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28,902 | Deep Generative Adversarial Neural Networks for Realistic Prostate
Lesion MRI Synthesis | cs.CV | Generative Adversarial Neural Networks (GANs) are applied to the synthetic
generation of prostate lesion MRI images. GANs have been applied to a variety
of natural images, is shown show that the same techniques can be used in the
medical domain to create realistic looking synthetic lesion images. 16mm x 16mm
patches ar... | computer science |
28,903 | Parallel Tracking and Verifying: A Framework for Real-Time and High
Accuracy Visual Tracking | cs.CV | Being intensively studied, visual tracking has seen great recent advances in
either speed (e.g., with correlation filters) or accuracy (e.g., with deep
features). Real-time and high accuracy tracking algorithms, however, remain
scarce. In this paper we study the problem from a new perspective and present a
novel parall... | computer science |
28,904 | Image Denoising via CNNs: An Adversarial Approach | cs.CV | Is it possible to recover an image from its noisy version using convolutional
neural networks? This is an interesting problem as convolutional layers are
generally used as feature detectors for tasks like classification, segmentation
and object detection. We present a new CNN architecture for blind image
denoising whic... | computer science |
28,905 | Towards Vision-Based Smart Hospitals: A System for Tracking and
Monitoring Hand Hygiene Compliance | cs.CV | One in twenty-five patients admitted to a hospital will suffer from a
hospital acquired infection. If we can intelligently track healthcare staff,
patients, and visitors, we can better understand the sources of such
infections. We envision a smart hospital capable of increasing operational
efficiency and improving pati... | computer science |
28,906 | A Locally Weighted Fixation Density-Based Metric for Assessing the
Quality of Visual Saliency Predictions | cs.CV | With the increased focus on visual attention (VA) in the last decade, a large
number of computational visual saliency methods have been developed over the
past few years. These models are traditionally evaluated by using performance
evaluation metrics that quantify the match between predicted saliency and
fixation data... | computer science |
28,907 | Model-based learning of local image features for unsupervised texture
segmentation | cs.CV | Features that capture well the textural patterns of a certain class of images
are crucial for the performance of texture segmentation methods. The manual
selection of features or designing new ones can be a tedious task. Therefore,
it is desirable to automatically adapt the features to a certain image or class
of image... | computer science |
28,908 | Real-time Deep Video Deinterlacing | cs.CV | Interlacing is a widely used technique, for television broadcast and video
recording, to double the perceived frame rate without increasing the bandwidth.
But it presents annoying visual artifacts, such as flickering and silhouette
"serration," during the playback. Existing state-of-the-art deinterlacing
methods either... | computer science |
28,909 | Switching Convolutional Neural Network for Crowd Counting | cs.CV | We propose a novel crowd counting model that maps a given crowd scene to its
density. Crowd analysis is compounded by myriad of factors like inter-occlusion
between people due to extreme crowding, high similarity of appearance between
people and background elements, and large variability of camera view-points.
Current ... | computer science |
28,910 | HMM-based Indic Handwritten Word Recognition using Zone Segmentation | cs.CV | This paper presents a novel approach towards Indic handwritten word
recognition using zone-wise information. Because of complex nature due to
compound characters, modifiers, overlapping and touching, etc., character
segmentation and recognition is a tedious job in Indic scripts (e.g.
Devanagari, Bangla, Gurumukhi, and ... | computer science |
28,911 | CNN Cascades for Segmenting Whole Slide Images of the Kidney | cs.CV | Due to the increasing availability of whole slide scanners facilitating
digitization of histopathological tissue, there is a strong demand for the
development of computer based image analysis systems. In this work, the focus
is on the segmentation of the glomeruli constituting a highly relevant
structure in renal histo... | computer science |
28,912 | Learning Deep Convolutional Embeddings for Face Representation Using
Joint Sample- and Set-based Supervision | cs.CV | In this work, we investigate several methods and strategies to learn deep
embeddings for face recognition, using joint sample- and set-based
optimization. We explain our framework that expands traditional learning with
set-based supervision together with the strategies used to maintain set
characteristics. We, then, br... | computer science |
28,913 | Dual Motion GAN for Future-Flow Embedded Video Prediction | cs.CV | Future frame prediction in videos is a promising avenue for unsupervised
video representation learning. Video frames are naturally generated by the
inherent pixel flows from preceding frames based on the appearance and motion
dynamics in the video. However, existing methods focus on directly
hallucinating pixel values,... | computer science |
28,914 | Best Viewpoint Tracking for Camera Mounted on Robotic Arm with Dynamic
Obstacles | cs.CV | The problem of finding a next best viewpoint for 3D modeling or scene mapping
has been explored in computer vision over the last decade. This paper tackles a
similar problem, but with different characteristics. It proposes a method for
dynamic next best viewpoint recovery of a target point while avoiding possible
occlu... | computer science |
28,915 | Generative Semantic Manipulation with Contrasting GAN | cs.CV | Generative Adversarial Networks (GANs) have recently achieved significant
improvement on paired/unpaired image-to-image translation, such as
photo$\rightarrow$ sketch and artist painting style transfer. However, existing
models can only be capable of transferring the low-level information (e.g.
