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27,702 | Using convolutional networks and satellite imagery to identify patterns
in urban environments at a large scale | cs.CV | Urban planning applications (energy audits, investment, etc.) require an
understanding of built infrastructure and its environment, i.e., both
low-level, physical features (amount of vegetation, building area and geometry
etc.), as well as higher-level concepts such as land use classes (which encode
expert understandin... | computer science |
27,703 | Weakly-Supervised Spatial Context Networks | cs.CV | We explore the power of spatial context as a self-supervisory signal for
learning visual representations. In particular, we propose spatial context
networks that learn to predict a representation of one image patch from another
image patch, within the same image, conditioned on their real-valued relative
spatial offset... | computer science |
27,704 | DRAW: Deep networks for Recognizing styles of Artists Who illustrate
children's books | cs.CV | This paper is motivated from a young boy's capability to recognize an
illustrator's style in a totally different context. In the book "We are All
Born Free" [1], composed of selected rights from the Universal Declaration of
Human Rights interpreted by different illustrators, the boy was surprised to
see a picture simil... | computer science |
27,705 | Action Unit Detection with Region Adaptation, Multi-labeling Learning
and Optimal Temporal Fusing | cs.CV | Action Unit (AU) detection becomes essential for facial analysis. Many
proposed approaches face challenging problems in dealing with the alignments of
different face regions, in the effective fusion of temporal information, and in
training a model for multiple AU labels. To better address these problems, we
propose a d... | computer science |
27,706 | Detecting Visual Relationships with Deep Relational Networks | cs.CV | Relationships among objects play a crucial role in image understanding.
Despite the great success of deep learning techniques in recognizing individual
objects, reasoning about the relationships among objects remains a challenging
task. Previous methods often treat this as a classification problem,
considering each typ... | computer science |
27,707 | DOPE: Distributed Optimization for Pairwise Energies | cs.CV | We formulate an Alternating Direction Method of Mul-tipliers (ADMM) that
systematically distributes the computations of any technique for optimizing
pairwise functions, including non-submodular potentials. Such discrete
functions are very useful in segmentation and a breadth of other vision
problems. Our method decompo... | computer science |
27,708 | Improving Pairwise Ranking for Multi-label Image Classification | cs.CV | Learning to rank has recently emerged as an attractive technique to train
deep convolutional neural networks for various computer vision tasks. Pairwise
ranking, in particular, has been successful in multi-label image
classification, achieving state-of-the-art results on various benchmarks.
However, most existing appro... | computer science |
27,709 | Deep Multimodal Representation Learning from Temporal Data | cs.CV | In recent years, Deep Learning has been successfully applied to multimodal
learning problems, with the aim of learning useful joint representations in
data fusion applications. When the available modalities consist of time series
data such as video, audio and sensor signals, it becomes imperative to consider
their temp... | computer science |
27,710 | EAST: An Efficient and Accurate Scene Text Detector | cs.CV | Previous approaches for scene text detection have already achieved promising
performances across various benchmarks. However, they usually fall short when
dealing with challenging scenarios, even when equipped with deep neural network
models, because the overall performance is determined by the interplay of
multiple st... | computer science |
27,711 | Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question
Answering | cs.CV | This paper presents a new baseline for visual question answering task. Given
an image and a question in natural language, our model produces accurate
answers according to the content of the image. Our model, while being
architecturally simple and relatively small in terms of trainable parameters,
sets a new state of th... | computer science |
27,712 | Mining Object Parts from CNNs via Active Question-Answering | cs.CV | Given a convolutional neural network (CNN) that is pre-trained for object
classification, this paper proposes to use active question-answering to
semanticize neural patterns in conv-layers of the CNN and mine part concepts.
For each part concept, we mine neural patterns in the pre-trained CNN, which
are related to the ... | computer science |
27,713 | Pyramidal Gradient Matching for Optical Flow Estimation | cs.CV | Initializing optical flow field by either sparse descriptor matching or dense
patch matches has been proved to be particularly useful for capturing large
displacements. In this paper, we present a pyramidal gradient matching approach
that can provide dense matches for highly accurate and efficient optical flow
estimati... | computer science |
27,714 | Learning Deep CNN Denoiser Prior for Image Restoration | cs.CV | Model-based optimization methods and discriminative learning methods have
been the two dominant strategies for solving various inverse problems in
low-level vision. Typically, those two kinds of methods have their respective
merits and drawbacks, e.g., model-based optimization methods are flexible for
handling differen... | computer science |
27,715 | Simultaneous Stereo Video Deblurring and Scene Flow Estimation | cs.CV | Videos for outdoor scene often show unpleasant blur effects due to the large
relative motion between the camera and the dynamic objects and large depth
variations. Existing works typically focus monocular video deblurring. In this
paper, we propose a novel approach to deblurring from stereo videos. In
particular, we ex... | computer science |
27,716 | Online Video Deblurring via Dynamic Temporal Blending Network | cs.CV | State-of-the-art video deblurring methods are capable of removing non-uniform
blur caused by unwanted camera shake and/or object motion in dynamic scenes.
