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31,502 | LEGO: Learning Edge with Geometry all at Once by Watching Videos | cs.CV | Learning to estimate 3D geometry in a single image by watching unlabeled
videos via deep convolutional network is attracting significant attention. In
this paper, we introduce a "3D as-smooth-as-possible (3D-ASAP)" priori inside
the pipeline, which enables joint estimation of edges and 3D scene, yielding
results with s... | computer science |
31,503 | VEGAC: Visual Saliency-based Age, Gender, and Facial Expression
Classification Using Convolutional Neural Networks | cs.CV | This paper explores the use of Visual Saliency to Classify Age, Gender and
Facial Expression for Facial Images. For multi-task classification, we propose
our method VEGAC, which is based on Visual Saliency. Using the Deep Multi-level
Network [1] and off-the-shelf face detector [2], our proposed method first
detects the... | computer science |
31,504 | Exploring Linear Relationship in Feature Map Subspace for ConvNets
Compression | cs.CV | While the research on convolutional neural networks (CNNs) is progressing
quickly, the real-world deployment of these models is often limited by
computing resources and memory constraints. In this paper, we address this
issue by proposing a novel filter pruning method to compress and accelerate
CNNs. Our work is based ... | computer science |
31,505 | Diverse M-Best Solutions by Dynamic Programming | cs.CV | Many computer vision pipelines involve dynamic programming primitives such as
finding a shortest path or the minimum energy solution in a tree-shaped
probabilistic graphical model. In such cases, extracting not merely the best,
but the set of M-best solutions is useful to generate a rich collection of
candidate proposa... | computer science |
31,506 | What Catches the Eye? Visualizing and Understanding Deep Saliency Models | cs.CV | Deep convolutional neural networks have demonstrated high performances for
fixation prediction in recent years. How they achieve this, however, is less
explored and they remain to be black box models. Here, we attempt to shed light
on the internal structure of deep saliency models and study what features they
extract f... | computer science |
31,507 | Salient Region Segmentation | cs.CV | Saliency prediction is a well studied problem in computer vision. Early
saliency models were based on low-level hand-crafted feature derived from
insights gained in neuroscience and psychophysics. In the wake of deep learning
breakthrough, a new cohort of models were proposed based on neural network
architectures, allo... | computer science |
31,508 | Using accumulation to optimize deep residual neural nets | cs.CV | Residual Neural Networks [1] won first place in all five main tracks of the
ImageNet and COCO 2015 competitions. This kind of network involves the creation
of pluggable modules such that the output contains a residual from the input.
The residual in that paper is the identity function. We propose to include
residuals f... | computer science |
31,509 | A predictor-corrector method for the training of deep neural networks | cs.CV | The training of deep neural nets is expensive. We present a predictor-
corrector method for the training of deep neural nets. It alternates a
predictor pass with a corrector pass using stochastic gradient descent with
backpropagation such that there is no loss in validation accuracy. No special
modifications to SGD wit... | computer science |
31,510 | Aggregated Sparse Attention for Steering Angle Prediction | cs.CV | In this paper, we apply the attention mechanism to autonomous driving for
steering angle prediction. We propose the first model, applying the recently
introduced sparse attention mechanism to visual domain, as well as the
aggregated extension for this model. We show the improvement of the proposed
method, comparing to ... | computer science |
31,511 | Temporal Human Action Segmentation via Dynamic Clustering | cs.CV | We present an effective dynamic clustering algorithm for the task of temporal
human action segmentation, which has comprehensive applications such as
robotics, motion analysis, and patient monitoring. Our proposed algorithm is
unsupervised, fast, generic to process various types of features, and
applicable in both the ... | computer science |
31,512 | A Structural Correlation Filter Combined with A Multi-task Gaussian
Particle Filter for Visual Tracking | cs.CV | In this paper, we propose a novel structural correlation filter combined with
a multi-task Gaussian particle filter (KCF-GPF) model for robust visual
tracking. We first present an assemble structure where several KCF trackers as
weak experts provide a preliminary decision for a Gaussian particle filter to
make a final ... | computer science |
31,513 | Accurate Facial Parts Localization and Deep Learning for 3D Facial
Expression Recognition | cs.CV | Meaningful facial parts can convey key cues for both facial action unit
detection and expression prediction. Textured 3D face scan can provide both
detailed 3D geometric shape and 2D texture appearance cues of the face which
are beneficial for Facial Expression Recognition (FER). However, accurate
facial parts extracti... | computer science |
31,514 | Image Registration Based Flicker Solving in Video Face Replacement and
Analysis Based Sub-pixel Image Registration | cs.CV | In this paper, a framework of video face replacement is proposed and it deals
with the flicker of swapped face in video sequence. This framework contains two
main innovations: 1) the technique of image registration is exploited to align
the source and target video faces for eliminating the flicker or jitter of the
segm... | computer science |
31,515 | Development and Validation of Deep Learning Algorithms for Detection of
Critical Findings in Head CT Scans | cs.CV | Importance: Non-contrast head CT scan is the current standard for initial
imaging of patients with head trauma or stroke symptoms.