color or texture changes... | computer science |
28,916 | Self-Supervised Learning for Spinal MRIs | cs.CV | A significant proportion of patients scanned in a clinical setting have
follow-up scans. We show in this work that such longitudinal scans alone can be
used as a form of 'free' self-supervision for training a deep network. We
demonstrate this self-supervised learning for the case of T2-weighted sagittal
lumbar Magnetic... | computer science |
28,917 | Hand2Face: Automatic Synthesis and Recognition of Hand Over Face
Occlusions | cs.CV | A person's face discloses important information about their affective state.
Although there has been extensive research on recognition of facial
expressions, the performance of existing approaches is challenged by facial
occlusions. Facial occlusions are often treated as noise and discarded in
recognition of affective ... | computer science |
28,918 | Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural
Network | cs.CV | Detection of brain tumor using a segmentation based approach is critical in
cases, where survival of a subject depends on an accurate and timely clinical
diagnosis. Gliomas are the most commonly found tumors having irregular shape
and ambiguous boundaries, making them one of the hardest tumors to detect. The
automation... | computer science |
28,919 | Momo: Monocular Motion Estimation on Manifolds | cs.CV | Knowledge about the location of a vehicle is indispensable for autonomous
driving. In order to apply global localisation methods, a pose prior must be
known which can be obtained from visual odometry. The quality and robustness of
that prior determine the success of localisation. Momo is a monocular
frame-to-frame moti... | computer science |
28,920 | Depth Super-Resolution Meets Uncalibrated Photometric Stereo | cs.CV | A novel depth super-resolution approach for RGB-D sensors is presented. It
disambiguates depth super-resolution through high-resolution photometric clues
and, symmetrically, it disambiguates uncalibrated photometric stereo through
low-resolution depth cues. To this end, an RGB-D sequence is acquired from the
same viewi... | computer science |
28,921 | Dense Piecewise Planar RGB-D SLAM for Indoor Environments | cs.CV | The paper exploits weak Manhattan constraints to parse the structure of
indoor environments from RGB-D video sequences in an online setting. We extend
the previous approach for single view parsing of indoor scenes to video
sequences and formulate the problem of recovering the floor plan of the
environment as an optimal... | computer science |
28,922 | Automatic 3D Cardiovascular MR Segmentation with Densely-Connected
Volumetric ConvNets | cs.CV | Automatic and accurate whole-heart and great vessel segmentation from 3D
cardiac magnetic resonance (MR) images plays an important role in the
computer-assisted diagnosis and treatment of cardiovascular disease. However,
this task is very challenging due to ambiguous cardiac borders and large
anatomical variations amon... | computer science |
28,923 | Kernalised Multi-resolution Convnet for Visual Tracking | cs.CV | Visual tracking is intrinsically a temporal problem. Discriminative
Correlation Filters (DCF) have demonstrated excellent performance for
high-speed generic visual object tracking. Built upon their seminal work, there
has been a plethora of recent improvements relying on convolutional neural
network (CNN) pretrained on... | computer science |
28,924 | Joint Transmission Map Estimation and Dehazing using Deep Networks | cs.CV | Single image haze removal is an extremely challenging problem due to its
inherent ill-posed nature. Several prior-based and learning-based methods have
been proposed in the literature to solve this problem and they have achieved
superior results. However, most of the existing methods assume constant
atmospheric light m... | computer science |
28,925 | A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo
Matching | cs.CV | Depth from defocus (DfD) and stereo matching are two most studied passive
depth sensing schemes. The techniques are essentially complementary: DfD can
robustly handle repetitive textures that are problematic for stereo matching
whereas stereo matching is insensitive to defocus blurs and can handle large
depth range. In... | computer science |
28,926 | A Simple Loss Function for Improving the Convergence and Accuracy of
Visual Question Answering Models | cs.CV | Visual question answering as recently proposed multimodal learning task has
enjoyed wide attention from the deep learning community. Lately, the focus was
on developing new representation fusion methods and attention mechanisms to
achieve superior performance. On the other hand, very little focus has been put
on the mo... | computer science |
28,927 | Dual-Glance Model for Deciphering Social Relationships | cs.CV | Since the beginning of early civilizations, social relationships derived from
each individual fundamentally form the basis of social structure in our daily
life. In the computer vision literature, much progress has been made in scene
understanding, such as object detection and scene parsing. Recent research
focuses on ... | computer science |
28,928 | Temporal Dynamic Graph LSTM for Action-driven Video Object Detection | cs.CV | In this paper, we investigate a weakly-supervised object detection framework.
