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29,002 | A Solution for Crime Scene Reconstruction using Time-of-Flight Cameras | cs.CV | In this work, we propose a method for three-dimensional (3D) reconstruction
of wide crime scene, based on a Simultaneous Localization and Mapping (SLAM)
approach. We used a Kinect V2 Time-of-Flight (TOF) RGB-D camera to provide
colored dense point clouds at a 30 Hz frequency. This device is moved freely (6
degrees of f... | computer science |
29,003 | Structured Attentions for Visual Question Answering | cs.CV | Visual attention, which assigns weights to image regions according to their
relevance to a question, is considered as an indispensable part by most Visual
Question Answering models. Although the questions may involve complex relations
among multiple regions, few attention models can effectively encode such
cross-region... | computer science |
29,004 | Learning for Active 3D Mapping | cs.CV | We propose an active 3D mapping method for depth sensors, which allow
individual control of depth-measuring rays, such as the newly emerging
solid-state lidars. The method simultaneously (i) learns to reconstruct a dense
3D occupancy map from sparse depth measurements, and (ii) optimizes the
reactive control of depth-m... | computer science |
29,005 | Extraction of Airways with Probabilistic State-space Models and Bayesian
Smoothing | cs.CV | Segmenting tree structures is common in several image processing
applications. In medical image analysis, reliable segmentations of airways,
vessels, neurons and other tree structures can enable important clinical
applications. We present a framework for tracking tree structures comprising of
elongated branches using p... | computer science |
29,006 | Two-Phase Learning for Weakly Supervised Object Localization | cs.CV | Weakly supervised semantic segmentation and localiza- tion have a problem of
focusing only on the most important parts of an image since they use only
image-level annota- tions. In this paper, we solve this problem fundamentally
via two-phase learning. Our networks are trained in two steps. In the first
step, a convent... | computer science |
29,007 | Learning to segment on tiny datasets: a new shape model | cs.CV | Current object segmentation algorithms are based on the hypothesis that one
has access to a very large amount of data. In this paper, we aim to segment
objects using only tiny datasets. To this extent, we propose a new automatic
part-based object segmentation algorithm for non-deformable and semi-deformable
objects in ... | computer science |
29,008 | Self-supervised Learning of Pose Embeddings from Spatiotemporal
Relations in Videos | cs.CV | Human pose analysis is presently dominated by deep convolutional networks
trained with extensive manual annotations of joint locations and beyond. To
avoid the need for expensive labeling, we exploit spatiotemporal relations in
training videos for self-supervised learning of pose embeddings. The key idea
is to combine ... | computer science |
29,009 | MemNet: A Persistent Memory Network for Image Restoration | cs.CV | Recently, very deep convolutional neural networks (CNNs) have been attracting
considerable attention in image restoration. However, as the depth grows, the
long-term dependency problem is rarely realized for these very deep models,
which results in the prior states/layers having little influence on the
subsequent ones.... | computer science |
29,010 | Training Deep Networks to be Spatially Sensitive | cs.CV | In many computer vision tasks, for example saliency prediction or semantic
segmentation, the desired output is a foreground map that predicts pixels where
some criteria is satisfied. Despite the inherently spatial nature of this task
commonly used learning objectives do not incorporate the spatial relationships
between... | computer science |
29,011 | Learning a CNN-based End-to-End Controller for a Formula SAE Racecar | cs.CV | We present a set of CNN-based end-to-end models for controls of a Formula SAE
racecar, along with various benchmarking and visualization tools to understand
model performance. We tackled three main problems in the context of
cone-delineated racetrack driving: (1) discretized steering, which translates a
first-person fr... | computer science |
29,012 | Graph Classification with 2D Convolutional Neural Networks | cs.CV | Graph learning is currently dominated by graph kernels, which, while
powerful, suffer some significant limitations. Convolutional Neural Networks
(CNNs) offer a very appealing alternative, but processing graphs with CNNs is
not trivial. To address this challenge, many sophisticated extensions of CNNs
have recently been... | computer science |
29,013 | Automatic segmentation of the intracranialvolume in fetal MR images | cs.CV | MR images of the fetus allow non-invasive analysis of the fetal brain.
Quantitative analysis of fetal brain development requires automatic brain
tissue segmentation that is typically preceded by segmentation of the
intracranial volume (ICV). This is challenging because fetal MR images
visualize the whole moving fetus a... | computer science |
29,014 | An Adaptive Cluster-based Wiener Filter for Speckle Reduction of OCT
Skin Images | cs.CV | Optical coherence tomography (OCT) has become a favorable device in the
dermatology discipline due to its moderate resolution and penetration depth.
