Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
28,502 | Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis | cs.CV | In this paper, we investigate the Chinese calligraphy synthesis problem:
synthesizing Chinese calligraphy images with specified style from standard
font(eg. Hei font) images (Fig. 1(a)). Recent works mostly follow the stroke
extraction and assemble pipeline which is complex in the process and limited by
the effect of s... | computer science |
28,503 | Approximate Reflection Symmetry in a Point Set: Theory and Algorithm
with an Application | cs.CV | We propose an algorithm to detect approximate reflection symmetry present in
a set of volumetrically distributed points belonging to $\mathbb{R}^d$
containing a distorted reflection symmetry pattern. We pose the problem of
detecting approximate reflection symmetry as the problem of establishing the
correspondences betw... | computer science |
28,504 | Recurrent Residual Learning for Action Recognition | cs.CV | Action recognition is a fundamental problem in computer vision with a lot of
potential applications such as video surveillance, human computer interaction,
and robot learning. Given pre-segmented videos, the task is to recognize
actions happening within videos. Historically, hand crafted video features were
used to add... | computer science |
28,505 | Rotational Rectification Network: Enabling Pedestrian Detection for
Mobile Vision | cs.CV | Across a majority of pedestrian detection datasets, it is typically assumed
that pedestrians will be standing upright with respect to the image coordinate
system. This assumption, however, is not always valid for many vision-equipped
mobile platforms such as mobile phones, UAVs or construction vehicles on rugged
terrai... | computer science |
28,506 | Cross-Country Skiing Gears Classification using Deep Learning | cs.CV | Human Activity Recognition has witnessed a significant progress in the last
decade. Although a great deal of work in this field goes in recognizing normal
human activities, few studies focused on identifying motion in sports.
Recognizing human movements in different sports has high impact on
understanding the different... | computer science |
28,507 | Super-Resolution via Deep Learning | cs.CV | The recent phenomenal interest in convolutional neural networks (CNNs) must
have made it inevitable for the super-resolution (SR) community to explore its
potential. The response has been immense and in the last three years, since the
advent of the pioneering work, there appeared too many works not to warrant a
compreh... | computer science |
28,508 | Classification of Medical Images and Illustrations in the Biomedical
Literature Using Synergic Deep Learning | cs.CV | The Classification of medical images and illustrations in the literature aims
to label a medical image according to the modality it was produced or label an
illustration according to its production attributes. It is an essential and
challenging research hotspot in the area of automated literature review,
retrieval and ... | computer science |
28,509 | Robust Lane Tracking with Multi-mode Observation Model and Particle
Filtering | cs.CV | Automatic lane tracking involves estimating the underlying signal from a
sequence of noisy signal observations. Many models and methods have been
proposed for lane tracking, and dynamic targets tracking in general. The Kalman
Filter is a widely used method that works well on linear Gaussian models. But
this paper shows... | computer science |
28,510 | Perceptual Adversarial Networks for Image-to-Image Transformation | cs.CV | In this paper, we propose a principled Perceptual Adversarial Networks (PAN)
for image-to-image transformation tasks. Unlike existing application-specific
algorithms, PAN provides a generic framework of learning mapping relationship
between paired images (Fig. 1), such as mapping a rainy image to its de-rained
counterp... | computer science |
28,511 | Yes-Net: An effective Detector Based on Global Information | cs.CV | This paper introduces a new real-time object detection approach named
Yes-Net. It realizes the prediction of bounding boxes and class via single
neural network like YOLOv2 and SSD, but owns more efficient and outstanding
features. It combines local information with global information by adding the
RNN architecture as a... | computer science |
28,512 | A Parameterized Approach to Personalized Variable Length Summarization
of Soccer Matches | cs.CV | We present a parameterized approach to produce personalized variable length
summaries of soccer matches. Our approach is based on temporally segmenting the
soccer video into 'plays', associating a user-specifiable 'utility' for each
type of play and using 'bin-packing' to select a subset of the plays that add
up to the... | computer science |
28,513 | The YouTube-8M Kaggle Competition: Challenges and Methods | cs.CV | We took part in the YouTube-8M Video Understanding Challenge hosted on
Kaggle, and achieved the 10th place within less than one month's time. In this
paper, we present an extensive analysis and solution to the underlying
machine-learning problem based on frame-level data, where major challenges are
identified and corre... | computer science |
28,514 | A New Urban Objects Detection Framework Using Weakly Annotated Sets | cs.CV | Urban informatics explore data science methods to address different urban
issues intensively based on data. The large variety and quantity of data
available should be explored but this brings important challenges. For
instance, although there are powerful computer vision methods that may be
explored, they may require l... | computer science |
28,515 | Online Adaptation of Convolutional Neural Networks for Video Object
Segmentation | cs.CV | We tackle the task of semi-supervised video object segmentation, i.e.
