise. Does this expose a flaw with deep neural networks, or do we
simply need a better way to estimate confidence? In this paper we consider the
problem of accura... | computer science |
28,768 | What-and-Where to Match: Deep Spatially Multiplicative Integration
Networks for Person Re-identification | cs.CV | Matching pedestrians across disjoint camera views, known as person
re-identification (re-id), is a challenging problem that is of importance to
visual recognition and surveillance. Most existing methods exploit local
regions within spatial manipulation to perform matching in local
correspondence. However, they essentia... | computer science |
28,769 | Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical
Flow Estimation | cs.CV | This paper proposes a novel framework to reconstruct the dynamic magnetic
resonance images (DMRI) with motion compensation (MC). Due to the inherent
motion effects during DMRI acquisition, reconstruction of DMRI using motion
estimation/compensation (ME/MC) has been studied under a compressed sensing
(CS) scheme. In thi... | computer science |
28,770 | PatchShuffle Regularization | cs.CV | This paper focuses on regularizing the training of the convolutional neural
network (CNN). We propose a new regularization approach named ``PatchShuffle``
that can be adopted in any classification-oriented CNN models. It is easy to
implement: in each mini-batch, images or feature maps are randomly chosen to
undergo a t... | computer science |
28,771 | Deep Networks for Compressed Image Sensing | cs.CV | The compressed sensing (CS) theory has been successfully applied to image
compression in the past few years as most image signals are sparse in a certain
domain. Several CS reconstruction models have been recently proposed and
obtained superior performance. However, there still exist two important
challenges within the... | computer science |
28,772 | Single Image Super-Resolution with Dilated Convolution based Multi-Scale
Information Learning Inception Module | cs.CV | Traditional works have shown that patches in a natural image tend to
redundantly recur many times inside the image, both within the same scale, as
well as across different scales. Make full use of these multi-scale information
can improve the image restoration performance. However, the current proposed
deep learning ba... | computer science |
28,773 | Multi-Oriented Text Detection and Verification in Video Frames and Scene
Images | cs.CV | In this paper, we bring forth a novel approach of video text detection using
Fourier-Laplacian filtering in the frequency domain that includes a
verification technique using Hidden Markov Model (HMM). The proposed approach
deals with the text region appearing not only in horizontal or vertical
directions, but also in a... | computer science |
28,774 | Coarse-to-Fine Lifted MAP Inference in Computer Vision | cs.CV | There is a vast body of theoretical research on lifted inference in
probabilistic graphical models (PGMs). However, few demonstrations exist where
lifting is applied in conjunction with top of the line applied algorithms. We
pursue the applicability of lifted inference for computer vision (CV), with the
insight that a ... | computer science |
28,775 | Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC
Agricultural Person-Detection Dataset | cs.CV | Person detection from vehicles has made rapid progress recently with the
advent of multiple highquality datasets of urban and highway driving, yet no
large-scale benchmark is available for the same problem in off-road or
agricultural environments. Here we present the NREC Agricultural
Person-Detection Dataset to spur r... | computer science |
28,776 | Emotion Recognition by Body Movement Representation on the Manifold of
Symmetric Positive Definite Matrices | cs.CV | Emotion recognition is attracting great interest for its potential
application in a multitude of real-life situations. Much of the Computer Vision
research in this field has focused on relating emotions to facial expressions,
with investigations rarely including more than upper body. In this work, we
propose a new scen... | computer science |
28,777 | A survey of exemplar-based texture synthesis | cs.CV | Exemplar-based texture synthesis is the process of generating, from an input
sample, new texture images of arbitrary size and which are perceptually
equivalent to the sample. The two main approaches are statistics-based methods
and patch re-arrangement methods. In the first class, a texture is
characterized by a statis... | computer science |
28,778 | Eyemotion: Classifying facial expressions in VR using eye-tracking
cameras | cs.CV | One of the main challenges of social interaction in virtual reality settings
is that head-mounted displays occlude a large portion of the face, blocking
facial expressions and thereby restricting social engagement cues among users.
Hence, auxiliary means of sensing and conveying these expressions are needed.