OCT images, however, contain a grainy pattern, called speckle, due to the use
of a broadband source in the configuration of OCT. So far, a variety of
filtering techniques ... | computer science |
29,015 | Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and
Soft-Weighted-Sum Inference | cs.CV | Monocular depth estimation is a challenging task in complex compositions
depicting multiple objects of diverse scales. Albeit the recent great progress
thanks to the deep convolutional neural networks (CNNs), the state-of-the-art
monocular depth estimation methods still fall short to handle such real-world
challenging ... | computer science |
29,016 | Beyond Low-Rank Representations: Orthogonal Clustering Basis
Reconstruction with Optimized Graph Structure for Multi-view Spectral
Clustering | cs.CV | Low-Rank Representation (LRR) is arguably one of the most powerful paradigms
for Multi-view spectral clustering, which elegantly encodes the multi-view
local graph/manifold structures into an intrinsic low-rank self-expressive data
similarity embedded in high-dimensional space, to yield a better graph
partition than th... | computer science |
29,017 | Unconstrained Face Detection and Open-Set Face Recognition Challenge | cs.CV | Face detection and recognition benchmarks have shifted toward more difficult
environments. The challenge presented in this paper addresses the next step in
the direction of automatic detection and identification of people from outdoor
surveillance cameras. While face detection has shown remarkable success in
images col... | computer science |
29,018 | Temporal Context Network for Activity Localization in Videos | cs.CV | We present a Temporal Context Network (TCN) for precise temporal localization
of human activities. Similar to the Faster-RCNN architecture, proposals are
placed at equal intervals in a video which span multiple temporal scales. We
propose a novel representation for ranking these proposals. Since pooling
features only i... | computer science |
29,019 | Learning a Repression Network for Precise Vehicle Search | cs.CV | The growing explosion in the use of surveillance cameras in public security
highlights the importance of vehicle search from large-scale image databases.
Precise vehicle search, aiming at finding out all instances for a given query
vehicle image, is a challenging task as different vehicles will look very
similar to eac... | computer science |
29,020 | Wasserstein CNN: Learning Invariant Features for NIR-VIS Face
Recognition | cs.CV | Heterogeneous face recognition (HFR) aims to match facial images acquired
from different sensing modalities with mission-critical applications in
forensics, security and commercial sectors. However, HFR is a much more
challenging problem than traditional face recognition because of large
intra-class variations of heter... | computer science |
29,021 | FoveaNet: Perspective-aware Urban Scene Parsing | cs.CV | Parsing urban scene images benefits many applications, especially
self-driving. Most of the current solutions employ generic image parsing models
that treat all scales and locations in the images equally and do not consider
the geometry property of car-captured urban scene images. Thus, they suffer
from heterogeneous o... | computer science |
29,022 | Prune the Convolutional Neural Networks with Sparse Shrink | cs.CV | Nowadays, it is still difficult to adapt Convolutional Neural Network (CNN)
based models for deployment on embedded devices. The heavy computation and
large memory footprint of CNN models become the main burden in real
application. In this paper, we propose a "Sparse Shrink" algorithm to prune an
existing CNN model. By... | computer science |
29,023 | An Effective Feature Selection Method Based on Pair-Wise Feature
Proximity for High Dimensional Low Sample Size Data | cs.CV | Feature selection has been studied widely in the literature. However, the
efficacy of the selection criteria for low sample size applications is
neglected in most cases. Most of the existing feature selection criteria are
based on the sample similarity. However, the distance measures become
insignificant for high dimen... | computer science |
29,024 | Weakly Supervised Image Annotation and Segmentation with Objects and
Attributes | cs.CV | We propose to model complex visual scenes using a non-parametric Bayesian
model learned from weakly labelled images abundant on media sharing sites such
as Flickr. Given weak image-level annotations of objects and attributes without
locations or associations between them, our model aims to learn the appearance
of objec... | computer science |
29,025 | An Unsupervised Game-Theoretic Approach to Saliency Detection | cs.CV | We propose a novel unsupervised game-theoretic salient object detection
algorithm that does not require labeled training data. First, saliency
detection problem is formulated as a non-cooperative game, hereinafter referred
to as Saliency Game, in which image regions are players who choose to be
"background" or "foregro... | computer science |
29,026 | From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video
Captioning | cs.CV | Video captioning in essential is a complex natural process, which is affected
by various uncertainties stemming from video content, subjective judgment, etc.