segmenting the pixels belonging to an object in the video using the ground
truth pixel mask for the first frame. We build on the recently introduced
one-shot video object segmentation (OSVOS) approach which uses a pretrained
network and fine-tunes it... | computer science |
28,516 | Summarization of ICU Patient Motion from Multimodal Multiview Videos | cs.CV | Clinical observations indicate that during critical care at the hospitals,
patients sleep positioning and motion affect recovery. Unfortunately, there is
no formal medical protocol to record, quantify, and analyze patient motion.
There is a small number of clinical studies, which use manual analysis of sleep
poses and ... | computer science |
28,517 | You Are How You Walk: Uncooperative MoCap Gait Identification for Video
Surveillance with Incomplete and Noisy Data | cs.CV | This work offers a design of a video surveillance system based on a soft
biometric -- gait identification from MoCap data. The main focus is on two
substantial issues of the video surveillance scenario: (1) the walkers do not
cooperate in providing learning data to establish their identities and (2) the
data are often ... | computer science |
28,518 | The application of deep convolutional neural networks to ultrasound for
modelling of dynamic states within human skeletal muscle | cs.CV | This paper concerns the fully automatic direct in vivo measurement of active
and passive dynamic skeletal muscle states using ultrasound imaging. Despite
the long standing medical need (myopathies, neuropathies, pain, injury,
ageing), currently technology (electromyography, dynamometry, shear wave
imaging) provides no ... | computer science |
28,519 | Real-time Distracted Driver Posture Classification | cs.CV | Distracted driving is a worldwide problem leading to an astoundingly
increasing number of accidents and deaths. Existing work is concerned with a
very small set of distractions (mostly, cell phone usage). Also, for the most
part, it uses unreliable ad-hoc methods to detect those distractions. In this
paper, we present ... | computer science |
28,520 | Flow-free Video Object Segmentation | cs.CV | Segmenting foreground object from a video is a challenging task because of
the large deformations of the objects, occlusions, and background clutter. In
this paper, we propose a frame-by-frame but computationally efficient approach
for video object segmentation by clustering visually similar generic object
segments thr... | computer science |
28,521 | R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection | cs.CV | In this paper, we propose a novel method called Rotational Region CNN (R2CNN)
for detecting arbitrary-oriented texts in natural scene images. The framework
is based on Faster R-CNN [1] architecture. First, we use the Region Proposal
Network (RPN) to generate axis-aligned bounding boxes that enclose the texts
with diffe... | computer science |
28,522 | CS591 Report: Application of siamesa network in 2D transformation | cs.CV | Deep learning has been extensively used various aspects of computer vision
area. Deep learning separate itself from traditional neural network by having a
much deeper and complicated network layers in its network structures.
Traditionally, deep neural network is abundantly used in computer vision tasks
including classi... | computer science |
28,523 | Actor-Critic Sequence Training for Image Captioning | cs.CV | Generating natural language descriptions of images is an important capability
for a robot or other visual-intelligence driven AI agent that may need to
communicate with human users about what it is seeing. Such image captioning
methods are typically trained by maximising the likelihood of ground-truth
annotated caption... | computer science |
28,524 | Weakly-supervised localization of diabetic retinopathy lesions in
retinal fundus images | cs.CV | Convolutional neural networks (CNNs) show impressive performance for image
classification and detection, extending heavily to the medical image domain.