We present... | computer science |
28,779 | Spatio-temporal Human Action Localisation and Instance Segmentation in
Temporally Untrimmed Videos | cs.CV | Current state-of-the-art human action recognition is focused on the
classification of temporally trimmed videos in which only one action occurs per
frame. In this work we address the problem of action localisation and instance
segmentation in which multiple concurrent actions of the same class may be
segmented out of a... | computer science |
28,780 | SAR Image Colorization: Converting Single-Polarization to Fully
Polarimetric Using Deep Neural Networks | cs.CV | A deep neural networks based method is proposed to convert single
polarization grayscale SAR image to fully polarimetric. It consists of two
components: a feature extractor network to extract hierarchical multi-scale
spatial features of grayscale SAR image, followed by a feature translator
network to map spatial featur... | computer science |
28,781 | Towards Good Practices for Deep 3D Hand Pose Estimation | cs.CV | 3D hand pose estimation from single depth image is an important and
challenging problem for human-computer interaction. Recently deep convolutional
networks (ConvNet) with sophisticated design have been employed to address it,
but the improvement over traditional random forest based methods is not so
apparent. To explo... | computer science |
28,782 | Detecting and Grouping Identical Objects for Region Proposal and
Classification | cs.CV | Often multiple instances of an object occur in the same scene, for example in
a warehouse. Unsupervised multi-instance object discovery algorithms are able
to detect and identify such objects. We use such an algorithm to provide object
proposals to a convolutional neural network (CNN) based classifier. This
results in ... | computer science |
28,783 | Deeply-Learned Part-Aligned Representations for Person Re-Identification | cs.CV | In this paper, we address the problem of person re-identification, which
refers to associating the persons captured from different cameras. We propose a
simple yet effective human part-aligned representation for handling the body
part misalignment problem. Our approach decomposes the human body into regions
(parts) whi... | computer science |
28,784 | Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure
Learning | cs.CV | As an important and challenging problem in computer vision, learning based
optical flow estimation aims to discover the intrinsic correspondence structure
between two adjacent video frames through statistical learning. Therefore, a
key issue to solve in this area is how to effectively model the multi-scale
corresponden... | computer science |
28,785 | Exploiting Deep Features for Remote Sensing Image Retrieval: A
Systematic Investigation | cs.CV | Remote sensing (RS) image retrieval based on visual content is of great
significance for geological information mining. Over the past two decades, a
large amount of research on this task has been carried out, which mainly
focuses on the following three core issues of image retrieval: visual feature,
similarity metric a... | computer science |
28,786 | Person Re-identification Using Visual Attention | cs.CV | Despite recent attempts for solving the person re-identification problem, it
remains a challenging task since a person's appearance can vary significantly
when large variations in view angle, human pose and illumination are involved.
The concept of attention is one of the most interesting recent architectural
innovatio... | computer science |
28,787 | Compact Model Representation for 3D Reconstruction | cs.CV | 3D reconstruction from 2D images is a central problem in computer vision.
Recent works have been focusing on reconstruction directly from a single image.