Objective: To develop and validate a set of deep learning algorithms for
automated detection of following key findings from non-contrast head CT scans:
intracranial hemorrhage (ICH) and i... | computer science |
31,516 | Pseudo Mask Augmented Object Detection | cs.CV | In this work, we present a novel and effective framework to facilitate object
detection with the instance-level segmentation information that is only
supervised by bounding box annotation. Starting from the joint object detection
and instance segmentation network, we propose to recursively estimate the
pseudo ground-tr... | computer science |
31,517 | Learned Iterative Decoding for Lossy Image Compression Systems | cs.CV | For lossy image compression systems, we develop an algorithm called iterative
refinement, to improve the decoder's reconstruction compared with standard
decoding techniques. Specifically, we propose a recurrent neural network
approach for nonlinear, iterative decoding. Our neural decoder, which can work
with any encode... | computer science |
31,518 | Virtual CNN Branching: Efficient Feature Ensemble for Person
Re-Identification | cs.CV | In this paper we introduce an ensemble method for convolutional neural
network (CNN), called "virtual branching," which can be implemented with nearly
no additional parameters and computation on top of standard CNNs. We propose
our method in the context of person re-identification (re-ID). Our CNN model
consists of sha... | computer science |
31,519 | Deep Structure Inference Network for Facial Action Unit Recognition | cs.CV | Facial expressions are combinations of basic components called Action Units
(AU). Recognizing AUs is key for developing general facial expression analysis.
In recent years, most efforts in automatic AU recognition have been dedicated
to learning combinations of local features and to exploiting correlations
between Acti... | computer science |
31,520 | Efficient Hardware Realization of Convolutional Neural Networks using
Intra-Kernel Regular Pruning | cs.CV | The recent trend toward increasingly deep convolutional neural networks
(CNNs) leads to a higher demand of computational power and memory storage.
Consequently, the deployment of CNNs in hardware has become more challenging.
In this paper, we propose an Intra-Kernel Regular (IKR) pruning scheme to
reduce the size and c... | computer science |
31,521 | A picture is worth a thousand words but how to organize thousands of
pictures? | cs.CV | We live in a society where the large majority of the population has a
camera-equipped smartphone. In addition, hard drives and cloud storage are
getting cheaper and cheaper, leading to a tremendous growth in stored personal
photos. Unlike photo collections captured by a digital camera, which typically
are pre-processed... | computer science |
31,522 | Mo2Cap2: Real-time Mobile 3D Motion Capture with a Cap-mounted Fisheye
Camera | cs.CV | We propose the first real-time approach for the egocentric estimation of 3D
human body pose in a wide range of unconstrained everyday activities. This
setting has a unique set of challenges, such as mobility of the hardware setup,
and robustness to long capture sessions with fast recovery from tracking
failures. We tac... | computer science |
31,523 | Studying Invariances of Trained Convolutional Neural Networks | cs.CV | Convolutional Neural Networks (CNNs) define an exceptionally powerful class
of models for image classification, but the theoretical background and the
understanding of how invariances to certain transformations are learned is
limited. In a large scale screening with images modified by different affine
and nonaffine tra... | computer science |
31,524 | Real-time Deep Registration With Geodesic Loss | cs.CV | With an aim to increase the capture range and accelerate the performance of
state-of-the-art inter-subject and subject-to-template 3D registration, we
propose deep learning-based methods that are trained to find the 3D position of
arbitrarily oriented subjects or anatomy based on slices or volumes of medical
images. Fo... | computer science |
31,525 | Deep Co-Training for Semi-Supervised Image Recognition | cs.CV | In this paper, we study the problem of semi-supervised image recognition,
which is to learn classifiers using both labeled and unlabeled images. We
present Deep Co-Training, a deep learning based method inspired by the
Co-Training framework. The original Co-Training learns two classifiers on two
views which are data fr... | computer science |
31,526 | Zero-Shot Object Detection: Learning to Simultaneously Recognize and
Localize Novel Concepts | cs.CV | Current Zero-Shot Learning (ZSL) approaches are restricted to recognition of
a single dominant unseen object category in a test image. We hypothesize that
this setting is ill-suited for real-world applications where unseen objects
appear only as a part of a complex scene, warranting both the `recognition' and
`localiza... | computer science |
31,527 | Deep Multiple Instance Learning for Zero-shot Image Tagging | cs.CV | In-line with the success of deep learning on traditional recognition problem,
several end-to-end deep models for zero-shot recognition have been proposed in
the literature. These models are successful to predict a single unseen label
given an input image, but does not scale to cases where multiple unseen objects
are pr... | computer science |