Nevertheless, medical experts are sceptical in these predictions as the
nonlinear multilayer structure resulting in a classification outcome is not
directly graspable. ... | computer science |
28,525 | Co-salient Object Detection Based on Deep Saliency Networks and Seed
Propagation over an Integrated Graph | cs.CV | This paper presents a co-salient object detection method to find common
salient regions in a set of images. We utilize deep saliency networks to
transfer co-saliency prior knowledge and better capture high-level semantic
information, and the resulting initial co-saliency maps are enhanced by seed
propagation steps over... | computer science |
28,526 | Iterative Spectral Clustering for Unsupervised Object Localization | cs.CV | This paper addresses the problem of unsupervised object localization in an
image. Unlike previous supervised and weakly supervised algorithms that require
bounding box or image level annotations for training classifiers in order to
learn features representing the object, we propose a simple yet effective
technique for ... | computer science |
28,527 | Robust Face Tracking using Multiple Appearance Models and Graph
Relational Learning | cs.CV | This paper addresses the problem of appearance matching across different
challenges while doing visual face tracking in real-world scenarios. In this
paper, FaceTrack is proposed that utilizes multiple appearance models with its
long-term and short-term appearance memory for efficient face tracking. It
demonstrates rob... | computer science |
28,528 | What's Mine is Yours: Pretrained CNNs for Limited Training Sonar ATR | cs.CV | Finding mines in Sonar imagery is a significant problem with a great deal of
relevance for seafaring military and commercial endeavors. Unfortunately, the
lack of enormous Sonar image data sets has prevented automatic target
recognition (ATR) algorithms from some of the same advances seen in other
computer vision field... | computer science |
28,529 | Scale-Aware Face Detection | cs.CV | Convolutional neural network (CNN) based face detectors are inefficient in
handling faces of diverse scales. They rely on either fitting a large single
model to faces across a large scale range or multi-scale testing. Both are
computationally expensive. We propose Scale-aware Face Detector (SAFD) to
handle scale explic... | computer science |
28,530 | Automatic Face Image Quality Prediction | cs.CV | Face image quality can be defined as a measure of the utility of a face image
to automatic face recognition. In this work, we propose (and compare) two
methods for automatic face image quality based on target face quality values
from (i) human assessments of face image quality (matcher-independent), and
(ii) quality va... | computer science |
28,531 | Superpixel-based Semantic Segmentation Trained by Statistical Process
Control | cs.CV | Semantic segmentation, like other fields of computer vision, has seen a
remarkable performance advance by the use of deep convolution neural networks.
However, considering that neighboring pixels are heavily dependent on each
other, both learning and testing of these methods have a lot of redundant
operations. To resol... | computer science |
28,532 | Improving Speech Related Facial Action Unit Recognition by Audiovisual
Information Fusion | cs.CV | It is challenging to recognize facial action unit (AU) from spontaneous
facial displays, especially when they are accompanied by speech. The major
reason is that the information is extracted from a single source, i.e., the
visual channel, in the current practice. However, facial activity is highly
correlated with voice... | computer science |
28,533 | SMC Faster R-CNN: Toward a scene-specialized multi-object detector | cs.CV | Generally, the performance of a generic detector decreases significantly when
it is tested on a specific scene due to the large variation between the source
training dataset and the samples from the target scene. To solve this problem,
we propose a new formalism of transfer learning based on the theory of a
Sequential ... | computer science |
28,534 | Color-opponent mechanisms for local hue encoding in a hierarchical
framework | cs.CV | A biologically plausible computational model for color representation is
introduced. We present a mechanistic hierarchical model of neurons that not
only successfully encodes local hue, but also explicitly reveals how the
contributions of each visual cortical layer participating in the process can
lead to a hue represe... | computer science |
28,535 | Multiple VLAD encoding of CNNs for image classification | cs.CV | Despite the effectiveness of convolutional neural networks (CNNs) especially
in image classification tasks, the effect of convolution features on learned
representations is still limited. It mostly focuses on the salient object of
the images, but ignores the variation information on clutter and local. In this
paper, we... | computer science |
28,536 | Adversarial Image Alignment and Interpolation | cs.CV | Volumetric (3d) images are acquired for many scientific and biomedical
purposes using imaging methods such as serial section microscopy, CT scans, and
MRI. A frequent step in the analysis and reconstruction of such data is the
alignment and registration of images that were acquired in succession along a
spatial or temp... | computer science |
28,537 | Better than Real: Complex-valued Neural Nets for MRI Fingerprinting | cs.CV | The task of MRI fingerprinting is to identify tissue parameters from
complex-valued MRI signals. The prevalent approach is dictionary based, where a
test MRI signal is compared to stored MRI signals with known tissue parameters
and the most similar signals and tissue parameters retrieved. Such an approach
does not scal... | computer science |
28,538 | Image Companding and Inverse Halftoning using Deep Convolutional Neural
Networks | cs.CV | In this paper, we introduce deep learning technology to tackle two
traditional low-level image processing problems, companding and inverse
halftoning. We make two main contributions. First, to the best knowledge of the
authors, this is the first work that has successfully developed deep learning
based solutions to thes... | computer science |
28,539 | Deep GrabCut for Object Selection | cs.CV | Most previous bounding-box-based segmentation methods assume the bounding box
tightly covers the object of interest. However it is common that a rectangle
input could be too large or too small. In this paper, we propose a novel
segmentation approach that uses a rectangle as a soft constraint by
transforming it into an ... | computer science |
28,540 | Where to Play: Retrieval of Video Segments using Natural-Language
Queries | cs.CV | In this paper, we propose a new approach for retrieval of video segments
using natural language queries. Unlike most previous approaches such as
concept-based methods or rule-based structured models, the proposed method uses
image captioning model to construct sentential queries for visual information.