In this paper we build on the recent progress in using encoder-decoder
framework for video captioning and address what we find to be a critical
deficiency of the ... | computer science |
29,027 | An Error Detection and Correction Framework for Connectomics | cs.CV | We define and study error detection and correction tasks that are useful for
3D reconstruction of neurons from electron microscopic imagery, and for image
segmentation more generally. Both tasks take as input the raw image and a
binary mask representing a candidate object. For the error detection task, the
desired outp... | computer science |
29,028 | A discriminative view of MRF pre-processing algorithms | cs.CV | While Markov Random Fields (MRFs) are widely used in computer vision, they
present a quite challenging inference problem. MRF inference can be accelerated
by pre-processing techniques like Dead End Elimination (DEE) or QPBO-based
approaches which compute the optimal labeling of a subset of variables. These
techniques a... | computer science |
29,029 | Generative Adversarial Network-based Synthesis of Visible Faces from
Polarimetric Thermal Faces | cs.CV | The large domain discrepancy between faces captured in polarimetric (or
conventional) thermal and visible domain makes cross-domain face recognition
quite a challenging problem for both human-examiners and computer vision
algorithms. Previous approaches utilize a two-step procedure (visible feature
estimation and visib... | computer science |
29,030 | Statistics of Deep Generated Images | cs.CV | Here, we explore the low-level statistics of images generated by
state-of-the-art deep generative models. First, Wasserstein generative
adversarial network (WGAN) and deep convolutional generative adversarial
network (DCGAN) are trained on the ImageNet dataset and a large set of cartoon
frames from animations. Then, fo... | computer science |
29,031 | What Actions are Needed for Understanding Human Actions in Videos? | cs.CV | What is the right way to reason about human activities? What directions
forward are most promising? In this work, we analyze the current state of human
activity understanding in videos. The goal of this paper is to examine
datasets, evaluation metrics, algorithms, and potential future directions. We
look at the qualita... | computer science |
29,032 | Sequential Dual Deep Learning with Shape and Texture Features for Sketch
Recognition | cs.CV | Recognizing freehand sketches with high arbitrariness is greatly challenging.
Most existing methods either ignore the geometric characteristics or treat
sketches as handwritten characters with fixed structural ordering.
Consequently, they can hardly yield high recognition performance even though
sophisticated learning ... | computer science |
29,033 | Deep Face Feature for Face Alignment | cs.CV | In this paper, we present a deep learning based image feature extraction
method designed specifically for face images. To train the feature extraction
model, we construct a large scale photo-realistic face image dataset with
ground-truth correspondence between multi-view face images, which are
synthesized from real pho... | computer science |
29,034 | Weakly- and Self-Supervised Learning for Content-Aware Deep Image
Retargeting | cs.CV | This paper proposes a weakly- and self-supervised deep convolutional neural
network (WSSDCNN) for content-aware image retargeting. Our network takes a
source image and a target aspect ratio, and then directly outputs a retargeted
image. Retargeting is performed through a shift map, which is a pixel-wise
mapping from th... | computer science |
29,035 | Probabilistic Neural Network with Complex Exponential Activation
Functions in Image Recognition using Deep Learning Framework | cs.CV | If the training dataset is not very large, image recognition is usually
implemented with the transfer learning methods. In these methods the features
are extracted using a deep convolutional neural network, which was
preliminarily trained with an external very-large dataset. In this paper we
consider the nonparametric ... | computer science |
29,036 | Joint Face Alignment and 3D Face Reconstruction with Application to Face
Recognition | cs.CV | Face alignment and 3D face reconstruction are traditionally accomplished as
separated tasks. By exploring the strong correlation between 2D landmarks and
3D shapes, in contrast, we propose a joint face alignment and 3D face
reconstruction method to simultaneously solve these two problems for 2D face
images of arbitrary... | computer science |
29,037 | Extreme clicking for efficient object annotation | cs.CV | Manually annotating object bounding boxes is central to building computer
vision datasets, and it is very time consuming (annotating ILSVRC [53] took 35s
for one high-quality box [62]). It involves clicking on imaginary corners of a
tight box around the object. This is difficult as these corners are often
outside the a... | computer science |
29,038 | Isointense infant brain MRI segmentation with a dilated convolutional
neural network | cs.CV | Quantitative analysis of brain MRI at the age of 6 months is difficult
because of the limited contrast between white matter and gray matter. In this
study, we use a dilated triplanar convolutional neural network in combination
with a non-dilated 3D convolutional neural network for the segmentation of
white matter, gray... | computer science |
29,039 | Learning to Disambiguate by Asking Discriminative Questions | cs.CV | The ability to ask questions is a powerful tool to gather information in
order to learn about the world and resolve ambiguities. In this paper, we
explore a novel problem of generating discriminative questions to help
disambiguate visual instances. Our work can be seen as a complement and new
extension to the rich rese... | computer science |
29,040 | Multi-dimensional Gated Recurrent Units for Automated Anatomical
Landmark Localization | cs.CV | We present an automated method for localizing an anatomical landmark in
three-dimensional medical images. The method combines two recurrent neural