31,528 | Dynamic-structured Semantic Propagation Network | cs.CV | Semantic concept hierarchy is still under-explored for semantic segmentation
due to the inefficiency and complicated optimization of incorporating
structural inference into dense prediction. This lack of modeling semantic
correlations also makes prior works must tune highly-specified models for each
task due to the lab... | computer science |
31,529 | Real-time Detection, Tracking, and Classification of Moving and
Stationary Objects using Multiple Fisheye Images | cs.CV | The ability to detect pedestrians and other moving objects is crucial for an
autonomous vehicle. This must be done in real-time with minimum system
overhead. This paper discusses the implementation of a surround view system to
identify moving as well as static objects that are close to the ego vehicle.
The algorithm wo... | computer science |
31,530 | Salient Objects in Clutter: Bringing Salient Object Detection to the
Foreground | cs.CV | In this paper, we provide a comprehensive evaluation of salient object
detection (SOD) models. Our analysis identifies a serious design bias of
existing SOD datasets which assumes that each image contains at least one
clearly outstanding salient object in low clutter. This is an unrealistic
assumption. The design bias ... | computer science |
31,531 | Varying k-Lipschitz Constraint for Generative Adversarial Networks | cs.CV | Generative Adversarial Networks (GANs) are powerful generative models, but
suffer from training instability. The recent proposed Wasserstein GAN with
gradient penalty (WGAN-GP) makes progress toward stable training. Gradient
penalty acts as the role of enforcing a Lipschitz constraint. Further
investigation on gradient... | computer science |
31,532 | Towards Image Understanding from Deep Compression without Decoding | cs.CV | Motivated by recent work on deep neural network (DNN)-based image compression
methods showing potential improvements in image quality, savings in storage,
and bandwidth reduction, we propose to perform image understanding tasks such
as classification and segmentation directly on the compressed representations
produced ... | computer science |
31,533 | Patchwise object tracking via structural local sparse appearance model | cs.CV | In this paper, we propose a robust visual tracking method which exploits the
relationships of targets in adjacent frames using patchwise joint sparse
representation. Two sets of overlapping patches with different sizes are
extracted from target candidates to construct two dictionaries with
consideration of joint sparse... | computer science |
31,534 | Object Captioning and Retrieval with Natural Language | cs.CV | We address the problem of jointly learning vision and language to understand
the object in a fine-grained manner. The key idea of our approach is the use of
object descriptions to provide the detailed understanding of an object. Based
on this idea, we propose two new architectures to solve two related problems:
object ... | computer science |
31,535 | Semantic Segmentation of Pathological Lung Tissue with Dilated Fully
Convolutional Networks | cs.CV | Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial
for making treatment decisions, but can be challenging even for experienced
radiologists. The diagnostic procedure is based on the detection and
recognition of the different ILD pathologies in thoracic CT scans, yet their
manifestation often a... | computer science |
31,536 | The ApolloScape Dataset for Autonomous Driving | cs.CV | Scene parsing aims to assign a class (semantic) label for each pixel in an
image. It is a comprehensive analysis of an image. Given the rise of autonomous
driving, pixel-accurate environmental perception is expected to be a key
enabling technical piece. However, providing a large scale dataset for the
design and evalua... | computer science |
31,537 | Triplet-Center Loss for Multi-View 3D Object Retrieval | cs.CV | Most existing 3D object recognition algorithms focus on leveraging the strong
discriminative power of deep learning models with softmax loss for the
classification of 3D data, while learning discriminative features with deep
metric learning for 3D object retrieval is more or less neglected. In the
paper, we study varia... | computer science |
31,538 | Monocular Fisheye Camera Depth Estimation Using Semi-supervised Sparse
Velodyne Data | cs.CV | Near field depth estimation around a self driving car is an important
function that can be achieved by four wide angle fisheye cameras having a field
of view of over 180. CNN based depth estimation produce state of the art
results, but progress is hindered because depth annotation cannot be obtained
manually. Synthetic... | computer science |
31,539 | Complex-YOLO: Real-time 3D Object Detection on Point Clouds | cs.CV | Lidar based 3D object detection is inevitable for autonomous driving, because
it directly links to environmental understanding and therefore builds the base
for prediction and motion planning. The capacity of inferencing highly sparse
3D data in real-time is an ill-posed problem for lots of other application
areas besi... | computer science |
31,540 | Land cover mapping at very high resolution with rotation equivariant
CNNs: towards small yet accurate models | cs.CV | In remote sensing images, the absolute orientation of objects is arbitrary.