In detail, our a... | computer science |
28,541 | Automatic Trimap Generation for Image Matting | cs.CV | Image matting is a longstanding problem in computational photography.
Although, it has been studied for more than two decades, yet there is a
challenge of developing an automatic matting algorithm which does not require
any human efforts. Most of the state-of-the-art matting algorithms require
human intervention in the... | computer science |
28,542 | Vectorial Dimension Reduction for Tensors Based on Bayesian Inference | cs.CV | Dimensionality reduction for high-order tensors is a challenging problem. In
conventional approaches, higher order tensors are `vectorized` via Tucker
decomposition to obtain lower order tensors. This will destroy the inherent
high-order structures or resulting in undesired tensors, respectively. This
paper introduces ... | computer science |
28,543 | Joint Pose and Principal Curvature Refinement Using Quadrics | cs.CV | In this paper we present a novel joint approach for optimising surface
curvature and pose alignment. We present two implementations of this joint
optimisation strategy, including a fast implementation that uses two frames and
an offline multi-frame approach. We demonstrate an order of magnitude
improvement in simulatio... | computer science |
28,544 | Physics Inspired Optimization on Semantic Transfer Features: An
Alternative Method for Room Layout Estimation | cs.CV | In this paper, we propose an alternative method to estimate room layouts of
cluttered indoor scenes. This method enjoys the benefits of two novel
techniques. The first one is semantic transfer (ST), which is: (1) a
formulation to integrate the relationship between scene clutter and room layout
into convolutional neural... | computer science |
28,545 | A Fast Method For Computing Principal Curvatures From Range Images | cs.CV | Estimation of surface curvature from range data is important for a range of
tasks in computer vision and robotics, object segmentation, object recognition
and robotic grasping estimation. This work presents a fast method of robustly
computing accurate metric principal curvature values from noisy point clouds
which was ... | computer science |
28,546 | Pedestrian Alignment Network for Large-scale Person Re-identification | cs.CV | Person re-identification (person re-ID) is mostly viewed as an image
retrieval problem. This task aims to search a query person in a large image
pool. In practice, person re-ID usually adopts automatic detectors to obtain
cropped pedestrian images. However, this process suffers from two types of
detector errors: excess... | computer science |
28,547 | Deep Ranking Model by Large Adaptive Margin Learning for Person
Re-identification | cs.CV | Person re-identification aims to match images of the same person across
disjoint camera views, which is a challenging problem in video surveillance.
The major challenge of this task lies in how to preserve the similarity of the
same person against large variations caused by complex backgrounds, mutual
occlusions and di... | computer science |
28,548 | Detection and Localization of Image Forgeries using Resampling Features
and Deep Learning | cs.CV | Resampling is an important signature of manipulated images. In this paper, we
propose two methods to detect and localize image manipulations based on a
combination of resampling features and deep learning. In the first method, the
Radon transform of resampling features are computed on overlapping image
patches. Deep le... | computer science |
28,549 | End-to-End Learning of Video Super-Resolution with Motion Compensation | cs.CV | Learning approaches have shown great success in the task of super-resolving
an image given a low resolution input. Video super-resolution aims for
exploiting additionally the information from multiple images. Typically, the
images are related via optical flow and consecutive image warping. In this
paper, we provide an ... | computer science |
28,550 | Generalised Wasserstein Dice Score for Imbalanced Multi-class
Segmentation using Holistic Convolutional Networks | cs.CV | The Dice score is widely used for binary segmentation due to its robustness
to class imbalance. Soft generalisations of the Dice score allow it to be used
as a loss function for training convolutional neural networks (CNN). Although
CNNs trained using mean-class Dice score achieve state-of-the-art results on
multi-clas... | computer science |
28,551 | Efficient Eye Typing with 9-direction Gaze Estimation | cs.CV | Vision based text entry systems aim to help disabled people achieve text
communication using eye movement. Most previous methods have employed an
existing eye tracker to predict gaze direction and design an input method based
upon that. However, these methods can result in eye tracking quality becoming
easily affected ... | computer science |
28,552 | Siamese Learning Visual Tracking: A Survey | cs.CV | The aim of this survey is an attempt to review the kind of machine learning
and stochastic techniques and the ways existing work currently uses machine
learning and stochastic methods for the challenging problem of visual tracking.