networks in a coarse-to-fine approach: The first network determines a candidate
neighborhood by analyzing the complete given image volume. The second network
localizes the a... | computer science |
29,041 | BlitzNet: A Real-Time Deep Network for Scene Understanding | cs.CV | Real-time scene understanding has become crucial in many applications such as
autonomous driving. In this paper, we propose a deep architecture, called
BlitzNet, that jointly performs object detection and semantic segmentation in
one forward pass, allowing real-time computations. Besides the computational
gain of havin... | computer science |
29,042 | Anveshak - A Groundtruth Generation Tool for Foreground Regions of
Document Images | cs.CV | We propose a graphical user interface based groundtruth generation tool in
this paper. Here, annotation of an input document image is done based on the
foreground pixels. Foreground pixels are grouped together with user interaction
to form labeling units. These units are then labeled by the user with the user
defined l... | computer science |
29,043 | Online Multi-Object Tracking Using CNN-based Single Object Tracker with
Spatial-Temporal Attention Mechanism | cs.CV | In this paper, we propose a CNN-based framework for online MOT. This
framework utilizes the merits of single object trackers in adapting appearance
models and searching for target in the next frame. Simply applying single
object tracker for MOT will encounter the problem in computational efficiency
and drifted results ... | computer science |
29,044 | WebVision Database: Visual Learning and Understanding from Web Data | cs.CV | In this paper, we present a study on learning visual recognition models from
large scale noisy web data. We build a new database called WebVision, which
contains more than $2.4$ million web images crawled from the Internet by using
queries generated from the 1,000 semantic concepts of the benchmark ILSVRC 2012
dataset.... | computer science |
29,045 | CoupleNet: Coupling Global Structure with Local Parts for Object
Detection | cs.CV | The region-based Convolutional Neural Network (CNN) detectors such as Faster
R-CNN or R-FCN have already shown promising results for object detection by
combining the region proposal subnetwork and the classification subnetwork
together. Although R-FCN has achieved higher detection speed while keeping the
detection per... | computer science |
29,046 | Transitive Invariance for Self-supervised Visual Representation Learning | cs.CV | Learning visual representations with self-supervised learning has become
popular in computer vision. The idea is to design auxiliary tasks where labels
are free to obtain. Most of these tasks end up providing data to learn specific
kinds of invariance useful for recognition. In this paper, we propose to
exploit differe... | computer science |
29,047 | SUBIC: A supervised, structured binary code for image search | cs.CV | For large-scale visual search, highly compressed yet meaningful
representations of images are essential. Structured vector quantizers based on
product quantization and its variants are usually employed to achieve such
compression while minimizing the loss of accuracy. Yet, unlike binary hashing
schemes, these unsupervi... | computer science |
29,048 | Learning Policies for Adaptive Tracking with Deep Feature Cascades | cs.CV | Visual object tracking is a fundamental and time-critical vision task. Recent
years have seen many shallow tracking methods based on real-time pixel-based
correlation filters, as well as deep methods that have top performance but need
a high-end GPU. In this paper, we learn to improve the speed of deep trackers
without... | computer science |
29,049 | Random Binary Trees for Approximate Nearest Neighbour Search in Binary
Space | cs.CV | Approximate nearest neighbour (ANN) search is one of the most important
problems in computer science fields such as data mining or computer vision. In
this paper, we focus on ANN for high-dimensional binary vectors and we propose
a simple yet powerful search method that uses Random Binary Search Trees
(RBST). We apply ... | computer science |
29,050 | ChromaTag: A Colored Marker and Fast Detection Algorithm | cs.CV | Current fiducial marker detection algorithms rely on marker IDs for false
positive rejection. Time is wasted on potential detections that will eventually
be rejected as false positives. We introduce ChromaTag, a fiducial marker and
detection algorithm designed to use opponent colors to limit and quickly reject
initial ... | computer science |
29,051 | A Unified Model for Near and Remote Sensing | cs.CV | We propose a novel convolutional neural network architecture for estimating
geospatial functions such as population density, land cover, or land use. In
our approach, we combine overhead and ground-level images in an end-to-end
trainable neural network, which uses kernel regression and density estimation
to convert fea... | computer science |
29,052 | TandemNet: Distilling Knowledge from Medical Images Using Diagnostic
Reports as Optional Semantic References | cs.CV | In this paper, we introduce the semantic knowledge of medical images from
their diagnostic reports to provide an inspirational network training and an
interpretable prediction mechanism with our proposed novel multimodal neural
network, namely TandemNet. Inside TandemNet, a language model is used to
represent report te... | computer science |
29,053 | Semantic Video CNNs through Representation Warping | cs.CV | In this work, we propose a technique to convert CNN models for semantic
segmentation of static images into CNNs for video data. We describe a warping
method that can be used to augment existing architectures with very little
extra computational cost. This module is called NetWarp and we demonstrate its
use for a range ... | computer science |
29,054 | Modality-bridge Transfer Learning for Medical Image Classification | cs.CV | This paper presents a new approach of transfer learning-based medical image
classification to mitigate insufficient labeled data problem in medical domain.