Most existing frameworks focus on using static images to learn object
detectors. However, these detectors often fail to generalize to videos because
of the existing domain shift. Therefore, we investigate learning these
detectors directly fro... | computer science |
28,929 | Action recognition by learning pose representations | cs.CV | Pose detection is one of the fundamental steps for the recognition of human
actions. In this paper we propose a novel trainable detector for recognizing
human poses based on the analysis of the skeleton. The main idea is that a
skeleton pose can be described by the spatial arrangements of its joints.
Starting from this... | computer science |
28,930 | Accurate Lung Segmentation via Network-Wise Training of Convolutional
Networks | cs.CV | We introduce an accurate lung segmentation model for chest radiographs based
on deep convolutional neural networks. Our model is based on atrous
convolutional layers to increase the field-of-view of filters efficiently. To
improve segmentation performances further, we also propose a multi-stage
training strategy, netwo... | computer science |
28,931 | InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop
Closure | cs.CV | Volumetric models have become a popular representation for 3D scenes in
recent years. One breakthrough leading to their popularity was KinectFusion,
which focuses on 3D reconstruction using RGB-D sensors. However, monocular SLAM
has since also been tackled with very similar approaches. Representing the
reconstruction v... | computer science |
28,932 | Structure-measure: A New Way to Evaluate Foreground Maps | cs.CV | Foreground map evaluation is crucial for gauging the progress of object
segmentation algorithms, in particular in the filed of salient object detection
where the purpose is to accurately detect and segment the most salient object
in a scene. Several widely-used measures such as Area Under the Curve (AUC),
Average Preci... | computer science |
28,933 | Predictive Coding for Dynamic Visual Processing: Development of
Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model | cs.CV | The current paper proposes a novel predictive coding type neural network
model, the predictive multiple spatio-temporal scales recurrent neural network
(P-MSTRNN). The P-MSTRNN learns to predict visually perceived human whole-body
cyclic movement patterns by exploiting multiscale spatio-temporal constraints
imposed on ... | computer science |
28,934 | Land Cover Classification from Multi-temporal, Multi-spectral Remotely
Sensed Imagery using Patch-Based Recurrent Neural Networks | cs.CV | Sustainability of the global environment is dependent on the accurate land
cover information over large areas. Even with the increased number of satellite
systems and sensors acquiring data with improved spectral, spatial, radiometric
and temporal characteristics and the new data distribution policy, most
existing land... | computer science |
28,935 | An End-to-End Compression Framework Based on Convolutional Neural
Networks | cs.CV | Deep learning, e.g., convolutional neural networks (CNNs), has achieved great
success in image processing and computer vision especially in high level vision
applications such as recognition and understanding. However, it is rarely used
to solve low-level vision problems such as image compression studied in this
paper.... | computer science |
28,936 | Fingerprint Extraction Using Smartphone Camera | cs.CV | In the previous decade, there has been a considerable rise in the usage of
smartphones.Due to exorbitant advancement in technology, computational speed
and quality of image capturing has increased considerably. With an increase in
the need for remote fingerprint verification, smartphones can be used as a
powerful alter... | computer science |
28,937 | PIVO: Probabilistic Inertial-Visual Odometry for Occlusion-Robust
Navigation | cs.CV | This paper presents a novel method for visual-inertial odometry. The method
is based on an information fusion framework employing low-cost IMU sensors and
the monocular camera in a standard smartphone. We formulate a sequential
inference scheme, where the IMU drives the dynamical model and the camera
frames are used in... | computer science |
28,938 | Learning Spherical Convolution for Fast Features from 360° Imagery | cs.CV | While 360{\deg} cameras offer tremendous new possibilities in vision,
graphics, and augmented reality, the spherical images they produce make core
feature extraction non-trivial. Convolutional neural networks (CNNs) trained on
images from perspective cameras yield "flat" filters, yet 360{\deg} images
cannot be projecte... | computer science |
28,939 | Associative Domain Adaptation | cs.CV | We propose associative domain adaptation, a novel technique for end-to-end
domain adaptation with neural networks, the task of inferring class labels for
an unlabeled target domain based on the statistical properties of a labeled
source domain. Our training scheme follows the paradigm that in order to
effectively deriv... | computer science |
28,940 | Predicting Human Activities Using Stochastic Grammar | cs.CV | This paper presents a novel method to predict future human activities from
partially observed RGB-D videos. Human activity prediction is generally
difficult due to its non-Markovian property and the rich context between human
and environments.