It is not intended to study the whole tracking literature of the last decades
as this se... | computer science |
28,553 | Automatic Cardiac Disease Assessment on cine-MRI via Time-Series
Segmentation and Domain Specific Features | cs.CV | Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular
diseases by providing images at high spatiotemporal resolution. Manual
evaluation of these time-series, however, is expensive and prone to biased and
non-reproducible outcomes. In this paper, we present a method that addresses
named limitations ... | computer science |
28,554 | Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and
the Ugly | cs.CV | Due to the importance of zero-shot learning, i.e. classifying images where
there is a lack of labeled training data, the number of proposed approaches has
recently increased steadily. We argue that it is time to take a step back and
to analyze the status quo of the area. The purpose of this paper is three-fold.
First, ... | computer science |
28,555 | DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image
Segmentation | cs.CV | Accurate medical image segmentation is essential for diagnosis, surgical
planning and many other applications. Convolutional Neural Networks (CNNs) have
become the state-of-the-art automatic segmentation methods. However, fully
automatic results may still need to be refined to become accurate and robust
enough for clin... | computer science |
28,556 | Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal
Cardiac Screening Video | cs.CV | We present an automatic method to describe clinically useful information
about scanning, and to guide image interpretation in ultrasound (US) videos of
the fetal heart. Our method is able to jointly predict the visibility, viewing
plane, location and orientation of the fetal heart at the frame level. The
contributions ... | computer science |
28,557 | High-Quality Face Image SR Using Conditional Generative Adversarial
Networks | cs.CV | We propose a novel single face image super-resolution method, which named
Face Conditional Generative Adversarial Network(FCGAN), based on boundary
equilibrium generative adversarial networks. Without taking any facial prior
information, our method can generate a high-resolution face image from a
low-resolution one. Co... | computer science |
28,558 | Zero-Shot Fine-Grained Classification by Deep Feature Learning with
Semantics | cs.CV | Fine-grained image classification, which aims to distinguish images with
subtle distinctions, is a challenging task due to two main issues: lack of
sufficient training data for every class and difficulty in learning
discriminative features for representation. In this paper, to address the two
issues, we propose a two-p... | computer science |
28,559 | Deep Representation Learning with Part Loss for Person Re-Identification | cs.CV | Learning discriminative representations for unseen person images is critical
for person Re-Identification (ReID). Most of current approaches learn deep
representations in classification tasks, which essentially minimize the
empirical classification risk on the training set. As shown in our experiments,
such representat... | computer science |
28,560 | Aggregating Frame-level Features for Large-Scale Video Classification | cs.CV | This paper introduces the system we developed for the Google Cloud &
YouTube-8M Video Understanding Challenge, which can be considered as a
multi-label classification problem defined on top of the large scale YouTube-8M
Dataset. We employ a large set of techniques to aggregate the provided
frame-level feature represent... | computer science |
28,561 | Selective Deep Convolutional Features for Image Retrieval | cs.CV | Convolutional Neural Network (CNN) is a very powerful approach to extract
discriminative local descriptors for effective image search. Recent work adopts
fine-tuned strategies to further improve the discriminative power of the
descriptors. Taking a different approach, in this paper, we propose a novel
framework to achi... | computer science |
28,562 | One-Shot Fine-Grained Instance Retrieval | cs.CV | Fine-Grained Visual Categorization (FGVC) has achieved significant progress
recently. However, the number of fine-grained species could be huge and
dynamically increasing in real scenarios, making it difficult to recognize
unseen objects under the current FGVC framework. This raises an open issue to
perform large-scale... | computer science |
28,563 | Spatial and Angular Resolution Enhancement of Light Fields Using
Convolutional Neural Networks | cs.CV | Light field imaging extends the traditional photography by capturing both
spatial and angular distribution of light, which enables new capabilities,
including post-capture refocusing, post-capture aperture control, and depth
estimation from a single shot. Micro-lens array (MLA) based light field cameras
offer a cost-ef... | computer science |
28,564 | Learning Human Pose Models from Synthesized Data for Robust RGB-D Action
Recognition | cs.CV | We propose Human Pose Models that represent RGB and depth images of human
poses independent of clothing textures, backgrounds, lighting conditions, body
shapes and camera viewpoints. Learning such universal models requires training
images where all factors are varied for every human pose. Capturing such data
is prohibi... | computer science |
28,565 | Face Recognition with Machine Learning in OpenCV_ Fusion of the results
with the Localization Data of an Acoustic Camera for Speaker Identification | cs.CV | This contribution gives an overview of face recogni-tion algorithms, their
implementation and practical uses. First, a training set of different persons'
faces has to be collected and used to train a face recognizer. The resulting
face model can be utilized to classify people in specific individuals or
unknowns. After ... | computer science |
28,566 | The Candidate Multi-Cut for Cell Segmentation | cs.CV | Two successful approaches for the segmentation of biomedical images are (1)
the selection of segment candidates from a merge-tree, and (2) the clustering
of small superpixels by solving a Multi-Cut problem. In this paper, we
introduce a model that unifies both approaches. Our model, the Candidate
Multi-Cut (CMC), allow... | computer science |
28,567 | LED-based Photometric Stereo: Modeling, Calibration and Numerical
Solution | cs.CV | We conduct a thorough study of photometric stereo under nearby point light
source illumination, from modeling to numerical solution, through calibration.