Depending on an object's orientation and on a sensor's flight path, objects of
the same semantic class can be observed in different orientations in the same
image. Equivariance to rotation, in this context understood as responding with
a rotate... | computer science |
31,541 | Improved Part Segmentation Performance by Optimising Realism of
Synthetic Images using Cycle Generative Adversarial Networks | cs.CV | In this paper we report on improved part segmentation performance using
convolutional neural networks to reduce the dependency on the large amount of
manually annotated empirical images. This was achieved by optimising the visual
realism of synthetic agricultural images.In Part I, a cycle consistent
generative adversar... | computer science |
31,542 | Activity Detection with Latent Sub-event Hierarchy Learning | cs.CV | In this paper, we introduce a new convolutional layer named the Temporal
Gaussian Mixture (TGM) layer and present how it can be used to efficiently
capture temporal structure in continuous activity videos. Our layer is designed
to allow the model to learn a latent hierarchy of sub-event intervals. Our
approach is fully... | computer science |
31,543 | Learning deep structured active contours end-to-end | cs.CV | The world is covered with millions of buildings, and precisely knowing each
instance's position and extents is vital to a multitude of applications.
Recently, automated building footprint segmentation models have shown superior
detection accuracy thanks to the usage of Convolutional Neural Networks (CNN).
However, even... | computer science |
31,544 | Faces as Lighting Probes via Unsupervised Deep Highlight Extraction | cs.CV | We present a method for estimating detailed scene illumination using human
faces in a single image. In contrast to previous works that estimate lighting
in terms of low-order basis functions or distant point lights, our technique
estimates illumination at a higher precision in the form of a non-parametric
environment m... | computer science |
31,545 | A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing | cs.CV | Tensor-based methods have recently emerged as a more natural and effective
formulation to address many problems in hyperspectral imaging. In hyperspectral
unmixing (HU), low-rank constraints on the abundance maps have been shown to
act as a regularization which adequately accounts for the multidimensional
structure of ... | computer science |
31,546 | Learning to Segment via Cut-and-Paste | cs.CV | This paper presents a weakly-supervised approach to object instance
segmentation. Starting with known or predicted object bounding boxes, we learn
object masks by playing a game of cut-and-paste in an adversarial learning
setup. A mask generator takes a detection box and Faster R-CNN features, and
constructs a segmenta... | computer science |
31,547 | Robust event-stream pattern tracking based on correlative filter | cs.CV | Object tracking based on retina-inspired and event-based dynamic vision
sensor (DVS) is challenging for the noise events, rapid change of event-stream
shape, chaos of complex background textures, and occlusion. To address these
challenges, this paper presents a robust event-stream pattern tracking method
based on corre... | computer science |
31,548 | Weakly Supervised Salient Object Detection Using Image Labels | cs.CV | Deep learning based salient object detection has recently achieved great
success with its performance greatly outperforms any other unsupervised
methods. However, annotating per-pixel saliency masks is a tedious and
inefficient procedure. In this paper, we note that superior salient object
detection can be obtained by ... | computer science |
31,549 | Learning Unsupervised Visual Grounding Through Semantic Self-Supervision | cs.CV | Localizing natural language phrases in images is a challenging problem that
requires joint understanding of both the textual and visual modalities. In the
unsupervised setting, lack of supervisory signals exacerbate this difficulty.