However, most existing methods are based on batch processing and thus need
access to all recorded frames, rendering them computationally demanding and
time consuming... | computer science |
27,717 | Automatic segmentation of MR brain images with a convolutional neural
network | cs.CV | Automatic segmentation in MR brain images is important for quantitative
analysis in large-scale studies with images acquired at all ages.
This paper presents a method for the automatic segmentation of MR brain
images into a number of tissue classes using a convolutional neural network. To
ensure that the method obtai... | computer science |
27,718 | Deep Learning for Multi-Task Medical Image Segmentation in Multiple
Modalities | cs.CV | Automatic segmentation of medical images is an important task for many
clinical applications. In practice, a wide range of anatomical structures are
visualised using different imaging modalities. In this paper, we investigate
whether a single convolutional neural network (CNN) can be trained to perform
different segmen... | computer science |
27,719 | A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection | cs.CV | How do we learn an object detector that is invariant to occlusions and
deformations? Our current solution is to use a data-driven strategy -- collect
large-scale datasets which have object instances under different conditions.
The hope is that the final classifier can use these examples to learn
invariances. But is it ... | computer science |
27,720 | Forecasting Human Dynamics from Static Images | cs.CV | This paper presents the first study on forecasting human dynamics from static
images. The problem is to input a single RGB image and generate a sequence of
upcoming human body poses in 3D. To address the problem, we propose the 3D Pose
Forecasting Network (3D-PFNet). Our 3D-PFNet integrates recent advances on
single-im... | computer science |
27,721 | Learning Two-Branch Neural Networks for Image-Text Matching Tasks | cs.CV | Image-language matching tasks have recently attracted a lot of attention in
the computer vision field. These tasks include image-sentence matching, i.e.,
given an image query, retrieving relevant sentences and vice versa, and
region-phrase matching or visual grounding, i.e., matching a phrase to relevant
regions. This ... | computer science |
27,722 | Learning Proximal Operators: Using Denoising Networks for Regularizing
Inverse Imaging Problems | cs.CV | While variational methods have been among the most powerful tools for solving
linear inverse problems in imaging, deep (convolutional) neural networks have
recently taken the lead in many challenging benchmarks. A remaining drawback of
deep learning approaches is their requirement for an expensive retraining
whenever t... | computer science |
27,723 | CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction | cs.CV | Given the recent advances in depth prediction from Convolutional Neural
Networks (CNNs), this paper investigates how predicted depth maps from a deep
neural network can be deployed for accurate and dense monocular reconstruction.
We propose a method where CNN-predicted dense depth maps are naturally fused
together with... | computer science |
27,724 | Creativity: Generating Diverse Questions using Variational Autoencoders | cs.CV | Generating diverse questions for given images is an important task for
computational education, entertainment and AI assistants. Different from many
conventional prediction techniques is the need for algorithms to generate a
diverse set of plausible questions, which we refer to as "creativity". In this
paper we propose... | computer science |
27,725 | Learning Detection with Diverse Proposals | cs.CV | To predict a set of diverse and informative proposals with enriched
representations, this paper introduces a differentiable Determinantal Point
Process (DPP) layer that is able to augment the object detection architectures.