In the classical formulation of photometric stereo, the luminous fluxes are
assumed to be directional, which is very difficult to achieve in practice.
Rather, we use ... | computer science |
28,568 | Skeleton-aided Articulated Motion Generation | cs.CV | This work make the first attempt to generate articulated human motion
sequence from a single image. On the one hand, we utilize paired inputs
including human skeleton information as motion embedding and a single human
image as appearance reference, to generate novel motion frames, based on the
conditional GAN infrastru... | computer science |
28,569 | ShuffleNet: An Extremely Efficient Convolutional Neural Network for
Mobile Devices | cs.CV | We introduce an extremely computation-efficient CNN architecture named
ShuffleNet, which is designed specially for mobile devices with very limited
computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new
operations, pointwise group convolution and channel shuffle, to greatly reduce
computation cost... | computer science |
28,570 | Discriminative Localization in CNNs for Weakly-Supervised Segmentation
of Pulmonary Nodules | cs.CV | Automated detection and segmentation of pulmonary nodules on lung computed
tomography (CT) scans can facilitate early lung cancer diagnosis. Existing
supervised approaches for automated nodule segmentation on CT scans require
voxel-based annotations for training, which are labor- and time-consuming to
obtain. In this w... | computer science |
28,571 | UPSET and ANGRI : Breaking High Performance Image Classifiers | cs.CV | In this paper, targeted fooling of high performance image classifiers is
achieved by developing two novel attack methods. The first method generates
universal perturbations for target classes and the second generates image
specific perturbations. Extensive experiments are conducted on MNIST and
CIFAR10 datasets to prov... | computer science |
28,572 | A Survey of Recent Advances in CNN-based Single Image Crowd Counting and
Density Estimation | cs.CV | Estimating count and density maps from crowd images has a wide range of
applications such as video surveillance, traffic monitoring, public safety and
urban planning. In addition, techniques developed for crowd counting can be
applied to related tasks in other fields of study such as cell microscopy,
vehicle counting a... | computer science |
28,573 | Exploration of object recognition from 3D point cloud | cs.CV | We present our latest experiment results of object recognition from 3D point
cloud data collected through moving car. | computer science |
28,574 | Laplacian-Steered Neural Style Transfer | cs.CV | Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to
synthesize a new image that retains the high-level structure of a content
image, rendered in the low-level texture of a style image. This is achieved by
constraining the new image to have high-level CNN features similar to the
content image, and... | computer science |
28,575 | Learning-based Image Enhancement for Visual Odometry in Challenging HDR
Environments | cs.CV | Most approaches to stereo visual odometry reconstruct the motion based on the
tracking of point features along a sequence of images. However, in low-textured
scenes it is often difficult to encounter a large set of point features, or it
may happen that they are not well distributed over the image, so that the
behavior ... | computer science |
28,576 | R-PHOC: Segmentation-Free Word Spotting using CNN | cs.CV | This paper proposes a region based convolutional neural network for
segmentation-free word spotting. Our net- work takes as input an image and a
set of word candidate bound- ing boxes and embeds all bounding boxes into an
embedding space, where word spotting can be casted as a simple nearest
neighbour search between th... | computer science |
28,577 | Fast Multi-frame Stereo Scene Flow with Motion Segmentation | cs.CV | We propose a new multi-frame method for efficiently computing scene flow
(dense depth and optical flow) and camera ego-motion for a dynamic scene
observed from a moving stereo camera rig. Our technique also segments out
moving objects from the rigid scene. In our method, we first estimate the