Instead of direct transfer learning from source to small number of labeled
target data, we propose a modality-bridge transfer learning which employs the
bridge data... | computer science |
29,055 | Attention-Aware Face Hallucination via Deep Reinforcement Learning | cs.CV | Face hallucination is a domain-specific super-resolution problem with the
goal to generate high-resolution (HR) faces from low-resolution (LR) input
images. In contrast to existing methods that often learn a single
patch-to-patch mapping from LR to HR images and are regardless of the
contextual interdependency between ... | computer science |
29,056 | Analysis of Convolutional Neural Networks for Document Image
Classification | cs.CV | Convolutional Neural Networks (CNNs) are state-of-the-art models for document
image classification tasks. However, many of these approaches rely on
parameters and architectures designed for classifying natural images, which
differ from document images. We question whether this is appropriate and
conduct a large empiric... | computer science |
29,057 | Incremental 3D Line Segments Extraction from Semi-dense SLAM | cs.CV | Despite much interest in Simultaneous Localization and Mapping (SLAM), there
is a lack of efficient methods for representing and processing their large
scale point clouds. In this paper, we propose to simplify the point clouds
generated by the semi-dense SLAM using three-dimensional (3D) line segments.
Specifically, we... | computer science |
29,058 | Document Image Binarization with Fully Convolutional Neural Networks | cs.CV | Binarization of degraded historical manuscript images is an important
pre-processing step for many document processing tasks. We formulate
binarization as a pixel classification learning task and apply a novel Fully
Convolutional Network (FCN) architecture that operates at multiple image
scales, including full resoluti... | computer science |
29,059 | Motion Feature Augmented Recurrent Neural Network for Skeleton-based
Dynamic Hand Gesture Recognition | cs.CV | Dynamic hand gesture recognition has attracted increasing interests because
of its importance for human computer interaction. In this paper, we propose a
new motion feature augmented recurrent neural network for skeleton-based
dynamic hand gesture recognition. Finger motion features are extracted to
describe finger mov... | computer science |
29,060 | Exploring Temporal Preservation Networks for Precise Temporal Action
Localization | cs.CV | Temporal action localization is an important task of computer vision. Though
a variety of methods have been proposed, it still remains an open question how
to predict the temporal boundaries of action segments precisely. Most works use
segment-level classifiers to select video segments pre-determined by action
proposal... | computer science |
29,061 | Cell Detection in Microscopy Images with Deep Convolutional Neural
Network and Compressed Sensing | cs.CV | The ability to automatically detect certain types of cells or cellular
subunits in microscopy images is of significant interest to a wide range of
biomedical research and clinical practices. Cell detection methods have evolved
from employing hand-crafted features to deep learning-based techniques. The
essential idea of... | computer science |
29,062 | Writer Identification and Verification from Intra-variable Individual
Handwriting | cs.CV | The handwriting of an individual may vary excessively with many factors such
as mood, time, space, writing speed, writing medium, utensils etc. Therefore,
it becomes more challenging to perform automated writer verification/
identification on a particular set of handwritten patterns (e.g. speedy
handwriting) of a perso... | computer science |
29,063 | Joint Multi-Person Pose Estimation and Semantic Part Segmentation | cs.CV | Human pose estimation and semantic part segmentation are two complementary
tasks in computer vision. In this paper, we propose to solve the two tasks
jointly for natural multi-person images, in which the estimated pose provides
object-level shape prior to regularize part segments while the part-level
segments constrain... | computer science |
29,064 | Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose
Estimation | cs.CV | Hand pose estimation from a single depth image is an essential topic in
computer vision and human computer interaction. Despite recent advancements in
this area promoted by convolutional neural network, accurate hand pose
estimation is still a challenging problem. In this paper we propose a Pose
guided structured Regio... | computer science |
29,065 | Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear
Kernel | cs.CV | Video deblurring is a challenging problem as the blur is complex and usually
caused by the combination of camera shakes, object motions, and depth