In this paper, we propose a novel framework for unsupervised visual grounding
which use... | computer science |
31,550 | SeqFace: Make full use of sequence information for face recognitio | cs.CV | Deep convolutional neural networks (CNNs) have greatly improved the Face
Recognition (FR) performance in recent years. Almost all CNNs in FR are trained
on the carefully labeled datasets containing plenty of identities. However,
such high-quality datasets are very expensive to collect, which restricts many
researchers ... | computer science |
31,551 | Adaptive strategy for superpixel-based region-growing image segmentation | cs.CV | This work presents a region-growing image segmentation approach based on
superpixel decomposition. From an initial contour-constrained over-segmentation
of the input image, the image segmentation is achieved by iteratively merging
similar superpixels into regions. This approach raises two key issues: (1) how
to compute... | computer science |
31,552 | A Multi-perspective Approach To Anomaly Detection For Self-aware
Embodied Agents | cs.CV | This paper focuses on multi-sensor anomaly detection for moving cognitive
agents using both external and private first-person visual observations. Both
observation types are used to characterize agents' motion in a given
environment. The proposed method generates locally uniform motion models by
dividing a Gaussian pro... | computer science |
31,553 | Deep Learning for Nonlinear Diffractive Imaging | cs.CV | Image reconstruction under multiple light scattering is crucial for a number
of important applications in cell microscopy and tissue imaging. The
reconstruction problem is often formulated as a nonconvex optimization, where a
nonlinear measurement model is used to account for multiple scattering and a
regularizer is us... | computer science |
31,554 | Facial Landmarks Detection by Self-Iterative Regression based
Landmarks-Attention Network | cs.CV | Cascaded Regression (CR) based methods have been proposed to solve facial
landmarks detection problem, which learn a series of descent directions by
multiple cascaded regressors separately trained in coarse and fine stages. They
outperform the traditional gradient descent based methods in both accuracy and
running spee... | computer science |
31,555 | Dynamic Trajectory Model for Analysis of Traffic States using DPMM | cs.CV | Appropriate modeling of a surveillance scene is essential while analyzing and
detecting anomalies in road traffic. Learning usual paths can provide much
insight into road traffic situation and to identify abnormal routes taken by
commuters/vehicles in a traffic scene. If usual traffic paths are learned in a
nonparametr... | computer science |
31,556 | The Automatic Identification of Butterfly Species | cs.CV | The available butterfly data sets comprise a few limited species, and the
images in the data sets are always standard patterns without the images of
butterflies in their living environment. To overcome the aforementioned
limitations in the butterfly data sets, we build a butterfly data set composed
of all species of bu... | computer science |
31,557 | Cross-modality image synthesis from unpaired data using CycleGAN:
Effects of gradient consistency loss and training data size | cs.CV | CT is commonly used in orthopedic procedures. MRI is used along with CT to
identify muscle structures and diagnose osteonecrosis due to its superior soft
tissue contrast. However, MRI has poor contrast for bone structures. Clearly,
it would be helpful if a corresponding CT were available, as bone boundaries
are more cl... | computer science |
31,558 | Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains | cs.CV | Despite the recent success of stereo matching with convolutional neural
networks (CNNs), it remains arduous to generalize a pre-trained deep stereo
model to a novel domain. A major difficulty is to collect accurate ground-truth
disparities for stereo pairs in the target domain. In this work, we propose a
self-adaptatio... | computer science |
31,559 | Line Artist: A Multiple Style Sketch to Painting Synthesis Scheme | cs.CV | Drawing a beautiful painting is a dream of many people since childhood. In
this paper, we propose a novel scheme, Line Artist, to synthesize artistic
style paintings with freehand sketch images, leveraging the power of deep
learning and advanced algorithms. Our scheme includes three models. The Sketch
Image Extraction ... | computer science |
31,560 | Ratio-Preserving Half-Cylindrical Warps for Natural Image Stitching | cs.CV | A novel warp for natural image stitching is proposed that utilizes the
property of cylindrical warp and a horizontal pixel selection strategy. The
proposed ratio-preserving half-cylindrical warp is a combination of homography
and cylindrical warps which guarantees alignment by homography and possesses
less projective d... | computer science |
31,561 | Sdf-GAN: Semi-supervised Depth Fusion with Multi-scale Adversarial
Networks | cs.CV | Fusing disparity maps from different algorithms to exploit their
complementary advantages is still challenging. Uncertainty estimation and
complex disparity relationships between neighboring pixels limit the accuracy
and robustness of the existing methods and there is no common method for depth
fusion of different kind... | computer science |
31,562 | Discriminative Learning of Latent Features for Zero-Shot Recognition | cs.CV | Zero-shot learning (ZSL) aims to recognize unseen image categories by
learning an embedding space between image and semantic representations. For
years, among existing works, it has been the center task to learn the proper
mapping matrices aligning the visual and semantic space, whilst the importance
to learn discrimin... | computer science |
31,563 | White matter hyperintensity segmentation from T1 and FLAIR images using
fully convolutional neural networks enhanced with residual connections | cs.CV | Segmentation and quantification of white matter hyperintensities (WMHs) are