disparity map and the 6-DO... | computer science |
28,578 | Benchmarking Denoising Algorithms with Real Photographs | cs.CV | Lacking realistic ground truth data, image denoising techniques are
traditionally evaluated on images corrupted by synthesized i.i.d. Gaussian
noise. We aim to obviate this unrealistic setting by developing a methodology
for benchmarking denoising techniques on real photographs. We capture pairs of
images with differen... | computer science |
28,579 | Robust Multi-Image HDR Reconstruction for the Modulo Camera | cs.CV | Photographing scenes with high dynamic range (HDR) poses great challenges to
consumer cameras with their limited sensor bit depth. To address this, Zhao et
al. recently proposed a novel sensor concept - the modulo camera - which
captures the least significant bits of the recorded scene instead of going into
saturation.... | computer science |
28,580 | A dataset for Computer-Aided Detection of Pulmonary Embolism in CTA
images | cs.CV | Todays, researchers in the field of Pulmonary Embolism (PE) analysis need to
use a publicly available dataset to assess and compare their methods. Different
systems have been designed for the detection of pulmonary embolism (PE), but
none of them have used any public datasets. All papers have used their own
private dat... | computer science |
28,581 | Generative diffeomorphic atlas construction from brain and spinal cord
MRI data | cs.CV | In this paper we will focus on the potential and on the challenges associated
with the development of an integrated brain and spinal cord modelling framework
for processing MR neuroimaging data. The aim of the work is to explore how a
hierarchical generative model of imaging data, which captures simultaneously
the dist... | computer science |
28,582 | AlignGAN: Learning to Align Cross-Domain Images with Conditional
Generative Adversarial Networks | cs.CV | Recently, several methods based on generative adversarial network (GAN) have
been proposed for the task of aligning cross-domain images or learning a joint
distribution of cross-domain images. One of the methods is to use conditional
GAN for alignment. However, previous attempts of adopting conditional GAN do
not perfo... | computer science |
28,583 | Video Representation Learning and Latent Concept Mining for Large-scale
Multi-label Video Classification | cs.CV | We report on CMU Informedia Lab's system used in Google's YouTube 8 Million
Video Understanding Challenge. In this multi-label video classification task,
our pipeline achieved 84.675% and 84.662% GAP on our evaluation split and the
official test set. We attribute the good performance to three components: 1)
Refined vid... | computer science |
28,584 | Dual Path Networks | cs.CV | In this work, we present a simple, highly efficient and modularized Dual Path
Network (DPN) for image classification which presents a new topology of
connection paths internally. By revealing the equivalence of the
state-of-the-art Residual Network (ResNet) and Densely Convolutional Network
(DenseNet) within the HORNN ... | computer science |
28,585 | RON: Reverse Connection with Objectness Prior Networks for Object
Detection | cs.CV | We present RON, an efficient and effective framework for generic object
detection. Our motivation is to smartly associate the best of the region-based
(e.g., Faster R-CNN) and region-free (e.g., SSD) methodologies. Under fully
convolutional architecture, RON mainly focuses on two fundamental problems: (a)
multi-scale o... | computer science |
28,586 | Computer methods for 3D motion tracking in real-time | cs.CV | This thesis is devoted to marker-less 3D human motion tracking in calibrated
and synchronized multicamera systems. Pose estimation is based on a 3D model,
which is transformed into the image plane and then rendered. Owing to
elaborated techniques the tracking of the full body has been achieved in
real-time via dynamic ... | computer science |
28,587 | Tensor-Train Recurrent Neural Networks for Video Classification | cs.CV | The Recurrent Neural Networks and their variants have shown promising
performances in sequence modeling tasks such as Natural Language Processing.