It is well known however that only one image cannot provide enough information
for such a reconstruction. A prior knowledge that has been entertained are 3D
CAD model... | computer science |
28,788 | Group-wise Deep Co-saliency Detection | cs.CV | In this paper, we propose an end-to-end group-wise deep co-saliency detection
approach to address the co-salient object discovery problem based on the fully
convolutional network (FCN) with group input and group output. The proposed
approach captures the group-wise interaction information for group images by
learning a... | computer science |
28,789 | Contrastive-center loss for deep neural networks | cs.CV | The deep convolutional neural network(CNN) has significantly raised the
performance of image classification and face recognition. Softmax is usually
used as supervision, but it only penalizes the classification loss. In this
paper, we propose a novel auxiliary supervision signal called contrastivecenter
loss, which can... | computer science |
28,790 | Synthesizing Robust Adversarial Examples | cs.CV | Neural network-based classifiers parallel or exceed human-level accuracy on
many common tasks and are used in practical systems. Yet, neural networks are
susceptible to adversarial examples, carefully perturbed inputs that cause
networks to misbehave in arbitrarily chosen ways. When generated with standard
methods, the... | computer science |
28,791 | Toward Geometric Deep SLAM | cs.CV | We present a point tracking system powered by two deep convolutional neural
networks. The first network, MagicPoint, operates on single images and extracts
salient 2D points. The extracted points are "SLAM-ready" because they are by
design isolated and well-distributed throughout the image. We compare this
network agai... | computer science |
28,792 | Traffic scene recognition based on deep cnn and vlad spatial pyramids | cs.CV | Traffic scene recognition is an important and challenging issue in
Intelligent Transportation Systems (ITS). Recently, Convolutional Neural
Network (CNN) models have achieved great success in many applications,
including scene classification. The remarkable representational learning
capability of CNN remains to be furt... | computer science |
28,793 | Generative OpenMax for Multi-Class Open Set Classification | cs.CV | We present a conceptually new and flexible method for multi-class open set
classification. Unlike previous methods where unknown classes are inferred with
respect to the feature or decision distance to the known classes, our approach
is able to provide explicit modelling and decision score for unknown classes.
The prop... | computer science |
28,794 | LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction | cs.CV | Offline handwritten text line recognition is a hard task that requires both
an efficient optical character recognizer and language model. Handwriting
recognition state of the art methods are based on Long Short Term Memory (LSTM)
recurrent neural networks (RNN) coupled with the use of linguistic knowledge.
Most of the ... | computer science |
28,795 | Delineation of line patterns in images using B-COSFIRE filters | cs.CV | Delineation of line patterns in images is a basic step required in various
applications such as blood vessel detection in medical images, segmentation of
rivers or roads in aerial images, detection of cracks in walls or pavements,
etc. In this paper we present trainable B-COSFIRE filters, which are a model of
some neur... | computer science |
28,796 | Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based
Ranking Approach | cs.CV | Feature selection is playing an increasingly significant role with respect to
many computer vision applications spanning from object recognition to visual
object tracking. However, most of the recent solutions in feature selection are
not robust across different and heterogeneous set of data. In this paper, we
address ... | computer science |
28,797 | Towards Accurate Markerless Human Shape and Pose Estimation over Time | cs.CV | Existing marker-less motion capture methods often assume known backgrounds,
static cameras, and sequence specific motion priors, which narrows its
application scenarios. Here we propose a fully automatic method that given
multi-view video, estimates 3D human motion and body shape. We take recent
SMPLify \cite{bogo2016k... | computer science |
28,798 | Joint Background Reconstruction and Foreground Segmentation via A
Two-stage Convolutional Neural Network | cs.CV | Foreground segmentation in video sequences is a classic topic in computer
vision. Due to the lack of semantic and prior knowledge, it is difficult for
existing methods to deal with sophisticated scenes well. Therefore, in this
paper, we propose an end-to-end two-stage deep convolutional neural network
(CNN) framework f... | computer science |
28,799 | Vision-Based Fallen Person Detection for the Elderly | cs.CV | Falls are serious and costly for elderly people. The Centers for Disease
Control and Prevention of the US reports that millions of older people, 65 and
older, fall each year at least once. Serious injuries such as; hip fractures,
broken bones or head injury, are caused by 20% of the falls. The time it takes
to respond ... | computer science |
28,800 | Liver lesion segmentation informed by joint liver segmentation | cs.CV | We propose a model for the joint segmentation of the liver and liver lesions
in computed tomography (CT) volumes. We build the model from two fully
convolutional networks, connected in tandem and trained together end-to-end. We
evaluate our approach on the 2017 MICCAI Liver Tumour Segmentation Challenge,
attaining comp... | computer science |
28,801 | Detection of curved lines with B-COSFIRE filters: A case study on crack
delineation | cs.CV | The detection of curvilinear structures is an important step for various
computer vision applications, ranging from medical image analysis for
segmentation of blood vessels, to remote sensing for the identification of
roads and rivers, and to biometrics and robotics, among others. %The visual
system of the brain has re... | computer science |
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