We use a stochastic grammar model to capture the compositional structure o... | computer science |
28,941 | Semantic Instance Labeling Leveraging Hierarchical Segmentation | cs.CV | Most of the approaches for indoor RGBD semantic la- beling focus on using
pixels or superpixels to train a classi- fier. In this paper, we implement a
higher level segmentation using a hierarchy of superpixels to obtain a better
segmen- tation for training our classifier. By focusing on meaningful segments
that conform... | computer science |
28,942 | Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs | cs.CV | We present a novel method called Contextual Pyramid CNN (CP-CNN) for
generating high-quality crowd density and count estimation by explicitly
incorporating global and local contextual information of crowd images. The
proposed CP-CNN consists of four modules: Global Context Estimator (GCE), Local
Context Estimator (LCE)... | computer science |
28,943 | Low Dose CT Image Denoising Using a Generative Adversarial Network with
Wasserstein Distance and Perceptual Loss | cs.CV | In this paper, we introduce a new CT image denoising method based on the
generative adversarial network (GAN) with Wasserstein distance and perceptual
similarity. The Wasserstein distance is a key concept of the optimal transform
theory, and promises to improve the performance of the GAN. The perceptual loss
compares t... | computer science |
28,944 | ORGB: Offset Correction in RGB Color Space for Illumination-Robust Image
Processing | cs.CV | Single materials have colors which form straight lines in RGB space. However,
in severe shadow cases, those lines do not intersect the origin, which is
inconsistent with the description of most literature. This paper is concerned
with the detection and correction of the offset between the intersection and
origin. First... | computer science |
28,945 | 3DFaceNet: Real-time Dense Face Reconstruction via Synthesizing
Photo-realistic Face Images | cs.CV | With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coar... | computer science |
28,946 | Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding
Learning | cs.CV | This paper presents an approach for recognizing human activities from extreme
low resolution (e.g., 16x12) videos. Extreme low resolution recognition is not
only necessary for analyzing actions at a distance but also is crucial for
enabling privacy-preserving recognition of human activities. We design a new
two-stream ... | computer science |
28,947 | Learning Accurate Low-Bit Deep Neural Networks with Stochastic
Quantization | cs.CV | Low-bit deep neural networks (DNNs) become critical for embedded applications
due to their low storage requirement and computing efficiency. However, they
suffer much from the non-negligible accuracy drop. This paper proposes the
stochastic quantization (SQ) algorithm for learning accurate low-bit DNNs. The
motivation ... | computer science |
28,948 | Beyond Low Rank: A Data-Adaptive Tensor Completion Method | cs.CV | Low rank tensor representation underpins much of recent progress in tensor
completion. In real applications, however, this approach is confronted with two
challenging problems, namely (1) tensor rank determination; (2) handling real
tensor data which only approximately fulfils the low-rank requirement. To
address these... | computer science |
28,949 | When Kernel Methods meet Feature Learning: Log-Covariance Network for
Action Recognition from Skeletal Data | cs.CV | Human action recognition from skeletal data is a hot research topic and
important in many open domain applications of computer vision, thanks to
recently introduced 3D sensors. In the literature, naive methods simply
transfer off-the-shelf techniques from video to the skeletal representation.
However, the current state... | computer science |
28,950 | What Will I Do Next? The Intention from Motion Experiment | cs.CV | In computer vision, video-based approaches have been widely explored for the
early classification and the prediction of actions or activities. However, it
remains unclear whether this modality (as compared to 3D kinematics) can still
be reliable for the prediction of human intentions, defined as the overarching
goal em... | computer science |
28,951 | Learning Feature Pyramids for Human Pose Estimation | cs.CV | Articulated human pose estimation is a fundamental yet challenging task in
computer vision. The difficulty is particularly pronounced in scale variations
of human body parts when camera view changes or severe foreshortening happens.