variations. Optical flow can be used for kernel estimation since it predicts
motion trajectories. However, the estimates are often inaccurate in complex
scenes at object bou... | computer science |
29,066 | Iterative Deep Convolutional Encoder-Decoder Network for Medical Image
Segmentation | cs.CV | In this paper, we propose a novel medical image segmentation using iterative
deep learning framework. We have combined an iterative learning approach and an
encoder-decoder network to improve segmentation results, which enables to
precisely localize the regions of interest (ROIs) including complex shapes or
detailed te... | computer science |
29,067 | A Generic Deep Architecture for Single Image Reflection Removal and
Image Smoothing | cs.CV | This paper proposes a deep neural network structure that exploits edge
information in addressing representative low-level vision tasks such as layer
separation and image filtering. Unlike most other deep learning strategies
applied in this context, our approach tackles these challenging problems by
estimating edges and... | computer science |
29,068 | Unsupervised Incremental Learning of Deep Descriptors From Video Streams | cs.CV | We present a novel unsupervised method for face identity learning from video
sequences. The method exploits the ResNet deep network for face detection and
VGGface fc7 face descriptors together with a smart learning mechanism that
exploits the temporal coherence of visual data in video streams. We present a
novel featur... | computer science |
29,069 | Beyond Bilinear: Generalized Multi-modal Factorized High-order Pooling
for Visual Question Answering | cs.CV | Visual question answering (VQA) is challenging because it requires a
simultaneous understanding of both visual content of images and textual content
of questions. To support the VQA task, we need to find good solutions for the
following three issues: 1) fine-grained feature representations for both the
image and the qu... | computer science |
29,070 | Convolutional Neural Networks for Font Classification | cs.CV | Classifying pages or text lines into font categories aids transcription
because single font Optical Character Recognition (OCR) is generally more
accurate than omni-font OCR. We present a simple framework based on
Convolutional Neural Networks (CNNs), where a CNN is trained to classify small
patches of text into predef... | computer science |
29,071 | Deep Recurrent Neural Networks for mapping winter vegetation quality
coverage via multi-temporal SAR Sentinel-1 | cs.CV | Mapping winter vegetation quality coverage is a challenge problem of remote
sensing. This is due to the cloud coverage in winter period, leading to use
radar rather than optical images. The objective of this paper is to provide a
better understanding of the capabilities of radar Sentinel-1 and deep learning
concerning ... | computer science |
29,072 | Learning Rotation for Kernel Correlation Filter | cs.CV | Kernel Correlation Filters have shown a very promising scheme for visual
tracking in terms of speed and accuracy on several benchmarks. However it
suffers from problems that affect its performance like occlusion, rotation and
scale change. This paper tries to tackle the problem of rotation by
reformulating the optimiza... | computer science |
29,073 | Exploiting Semantic Contextualization for Interpretation of Human
Activity in Videos | cs.CV | We use large-scale commonsense knowledge bases, e.g. ConceptNet, to provide
context cues to establish semantic relationships among entities directly
hypothesized from video signal, such as putative object and actions labels, and
infer a deeper interpretation of events than what is directly sensed. One
approach is to le... | computer science |
29,074 | Face Parsing via a Fully-Convolutional Continuous CRF Neural Network | cs.CV | In this work, we address the face parsing task with a Fully-Convolutional
continuous CRF Neural Network (FC-CNN) architecture. In contrast to previous
face parsing methods that apply region-based subnetwork hundreds of times, our
FC-CNN is fully convolutional with high segmentation accuracy. To achieve this
goal, FC-CN... | computer science |
29,075 | Flower Categorization using Deep Convolutional Neural Networks | cs.CV | We have developed a deep learning network for classification of different
flowers. For this, we have used Visual Geometry Group's 102 category flower
dataset having 8189 images of 102 different flowers from University of Oxford.
The method is basically divided into two parts; Image segmentation and
classification. We h... | computer science |
29,076 | Noisy Softmax: Improving the Generalization Ability of DCNN via
Postponing the Early Softmax Saturation | cs.CV | Over the past few years, softmax and SGD have become a commonly used
component and the default training strategy in CNN frameworks, respectively.