of great importance in studying and understanding various neurological and
geriatric disorders. Although automatic methods have been proposed for WMH
segmentation on magnetic resonance imaging (MRI), manual corrections are often
necessary to ac... | computer science |
31,564 | Depth-aware CNN for RGB-D Segmentation | cs.CV | Convolutional neural networks (CNN) are limited by the lack of capability to
handle geometric information due to the fixed grid kernel structure. The
availability of depth data enables progress in RGB-D semantic segmentation with
CNNs. State-of-the-art methods either use depth as additional images or process
spatial in... | computer science |
31,565 | Attention-GAN for Object Transfiguration in Wild Images | cs.CV | This paper studies the object transfiguration problem in wild images. The
generative network in classical GANs for object transfiguration often
undertakes a dual responsibility: to detect the objects of interests and to
convert the object from source domain to target domain. In contrast, we
decompose the generative net... | computer science |
31,566 | Revisiting RCNN: On Awakening the Classification Power of Faster RCNN | cs.CV | Recent region-based object detectors are usually built with separate
classification and localization branches on top of shared feature extraction
networks. In this paper, we analyze failure cases of state-of-the-art detectors
and observe that most hard false positives result from classification instead
of localization.... | computer science |
31,567 | Alive Caricature from 2D to 3D | cs.CV | Caricature is an art form that expresses subjects in abstract, simple and
exaggerated view. While many caricatures are 2D images, this paper presents an
algorithm for creating expressive 3D caricatures from 2D caricature images with
a minimum of user interaction. The key idea of our approach is to introduce an
intrinsi... | computer science |
31,568 | Weakly Supervised Object Localization on grocery shelves using simple
FCN and Synthetic Dataset | cs.CV | We propose a weakly supervised method using two algorithms to predict object
bounding boxes given only an image classification dataset. First algorithm is a
simple Fully Convolutional Network (FCN) trained to classify object instances.
We use the property of FCN to return a mask for images larger than training
images t... | computer science |
31,569 | ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic
Segmentation | cs.CV | We introduce a fast and efficient convolutional neural network, ESPNet, for
semantic segmentation of high resolution images under resource constraints.
ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP),
which is efficient in terms of computation, memory, and power. ESPNet is 22
times faster... | computer science |
31,570 | Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery
using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural
Networks | cs.CV | The knowledge about the placement and appearance of lane markings is a
prerequisite for the creation of maps with high precision, necessary for
autonomous driving, infrastructure monitoring, lane-wise traffic management,
and urban planning. Lane markings are one of the important components of such
maps. Lane markings c... | computer science |
31,571 | Unsupervised Semantic Deep Hashing | cs.CV | In recent years, deep hashing methods have been proved to be efficient since
it employs convolutional neural network to learn features and hashing codes
simultaneously. However, these methods are mostly supervised. In real-world
application, it is a time-consuming and overloaded task for annotating a large
number of im... | computer science |
31,572 | Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis
Tool | cs.CV | In recent years, visual question answering (VQA) has become topical. The
premise of VQA's significance as a benchmark in AI, is that both the image and
textual question need to be well understood and mutually grounded in order to
infer the correct answer. However, current VQA models perhaps `understand' less
than initi... | computer science |
31,573 | Deja Vu: Motion Prediction in Static Images | cs.CV | This paper proposes motion prediction in single still images by learning it
from a set of videos. The building assumption is that similar motion is
characterized by similar appearance. The proposed method learns local motion
patterns given a specific appearance and adds the predicted motion in a number
of applications.... | computer science |
31,574 | Featureless: Bypassing feature extraction in action categorization | cs.CV | This method introduces an efficient manner of learning action categories
without the need of feature estimation. The approach starts from low-level
values, in a similar style to the successful CNN methods. However, rather than
extracting general image features, we learn to predict specific video
representations from ra... | computer science |
31,575 | Live Target Detection with Deep Learning Neural Network and Unmanned
Aerial Vehicle on Android Mobile Device | cs.CV | This paper describes the stages faced during the development of an Android
program which obtains and decodes live images from DJI Phantom 3 Professional
Drone and implements certain features of the TensorFlow Android Camera Demo
application. Test runs were made and outputs of the application were noted. A
lake was clas... | computer science |
31,576 | Factorised spatial representation learning: application in
semi-supervised myocardial segmentation | cs.CV | The success and generalisation of deep learning algorithms heavily depend on
learning good feature representations. In medical imaging this entails
representing anatomical information, as well as properties related to the
specific imaging setting. Anatomical information is required to perform further
analysis, whereas ... | computer science |
31,577 | Learning Region Features for Object Detection | cs.CV | While most steps in the modern object detection methods are learnable, the
region feature extraction step remains largely hand-crafted, featured by RoI