Although pyramid methods are widely used to handle scale changes at inference
time, lea... | computer science |
28,952 | A Unified View-Graph Selection Framework for Structure from Motion | cs.CV | View-graph is an essential input to large-scale structure from motion (SfM)
pipelines. Accuracy and efficiency of large-scale SfM is crucially dependent on
the input view-graph. Inconsistent or inaccurate edges can lead to inferior or
wrong reconstruction. Most SfM methods remove `undesirable' images and pairs
using se... | computer science |
28,953 | Automatic Segmentation and Disease Classification Using Cardiac Cine MR
Images | cs.CV | Segmentation of the heart in cardiac cine MR is clinically used to quantify
cardiac function. We propose a fully automatic method for segmentation and
disease classification using cardiac cine MR images. A convolutional neural
network (CNN) was designed to simultaneously segment the left ventricle (LV),
right ventricle... | computer science |
28,954 | Three-dimensional planar model estimation using multi-constraint
knowledge based on k-means and RANSAC | cs.CV | Plane model extraction from three-dimensional point clouds is a necessary
step in many different applications such as planar object reconstruction,
indoor mapping and indoor localization. Different RANdom SAmple Consensus
(RANSAC)-based methods have been proposed for this purpose in recent years. In
this study, we prop... | computer science |
28,955 | Deep MR to CT Synthesis using Unpaired Data | cs.CV | MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis.
Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned
MR and CT training images of the same patient. However, misalignment between
paired images could lead to errors in synthesized CT images. To overcome this,
we pro... | computer science |
28,956 | Patch-based adaptive weighting with segmentation and scale (PAWSS) for
visual tracking | cs.CV | Tracking-by-detection algorithms are widely used for visual tracking, where
the problem is treated as a classification task where an object model is
updated over time using online learning techniques. In challenging conditions
where an object undergoes deformation or scale variations, the update step is
prone to includ... | computer science |
28,957 | Unsupervised Video Understanding by Reconciliation of Posture
Similarities | cs.CV | Understanding human activity and being able to explain it in detail surpasses
mere action classification by far in both complexity and value. The challenge
is thus to describe an activity on the basis of its most fundamental
constituents, the individual postures and their distinctive transitions.
Supervised learning of... | computer science |
28,958 | Recent Developments and Future Challenges in Medical Mixed Reality | cs.CV | Mixed Reality (MR) is of increasing interest within technology-driven modern
medicine but is not yet used in everyday practice. This situation is changing
rapidly, however, and this paper explores the emergence of MR technology and
the importance of its utility within medical applications. A classification of
medical M... | computer science |
28,959 | Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery | cs.CV | The potential of Augmented Reality (AR) technology to assist minimally
invasive surgeries (MIS) lies in its computational performance and accuracy in
dealing with challenging MIS scenes. Even with the latest hardware and software
technologies, achieving both real-time and accurate augmented information
overlay in MIS i... | computer science |
28,960 | Unsupervised Representation Learning by Sorting Sequences | cs.CV | We present an unsupervised representation learning approach using videos
without semantic labels. We leverage the temporal coherence as a supervisory
signal by formulating representation learning as a sequence sorting task. We
take temporally shuffled frames (i.e., in non-chronological order) as inputs
and train a conv... | computer science |
28,961 | Automatic Spatially-aware Fashion Concept Discovery | cs.CV | This paper proposes an automatic spatially-aware concept discovery approach
using weakly labeled image-text data from shopping websites. We first fine-tune
GoogleNet by jointly modeling clothing images and their corresponding
descriptions in a visual-semantic embedding space. Then, for each attribute
(word), we generat... | computer science |
28,962 | μ-MAR: Multiplane 3D Marker based Registration for Depth-sensing
Cameras | cs.CV | Many applications including object reconstruction, robot guidance, and scene
mapping require the registration of multiple views from a scene to generate a
complete geometric and appearance model of it. In real situations,
transformations between views are unknown an it is necessary to apply expert
inference to estimate... | computer science |
28,963 | On the Selective and Invariant Representation of DCNN for
High-Resolution Remote Sensing Image Recognition | cs.CV | Human vision possesses strong invariance in image recognition. The cognitive
capability of deep convolutional neural network (DCNN) is close to the human
visual level because of hierarchical coding directly from raw image. Owing to
its superiority in feature representation, DCNN has exhibited remarkable
performance in ... | computer science |
28,964 | Video Salient Object Detection Using Spatiotemporal Deep Features | cs.CV | This paper presents a method for detecting salient objects in videos where
temporal information in addition to spatial information is fully taken into
account. Following recent reports on the advantage of deep features over
conventional hand-crafted features, we propose the SpatioTemporal Deep (STD)