However, when optimizing CNNs with SGD, the saturation behavior behind softmax
always gives us an illusion of training well and then is omitted. In this
paper, we first empha... | computer science |
29,077 | Kill Two Birds With One Stone: Boosting Both Object Detection Accuracy
and Speed With adaptive Patch-of-Interest Composition | cs.CV | Object detection is an important yet challenging task in video understanding
& analysis, where one major challenge lies in the proper balance between two
contradictive factors: detection accuracy and detection speed. In this paper,
we propose a new adaptive patch-of-interest composition approach for boosting
both the a... | computer science |
29,078 | Deep Steering: Learning End-to-End Driving Model from Spatial and
Temporal Visual Cues | cs.CV | In recent years, autonomous driving algorithms using low-cost vehicle-mounted
cameras have attracted increasing endeavors from both academia and industry.
There are multiple fronts to these endeavors, including object detection on
roads, 3-D reconstruction etc., but in this work we focus on a vision-based
model that di... | computer science |
29,079 | Revisiting the Effectiveness of Off-the-shelf Temporal Modeling
Approaches for Large-scale Video Classification | cs.CV | This paper describes our solution for the video recognition task of
ActivityNet Kinetics challenge that ranked the 1st place. Most of existing
state-of-the-art video recognition approaches are in favor of an end-to-end
pipeline. One exception is the framework of DevNet. The merit of DevNet is that
they first use the vi... | computer science |
29,080 | Mass Displacement Networks | cs.CV | Despite the large improvements in performance attained by using deep learning
in computer vision, one can often further improve results with some additional
post-processing that exploits the geometric nature of the underlying task. This
commonly involves displacing the posterior distribution of a CNN in a way that
make... | computer science |
29,081 | Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample
Filtering and Hybrid-Loss Residual Learning | cs.CV | In this paper, we propose a novel framework with 3D convolutional networks
(ConvNets) for automated detection of pulmonary nodules from low-dose CT scans,
which is a challenging yet crucial task for lung cancer early diagnosis and
treatment. Different from previous standard ConvNets, we try to tackle the
severe hard/ea... | computer science |
29,082 | Recurrent Filter Learning for Visual Tracking | cs.CV | Recently using convolutional neural networks (CNNs) has gained popularity in
visual tracking, due to its robust feature representation of images. Recent
methods perform online tracking by fine-tuning a pre-trained CNN model to the
specific target object using stochastic gradient descent (SGD)
back-propagation, which is... | computer science |
29,083 | Large Batch Training of Convolutional Networks | cs.CV | A common way to speed up training of large convolutional networks is to add
computational units. Training is then performed using data-parallel synchronous
Stochastic Gradient Descent (SGD) with mini-batch divided between computational
units. With an increase in the number of nodes, the batch size grows. But
training w... | computer science |
29,084 | An Extremely Efficient Chess-board Detection for Non-trivial Photos | cs.CV | We present a set of algorithms that can be used to locate and crop the
chess-board/chess-pieces from the picture, including every rectangular grid
with any pattern. Our method is non-parametric, and thus does not require the
prior knowledge from computer vision and machine learning, which is instead
inferred from data.... | computer science |
29,085 | A Cost-Sensitive Visual Question-Answer Framework for Mining a Deep
And-OR Object Semantics from Web Images | cs.CV | This paper presents a cost-sensitive Question-Answering (QA) framework for
learning a nine-layer And-Or graph (AoG) from web images, which explicitly
represents object categories, poses, parts, and detailed structures within the
parts in a compositional hierarchy. The QA framework is designed to minimize an
overall ris... | computer science |
29,086 | Learning Deep Neural Networks for Vehicle Re-ID with
Visual-spatio-temporal Path Proposals | cs.CV | Vehicle re-identification is an important problem and has many applications
in video surveillance and intelligent transportation. It gains increasing
attention because of the recent advances of person re-identification
techniques. However, unlike person re-identification, the visual differences
between pairs of vehicle... | computer science |
29,087 | Visual Graph Mining | cs.CV | In this study, we formulate the concept of "mining maximal-size frequent
subgraphs" in the challenging domain of visual data (images and videos). In
general, visual knowledge can usually be modeled as attributed relational
graphs (ARGs) with local attributes representing local parts and pairwise
attributes describing t... | computer science |
29,088 | Lattice Long Short-Term Memory for Human Action Recognition | cs.CV | Human actions captured in video sequences are three-dimensional signals
characterizing visual appearance and motion dynamics. To learn action patterns,
existing methods adopt Convolutional and/or Recurrent Neural Networks (CNNs and
RNNs). CNN based methods are effective in learning spatial appearances, but are