pooling methods. This work proposes a general viewpoint that unifies existing
region feature extraction methods and a novel method that is end-to-end
learnable. The pro... | computer science |
31,578 | VGAN-Based Image Representation Learning for Privacy-Preserving Facial
Expression Recognition | cs.CV | Reliable facial expression recognition plays a critical role in human-machine
interactions. However, most of the facial expression analysis methodologies
proposed to date pay little or no attention to the protection of a user's
privacy. In this paper, we propose a Privacy-Preserving Representation-Learning
Variational ... | computer science |
31,579 | Zero-Shot Detection | cs.CV | As we move towards large-scale object detection, it is unrealistic to expect
annotated training data for all object classes at sufficient scale, and so
methods capable of unseen object detection are required. We propose a novel
zero-shot method based on training an end-to-end model that fuses semantic
attribute predict... | computer science |
31,580 | Local Binary Pattern Networks | cs.CV | Memory and computation efficient deep learning architec- tures are crucial to
continued proliferation of machine learning capabili- ties to new platforms and
systems. Binarization of operations in convo- lutional neural networks has
shown promising results in reducing model size and computing efficiency. In
this paper,... | computer science |
31,581 | Visual Psychophysics for Making Face Recognition Algorithms More
Explainable | cs.CV | Scientific fields that are interested in faces have developed their own sets
of concepts and procedures for understanding how a target model system (be it a
person or algorithm) perceives a face under varying conditions. In computer
vision, this has largely been in the form of dataset evaluation for recognition
tasks w... | computer science |
31,582 | Attention-based Temporal Weighted Convolutional Neural Network for
Action Recognition | cs.CV | Research in human action recognition has accelerated significantly since the
introduction of powerful machine learning tools such as Convolutional Neural
Networks (CNNs). However, effective and efficient methods for incorporation of
temporal information into CNNs are still being actively explored in the recent
literatu... | computer science |
31,583 | DYAN: A Dynamical Atoms Network for Video Prediction | cs.CV | The ability to anticipate the future is essential when making real time
critical decisions, provides valuable information to understand dynamic natural
scenes, and can help unsupervised video representation learning. State-of-art
video prediction is based on LSTM recursive networks and/or generative
adversarial network... | computer science |
31,584 | Real-time Burst Photo Selection Using a Light-Head Adversarial Network | cs.CV | We present an automatic moment capture system that runs in real-time on
mobile cameras. The system is designed to run in the viewfinder mode and
capture a burst sequence of frames before and after the shutter is pressed. For
each frame, the system predicts in real-time a "goodness" score, based on which
the best moment... | computer science |
31,585 | A Temporally-Aware Interpolation Network for Video Frame Inpainting | cs.CV | We propose the first deep learning solution to video frame inpainting, a
challenging instance of the general video inpainting problem with applications
in video editing, manipulation, and forensics. Our task is less ambiguous than
frame interpolation and video prediction because we have access to both the
temporal cont... | computer science |
31,586 | Hierarchical Metric Learning and Matching for 2D and 3D Geometric
Correspondences | cs.CV | Interest point descriptors have fueled progress on almost every problem in
computer vision. Recent advances in deep neural networks have enabled
task-specific learned descriptors that outperform hand-crafted descriptors on
many problems. We demonstrate that commonly used metric learning approaches do
not optimally leve... | computer science |
31,587 | SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep
Neural Networks | cs.CV | This work tackles the automatic fine-grained slide quality assessment problem
for digitized direct smears test using the Gram staining protocol. Automatic
quality assessment can provide useful information for the pathologists and the
whole digital pathology workflow. For instance, if the system found a slide to
have a ... | computer science |
31,588 | 3D Point Cloud Denoising using Graph Laplacian Regularization of a Low
Dimensional Manifold Model | cs.CV | 3D point cloud - a new signal representation of volumetric objects - is a
discrete collection of triples marking exterior object surface locations in 3D
space. Conventional imperfect acquisition processes of 3D point cloud - e.g.,
stereo-matching from multiple viewpoint images or depth data acquired directly
from activ... | computer science |
31,589 | Transferring Rich Deep Features for Facial Beauty Prediction | cs.CV | Feature extraction plays a significant part in computer vision tasks. In this
paper, we propose a method which transfers rich deep features from a pretrained
model on face verification task and feeds the features into Bayesian ridge
regression algorithm for facial beauty prediction. We leverage the deep neural
networks... | computer science |
31,590 | Learning Dynamic Memory Networks for Object Tracking | cs.CV | Template-matching methods for visual tracking have gained popularity recently
due to their comparable performance and fast speed. However, they lack
effective ways to adapt to changes in the target object's appearance, making
their tracking accuracy still far from state-of-the-art. In this paper, we
propose a dynamic m... | computer science |
31,591 | Text Detection and Recognition in images: A survey | cs.CV | Text Detection and recognition is a one of the important aspect of image
processing. This paper analyzes and compares the methods to handle this task.