feature that utiliz... | computer science |
28,965 | Correlation and Class Based Block Formation for Improved Structured
Dictionary Learning | cs.CV | In recent years, the creation of block-structured dictionary has attracted a
lot of interest. Learning such dictionaries involve two step process: block
formation and dictionary update. Both these steps are important in producing an
effective dictionary. The existing works mostly assume that the block structure
is know... | computer science |
28,966 | Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for
Visual Question Answering | cs.CV | Visual question answering (VQA) is challenging because it requires a
simultaneous understanding of both the visual content of images and the textual
content of questions. The approaches used to represent the images and questions
in a fine-grained manner and questions and to fuse these multi-modal features
play key role... | computer science |
28,967 | Hierarchical Metric Learning for Fine Grained Image Classification | cs.CV | This paper deals with the problem of fine-grained image classification and
introduces the notion of hierarchical metric learning for the same. It is
indeed challenging to categorize fine-grained image classes merely in terms of
a single level classifier given the subtle inter-class visual differences. In
order to tackl... | computer science |
28,968 | Associations among Image Assessments as Cost Functions in Linear
Decomposition: MSE, SSIM, and Correlation Coefficient | cs.CV | The traditional methods of image assessment, such as mean squared error
(MSE), signal-to-noise ratio (SNR), and Peak signal-to-noise ratio (PSNR), are
all based on the absolute error of images. Pearson's inner-product correlation
coefficient (PCC) is also usually used to measure the similarity between
images. Structura... | computer science |
28,969 | Augmented Reality Meets Computer Vision : Efficient Data Generation for
Urban Driving Scenes | cs.CV | The success of deep learning in computer vision is based on availability of
large annotated datasets. To lower the need for hand labeled images, virtually
rendered 3D worlds have recently gained popularity. Creating realistic 3D
content is challenging on its own and requires significant human effort. In
this work, we p... | computer science |
28,970 | Sensing Urban Land-Use Patterns By Integrating Google Tensorflow And
Scene-Classification Models | cs.CV | With the rapid progress of China's urbanization, research on the automatic
detection of land-use patterns in Chinese cities is of substantial importance.
Deep learning is an effective method to extract image features. To take
advantage of the deep-learning method in detecting urban land-use patterns, we
applied a trans... | computer science |
28,971 | Region-Based Multiscale Spatiotemporal Saliency for Video | cs.CV | Detecting salient objects from a video requires exploiting both spatial and
temporal knowledge included in the video. We propose a novel region-based
multiscale spatiotemporal saliency detection method for videos, where static
features and dynamic features computed from the low and middle levels are
combined together. ... | computer science |
28,972 | Localizing Moments in Video with Natural Language | cs.CV | We consider retrieving a specific temporal segment, or moment, from a video
given a natural language text description. Methods designed to retrieve whole
video clips with natural language determine what occurs in a video but not
when. To address this issue, we propose the Moment Context Network (MCN) which
effectively ... | computer science |
28,973 | Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection | cs.CV | A major impediment in rapidly deploying object detection models for instance
detection is the lack of large annotated datasets. For example, finding a large
labeled dataset containing instances in a particular kitchen is unlikely. Each
new environment with new instances requires expensive data collection and
annotation... | computer science |
28,974 | Better Together: Joint Reasoning for Non-rigid 3D Reconstruction with
Specularities and Shading | cs.CV | We demonstrate the use of shape-from-shading (SfS) to improve both the
quality and the robustness of 3D reconstruction of dynamic objects captured by
a single camera. Unlike previous approaches that made use of SfS as a
post-processing step, we offer a principled integrated approach that solves
dynamic object tracking ... | computer science |
28,975 | Accelerated Image Reconstruction for Nonlinear Diffractive Imaging | cs.CV | The problem of reconstructing an object from the measurements of the light it
scatters is common in numerous imaging applications. While the most popular
formulations of the problem are based on linearizing the object-light
relationship, there is an increased interest in considering nonlinear
formulations that can acco... | computer science |
28,976 | Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and
Geometry Optimization with Spatially-Varying Lighting | cs.CV | We introduce a novel method to obtain high-quality 3D reconstructions from
consumer RGB-D sensors. Our core idea is to simultaneously optimize for
geometry encoded in a signed distance field (SDF), textures from
automatically-selected keyframes, and their camera poses along with material
and scene lighting. To this end... | computer science |
28,977 | Query-guided Regression Network with Context Policy for Phrase Grounding | cs.CV | Given a textual description of an image, phrase grounding localizes objects
in the image referred by query phrases in the description. State-of-the-art