limited ... | computer science |
29,089 | SSH: Single Stage Headless Face Detector | cs.CV | We introduce the Single Stage Headless (SSH) face detector. Unlike two stage
proposal-classification detectors, SSH detects faces in a single stage directly
from the early convolutional layers in a classification network. SSH is
headless. That is, it is able to achieve state-of-the-art results while
removing the "head"... | computer science |
29,090 | AffectNet: A Database for Facial Expression, Valence, and Arousal
Computing in the Wild | cs.CV | Automated affective computing in the wild setting is a challenging problem in
computer vision. Existing annotated databases of facial expressions in the wild
are small and mostly cover discrete emotions (aka the categorical model). There
are very limited annotated facial databases for affective computing in the
continu... | computer science |
29,091 | Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile
Robots | cs.CV | Safety is paramount for mobile robotic platforms such as self-driving cars
and unmanned aerial vehicles. This work is devoted to a task that is
indispensable for safety yet was largely overlooked in the past -- detecting
obstacles that are of very thin structures, such as wires, cables and tree
branches. This is a chal... | computer science |
29,092 | Style2Vec: Representation Learning for Fashion Items from Style Sets | cs.CV | With the rapid growth of online fashion market, demand for effective fashion
recommendation systems has never been greater. In fashion recommendation, the
ability to find items that goes well with a few other items based on style is
more important than picking a single item based on the user's entire purchase
history. ... | computer science |
29,093 | Kinship Verification from Videos using Spatio-Temporal Texture Features
and Deep Learning | cs.CV | Automatic kinship verification using facial images is a relatively new and
challenging research problem in computer vision. It consists in automatically
predicting whether two persons have a biological kin relation by examining
their facial attributes. While most of the existing works extract shallow
handcrafted featur... | computer science |
29,094 | Context-based Normalization of Histological Stains using Deep
Convolutional Features | cs.CV | While human observers are able to cope with variations in color and
appearance of histological stains, digital pathology algorithms commonly
require a well-normalized setting to achieve peak performance, especially when
a limited amount of labeled data is available. This work provides a fully
automated, end-to-end lear... | computer science |
29,095 | Towards Semantic Fast-Forward and Stabilized Egocentric Videos | cs.CV | The emergence of low-cost personal mobiles devices and wearable cameras and
the increasing storage capacity of video-sharing websites have pushed forward a
growing interest towards first-person videos. Since most of the recorded videos
compose long-running streams with unedited content, they are tedious and
unpleasant ... | computer science |
29,096 | Binary Generative Adversarial Networks for Image Retrieval | cs.CV | The most striking successes in image retrieval using deep hashing have mostly
involved discriminative models, which require labels. In this paper, we use
binary generative adversarial networks (BGAN) to embed images to binary codes
in an unsupervised way. By restricting the input noise variable of generative
adversaria... | computer science |
29,097 | Fast-Forward Video Based on Semantic Extraction | cs.CV | Thanks to the low operational cost and large storage capacity of smartphones
and wearable devices, people are recording many hours of daily activities,
sport actions and home videos. These videos, also known as egocentric videos,
are generally long-running streams with unedited content, which make them
boring and visua... | computer science |
29,098 | Divide and Fuse: A Re-ranking Approach for Person Re-identification | cs.CV | As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type... | computer science |
29,099 | Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted
Low-Rank Tensors via Convex Optimization | cs.CV | This paper studies the Tensor Robust Principal Component (TRPCA) problem
which extends the known Robust PCA (Cand${\`e}$s et al. 2011) to the tensor
case. Our model is based on a new tensor Singular Value Decomposition (t-SVD)
(Kilmer and Martin 2011) and its induced tensor tubal rank and tensor nuclear
norm. Consider ... | computer science |
29,100 | Learning Blind Motion Deblurring | cs.CV | As handheld video cameras are now commonplace and available in every
smartphone, images and videos can be recorded almost everywhere at anytime.
However, taking a quick shot frequently yields a blurry result due to unwanted
camera shake during recording or moving objects in the scene. Removing these
artifacts from the ... | computer science |
29,101 | An ELU Network with Total Variation for Image Denoising | cs.CV | In this paper, we propose a novel convolutional neural network (CNN) for
image denoising, which uses exponential linear unit (ELU) as the activation
function. We investigate the suitability by analyzing ELU's connection with
trainable nonlinear reaction diffusion model (TNRD) and residual denoising. On
the other hand, ... | computer science |
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