It summarizes the fundamental problems and enumerates factors that need
consideration when addressing these problems. Existing techniques are
categorized as either stepw... | computer science |
31,592 | Face Recognition Techniques: A Survey | cs.CV | Nowadays research has expanded to extracting auxiliary information from
various biometric techniques like fingerprints, face, iris, palm and voice .
This information contains some major features like gender, age, beard,
mustache, scars, height, hair, skin color, glasses, weight, facial marks and
tattoos. All this infor... | computer science |
31,593 | Flex-Convolution (Deep Learning Beyond Grid-Worlds) | cs.CV | The goal of this work is to enable deep neural networks to learn
representations for irregular 3D structures -- just like in common approaches
for 2D images. Unfortunately, current network primitives such as convolution
layers are specifically designed to exploit the natural data representation of
images -- a fixed and... | computer science |
31,594 | Unsupervised Cross-dataset Person Re-identification by Transfer Learning
of Spatial-Temporal Patterns | cs.CV | Most of the proposed person re-identification algorithms conduct supervised
training and testing on single labeled datasets with small size, so directly
deploying these trained models to a large-scale real-world camera network may
lead to poor performance due to underfitting. It is challenging to
incrementally optimize... | computer science |
31,595 | Segmentation of histological images and fibrosis identification with a
convolutional neural network | cs.CV | Segmentation of histological images is one of the most crucial tasks for many
biomedical analyses including quantification of certain tissue type. However,
challenges are posed by high variability and complexity of structural features
in such images, in addition to imaging artifacts. Further, the conventional
approach ... | computer science |
31,596 | Progressive Structure from Motion | cs.CV | Structure from Motion or the sparse 3D reconstruction out of individual
photos is a long studied topic in computer vision. Yet none of the existing
reconstruction pipelines fully addresses a progressive scenario where images
are only getting available during the reconstruction process and intermediate
results are deliv... | computer science |
31,597 | Discrete Potts Model for Generating Superpixels on Noisy Images | cs.CV | Many computer vision applications, such as object recognition and
segmentation, increasingly build on superpixels. However, there have been so
far few superpixel algorithms that systematically deal with noisy images. We
propose to first decompose the image into equal-sized rectangular patches,
which also sets the maxim... | computer science |
31,598 | Adaptive Co-weighting Deep Convolutional Features For Object Retrieval | cs.CV | Aggregating deep convolutional features into a global image vector has
attracted sustained attention in image retrieval. In this paper, we propose an
efficient unsupervised aggregation method that uses an adaptive Gaussian filter
and an elementvalue sensitive vector to co-weight deep features. Specifically,
the Gaussia... | computer science |
31,599 | Are you eligible? Predicting adulthood from face images via class
specific mean autoencoder | cs.CV | Predicting if a person is an adult or a minor has several applications such
as inspecting underage driving, preventing purchase of alcohol and tobacco by
minors, and granting restricted access. The challenging nature of this problem
arises due to the complex and unique physiological changes that are observed
with age p... | computer science |
31,600 | Residual Codean Autoencoder for Facial Attribute Analysis | cs.CV | Facial attributes can provide rich ancillary information which can be
utilized for different applications such as targeted marketing, human computer
interaction, and law enforcement. This research focuses on facial attribute
prediction using a novel deep learning formulation, termed as R-Codean
autoencoder. The paper f... | computer science |
31,601 | Patch-Based Image Inpainting with Generative Adversarial Networks | cs.CV | Area of image inpainting over relatively large missing regions recently
advanced substantially through adaptation of dedicated deep neural networks.
However, current network solutions still introduce undesired artifacts and
noise to the repaired regions. We present an image inpainting method that is
based on the celebr... | computer science |
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