methods address the problem by ranking a set of proposals based on the
relevance to each query, which are limited by the performance of independent
proposal generation ... | computer science |
28,978 | Deep Metric Learning with Angular Loss | cs.CV | The modern image search system requires semantic understanding of image, and
a key yet under-addressed problem is to learn a good metric for measuring the
similarity between images. While deep metric learning has yielded impressive
performance gains by extracting high level abstractions from image data, a
proper object... | computer science |
28,979 | Video Frame Interpolation via Adaptive Separable Convolution | cs.CV | Standard video frame interpolation methods first estimate optical flow
between input frames and then synthesize an intermediate frame guided by
motion. Recent approaches merge these two steps into a single convolution
process by convolving input frames with spatially adaptive kernels that account
for motion and re-samp... | computer science |
28,980 | Adversarial Robustness: Softmax versus Openmax | cs.CV | Deep neural networks (DNNs) provide state-of-the-art results on various tasks
and are widely used in real world applications. However, it was discovered that
machine learning models, including the best performing DNNs, suffer from a
fundamental problem: they can unexpectedly and confidently misclassify examples
formed ... | computer science |
28,981 | Optimizing Region Selection for Weakly Supervised Object Detection | cs.CV | Training object detectors with only image-level annotations is very
challenging because the target objects are often surrounded by a large number
of background clutters. Many existing approaches tackle this problem through
object proposal mining. However, the collected positive regions are either low
in precision or la... | computer science |
28,982 | Learning Discriminative Alpha-Beta-divergence for Positive Definite
Matrices (Extended Version) | cs.CV | Symmetric positive definite (SPD) matrices are useful for capturing
second-order statistics of visual data. To compare two SPD matrices, several
measures are available, such as the affine-invariant Riemannian metric,
Jeffreys divergence, Jensen-Bregman logdet divergence, etc.; however, their
behaviors may be applicatio... | computer science |
28,983 | SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis | cs.CV | This paper proposes an end-to-end learning framework for multiview
stereopsis. We term the network SurfaceNet. It takes a set of images and their
corresponding camera parameters as input and directly infers the 3D model. The
key advantage of the framework is that both photo-consistency as well geometric
relations of th... | computer science |
28,984 | Interactively Transferring CNN Patterns for Part Localization | cs.CV | In the scenario of one/multi-shot learning, conventional end-to-end learning
strategies without sufficient supervision are usually not powerful enough to
learn correct patterns from noisy signals. Thus, given a CNN pre-trained for
object classification, this paper proposes a method that first summarizes the
knowledge h... | computer science |
28,985 | Interpreting CNN Knowledge via an Explanatory Graph | cs.CV | This paper learns a graphical model, namely an explanatory graph, which
reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering
that each filter in a conv-layer of a pre-trained CNN usually represents a
mixture of object parts, we propose a simple yet efficient method to
automatically disentangles ... | computer science |
28,986 | Detecting Noteheads in Handwritten Scores with ConvNets and Bounding Box
Regression | cs.CV | Noteheads are the interface between the written score and music. Each
notehead on the page signifies one note to be played, and detecting noteheads
is thus an unavoidable step for Optical Music Recognition. Noteheads are
clearly distinct objects, however, the variety of music notation handwriting
makes noteheads harder... | computer science |
28,987 | Depth Adaptive Deep Neural Network for Semantic Segmentation | cs.CV | In this work, we present the depth-adaptive deep neural network using a depth
map for semantic segmentation. Typical deep neural networks receive inputs at
the predetermined locations regardless of the distance from the camera. This
fixed receptive field presents a challenge to generalize the features of
objects at var... | computer science |
28,988 | Automated Assessment of Facial Wrinkling: a case study on the effect of
smoking | cs.CV | Facial wrinkle is one of the most prominent biological changes that
accompanying the natural aging process. However, there are some external
factors contributing to premature wrinkles development, such as sun exposure
and smoking. Clinical studies have shown that heavy smoking causes premature
wrinkles development. How... | computer science |
28,989 | Manifold Constrained Low-Rank Decomposition | cs.CV | Low-rank decomposition (LRD) is a state-of-the-art method for visual data
reconstruction and modelling. However, it is a very challenging problem when
the image data contains significant occlusion, noise, illumination variation,
and misalignment from rotation or viewpoint changes. We leverage the specific
structure of ... | computer science |
28,990 | Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for
Pose Regularization | cs.CV | One-shot pose estimation for tasks such as body joint localization, camera
pose estimation, and object tracking are generally noisy, and temporal filters
have been extensively used for regularization. One of the most widely-used
methods is the Kalman filter, which is both extremely simple and general.