These models, however, turn out to be impractical and difficult to train when
exposed to very high-dimensional inputs due to the large input-to-hidden weight
matrix. This ma... | computer science |
28,588 | Cardiologist-Level Arrhythmia Detection with Convolutional Neural
Networks | cs.CV | We develop an algorithm which exceeds the performance of board certified
cardiologists in detecting a wide range of heart arrhythmias from
electrocardiograms recorded with a single-lead wearable monitor. We build a
dataset with more than 500 times the number of unique patients than previously
studied corpora. On this d... | computer science |
28,589 | Zero-Shot Deep Domain Adaptation | cs.CV | The existing methods of domain adaptation (DA) work under the assumption that
the task-relevant target-domain training data is given. However, such
assumption can be violated, which is often ignored by the prior works. To
tackle this issue, we propose zero-shot deep domain adaptation (ZDDA), which
uses the privileged i... | computer science |
28,590 | A Generalised Seizure Prediction with Convolutional Neural Networks for
Intracranial and Scalp Electroencephalogram Data Analysis | cs.CV | Seizure prediction has attracted a growing attention as one of the most
challenging predictive data analysis efforts in order to improve the life of
patients living with drug-resistant epilepsy and tonic seizures. Many
outstanding works have been reporting great results in providing a sensible
indirect (warning systems... | computer science |
28,591 | On the Compactness, Efficiency, and Representation of 3D Convolutional
Networks: Brain Parcellation as a Pretext Task | cs.CV | Deep convolutional neural networks are powerful tools for learning visual
representations from images. However, designing efficient deep architectures to
analyse volumetric medical images remains challenging. This work investigates
efficient and flexible elements of modern convolutional networks such as
dilated convolu... | computer science |
28,592 | Automatic Classification of Bright Retinal Lesions via Deep Network
Features | cs.CV | The diabetic retinopathy is timely diagonalized through color eye fundus
images by experienced ophthalmologists, in order to recognize potential retinal
features and identify early-blindness cases. In this paper, it is proposed to
extract deep features from the last fully-connected layer of, four different,
pre-trained... | computer science |
28,593 | Image Segmentation Algorithms Overview | cs.CV | The technology of image segmentation is widely used in medical image
processing, face recognition pedestrian detection, etc. The current image
segmentation techniques include region-based segmentation, edge detection
segmentation, segmentation based on clustering, segmentation based on
weakly-supervised learning in CNN... | computer science |
28,594 | A spatiotemporal model with visual attention for video classification | cs.CV | High level understanding of sequential visual input is important for safe and
stable autonomy, especially in localization and object detection. While
traditional object classification and tracking approaches are specifically
designed to handle variations in rotation and scale, current state-of-the-art
approaches based ... | computer science |
28,595 | Deep Discrete Hashing with Self-supervised Pairwise Labels | cs.CV | Hashing methods have been widely used for applications of large-scale image
retrieval and classification. Non-deep hashing methods using handcrafted
features have been significantly outperformed by deep hashing methods due to
their better feature representation and end-to-end learning framework. However,
the most strik... | computer science |
28,596 | Sparse Approximation of 3D Meshes using the Spectral Geometry of the
Hamiltonian Operator | cs.CV | The discrete Laplace operator is ubiquitous in spectral shape analysis, since
its eigenfunctions are provably optimal in representing smooth functions
defined on the surface of the shape. Indeed, subspaces defined by its
eigenfunctions have been utilized for shape compression, treating the
coordinates as smooth functio... | computer science |
28,597 | SigNet: Convolutional Siamese Network for Writer Independent Offline
Signature Verification | cs.CV | Offline signature verification is one of the most challenging tasks in
biometrics and document forensics. Unlike other verification problems, it needs
to model minute but critical details between genuine and forged signatures,
because a skilled falsification might often resembles the real signature with
small deformati... | computer science |
28,598 | Design and Processing of Invertible Orientation Scores of 3D Images for
Enhancement of Complex Vasculature | cs.CV | The enhancement and detection of elongated structures in noisy image data is
relevant for many biomedical imaging applications. To handle complex crossing
structures in 2D images, 2D orientation scores $U: \mathbb{R} ^ 2\times S ^ 1
\rightarrow \mathbb{C}$ were introduced, which already showed their use in a
variety of... | computer science |
28,599 | A multi-layer image representation using Regularized Residual
Quantization: application to compression and denoising | cs.CV | A learning-based framework for representation of domain-specific images is
proposed where joint compression and denoising can be done using a VQ-based
multi-layer network. While it learns to compress the images from a training
set, the compression performance is very well generalized on images from a test
set. Moreover... | computer science |
28,600 | The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation | cs.CV | We present the 2017 Hands in the Million Challenge, a public competition
designed for the evaluation of the task of 3D hand pose estimation. The goal of
this challenge is to assess how far is the state of the art in terms of solving
the problem of 3D hand pose estimation as well as detect major failure and
strength mod... | computer science |
28,601 | Generative Adversarial Models for People Attribute Recognition in
Surveillance | cs.CV | In this paper we propose a deep architecture for detecting people attributes
(e.g. gender, race, clothing ...) in surveillance contexts. Our proposal
explicitly deal with poor resolution and occlusion issues that often occur in
surveillance footages by enhancing the images by means of Deep Convolutional
Generative Adve... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.