However, Kalman f... | computer science |
28,991 | End-to-end learning potentials for structured attribute prediction | cs.CV | We present a structured inference approach in deep neural networks for
multiple attribute prediction. In attribute prediction, a common approach is to
learn independent classifiers on top of a good feature representation. However,
such classifiers assume conditional independence on features and do not
explicitly consid... | computer science |
28,992 | EndNet: Sparse AutoEncoder Network for Endmember Extraction and
Hyperspectral Unmixing | cs.CV | Data acquired from multi-channel sensors is a highly valuable asset to
interpret the environment for a variety of remote sensing applications.
However, low spatial resolution is a critical limitation for the sensors and
the constituent materials of a scene can be mixed in different fractions due to
their spatial intera... | computer science |
28,993 | Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation | cs.CV | Diabetic Foot Ulcer (DFU) is a major complication of Diabetes, which if not
managed properly can lead to amputation. DFU can appear anywhere on the foot
and can vary in size, colour, and contrast depending on various pathologies.
Current clinical approaches to DFU treatment rely on patients and clinician
vigilance, whi... | computer science |
28,994 | Face Parsing via Recurrent Propagation | cs.CV | Face parsing is an important problem in computer vision that finds numerous
applications including recognition and editing. Recently, deep convolutional
neural networks (CNNs) have been applied to image parsing and segmentation with
the state-of-the-art performance. In this paper, we propose a face parsing
algorithm th... | computer science |
28,995 | Intensity Video Guided 4D Fusion for Improved Highly Dynamic 3D
Reconstruction | cs.CV | The availability of high-speed 3D video sensors has greatly facilitated 3D
shape acquisition of dynamic and deformable objects, but high frame rate 3D
reconstruction is always degraded by spatial noise and temporal fluctuations.
This paper presents a simple yet powerful intensity video guided multi-frame 4D
fusion pipe... | computer science |
28,996 | PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel
Pairwise R-FCN | cs.CV | We aim to tackle a novel vision task called Weakly Supervised Visual Relation
Detection (WSVRD) to detect "subject-predicate-object" relations in an image
with object relation groundtruths available only at the image level. This is
motivated by the fact that it is extremely expensive to label the combinatorial
relation... | computer science |
28,997 | Accurate Light Field Depth Estimation with Superpixel Regularization
over Partially Occluded Regions | cs.CV | Depth estimation is a fundamental problem for light field photography
applications. Numerous methods have been proposed in recent years, which either
focus on crafting cost terms for more robust matching, or on analyzing the
geometry of scene structures embedded in the epipolar-plane images. Significant
improvements ha... | computer science |
28,998 | Identity-Aware Textual-Visual Matching with Latent Co-attention | cs.CV | Textual-visual matching aims at measuring similarities between sentence
descriptions and images. Most existing methods tackle this problem without
effectively utilizing identity-level annotations. In this paper, we propose an
identity-aware two-stage framework for the textual-visual matching problem. Our
stage-1 CNN-LS... | computer science |
28,999 | Amulet: Aggregating Multi-level Convolutional Features for Salient
Object Detection | cs.CV | Fully convolutional neural networks (FCNs) have shown outstanding performance
in many dense labeling problems. One key pillar of these successes is mining
relevant information from features in convolutional layers. However, how to
better aggregate multi-level convolutional feature maps for salient object
detection is u... | computer science |
29,000 | Focal Loss for Dense Object Detection | cs.CV | The highest accuracy object detectors to date are based on a two-stage
approach popularized by R-CNN, where a classifier is applied to a sparse set of
candidate object locations. In contrast, one-stage detectors that are applied
over a regular, dense sampling of possible object locations have the potential
to be faster... | computer science |
29,001 | Learning Uncertain Convolutional Features for Accurate Saliency
Detection | cs.CV | Deep convolutional neural networks (CNNs) have delivered superior performance
in many computer vision tasks. In this paper, we propose a novel deep fully
convolutional network model for accurate salient object detection. The key
contribution of this work is to learn deep uncertain convolutional features
(UCF), which en... | computer science |
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