Most modern object detection architectures, such as Faster R-CNN, learn to
localize objects by m... | computer science |
27,726 | Attention-based Extraction of Structured Information from Street View
Imagery | cs.CV | We present a neural network model - based on CNNs, RNNs and a novel attention
mechanism - which achieves 84.2% accuracy on the challenging French Street Name
Signs (FSNS) dataset, significantly outperforming the previous state of the art
(Smith'16), which achieved 72.46%. Furthermore, our new method is much simpler
and... | computer science |
27,727 | Cutting the Error by Half: Investigation of Very Deep CNN and Advanced
Training Strategies for Document Image Classification | cs.CV | We present an exhaustive investigation of recent Deep Learning architectures,
algorithms, and strategies for the task of document image classification to
finally reduce the error by more than half. Existing approaches, such as the
DeepDocClassifier, apply standard Convolutional Network architectures with
transfer learn... | computer science |
27,728 | Reformulating Level Sets as Deep Recurrent Neural Network Approach to
Semantic Segmentation | cs.CV | Variational Level Set (LS) has been a widely used method in medical
segmentation. However, it is limited when dealing with multi-instance objects
in the real world. In addition, its segmentation results are quite sensitive to
initial settings and highly depend on the number of iterations. To address
these issues and bo... | computer science |
27,729 | Deep Contextual Recurrent Residual Networks for Scene Labeling | cs.CV | Designed as extremely deep architectures, deep residual networks which
provide a rich visual representation and offer robust convergence behaviors
have recently achieved exceptional performance in numerous computer vision
problems. Being directly applied to a scene labeling problem, however, they
were limited to captur... | computer science |
27,730 | Instance-Level Salient Object Segmentation | cs.CV | Image saliency detection has recently witnessed rapid progress due to deep
convolutional neural networks. However, none of the existing methods is able to
identify object instances in the detected salient regions. In this paper, we
present a salient instance segmentation method that produces a saliency mask
with distin... | computer science |
27,731 | Automatic Discovery, Association Estimation and Learning of Semantic
Attributes for a Thousand Categories | cs.CV | Attribute-based recognition models, due to their impressive performance and
their ability to generalize well on novel categories, have been widely adopted
for many computer vision applications. However, usually both the attribute
vocabulary and the class-attribute associations have to be provided manually by
domain exp... | computer science |
27,732 | Predictive-Corrective Networks for Action Detection | cs.CV | While deep feature learning has revolutionized techniques for static-image
understanding, the same does not quite hold for video processing. Architectures
and optimization techniques used for video are largely based off those for
static images, potentially underutilizing rich video information. In this work,
we rethink... | computer science |
27,733 | Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline
Optimization | cs.CV | Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an
area of interest for quantification of regional cardiac function from balanced,
steady state free precession (SSFP) cine sequences. However, currently
available techniques lack full automation, limiting reproducibility. We propose
a fully auto... | computer science |
27,734 | Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation
in Congenital Heart Disease | cs.CV | We propose an automatic method using dilated convolutional neural networks
(CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR
(CMR) of patients with congenital heart disease (CHD).
Ten training and ten test CMR scans cropped to an ROI around the heart were
provided in the MICCAI 2016 HVSMR ... | computer science |
27,735 | Object proposal generation applying the distance dependent Chinese
restaurant process | cs.CV | In application domains such as robotics, it is useful to represent the
uncertainty related to the robot's belief about the state of its environment.
Algorithms that only yield a single "best guess" as a result are not
sufficient. In this paper, we propose object proposal generation based on
non-parametric Bayesian infe... | computer science |
27,736 | Unsupervised Construction of Human Body Models Using Principles of
Organic Computing | cs.CV | Unsupervised learning of a generalizable model of the visual appearance of
humans from video data is of major importance for computing systems interacting
naturally with their users and others. We propose a step towards automatic
behavior understanding by integrating principles of Organic Computing into the
posture est... | computer science |
27,737 | Ensemble classifier approach in breast cancer detection and malignancy
grading- A review | cs.CV | The diagnosed cases of Breast cancer is increasing annually and unfortunately
getting converted into a high mortality rate. Cancer, at the early stages, is
hard to detect because the malicious cells show similar properties (density) as
shown by the non-malicious cells. The mortality ratio could have been minimized
if t... | computer science |
27,738 | Attention-Set based Metric Learning for Video Face Recognition | cs.CV | Face recognition has made great progress with the development of deep
learning. However, video face recognition (VFR) is still an ongoing task due to
various illumination, low-resolution, pose variations and motion blur. Most
existing CNN-based VFR methods only obtain a feature vector from a single image
and simply agg... | computer science |
27,739 | Connecting Look and Feel: Associating the visual and tactile properties
of physical materials | cs.CV | For machines to interact with the physical world, they must understand the
physical properties of objects and materials they encounter. We use fabrics as
an example of a deformable material with a rich set of mechanical properties. A
thin flexible fabric, when draped, tends to look different from a heavy stiff
fabric. ... | computer science |
27,740 | Optimal Threshold Design for Quanta Image Sensor | cs.CV | Quanta Image Sensor (QIS) is a binary imaging device envisioned to be the
next generation image sensor after CCD and CMOS. Equipped with a massive number
of single photon detectors, the sensor has a threshold $q$ above which the
number of arriving photons will trigger a binary response "1", or "0"
otherwise. Existing m... | computer science |
27,741 | What's in a Question: Using Visual Questions as a Form of Supervision | cs.CV | Collecting fully annotated image datasets is challenging and expensive. Many
types of weak supervision have been explored: weak manual annotations, web
search results, temporal continuity, ambient sound and others. We focus on one
particular unexplored mode: visual questions that are asked about images. The
key observa... | computer science |
27,742 | Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution | cs.CV | Convolutional neural networks have recently demonstrated high-quality
reconstruction for single-image super-resolution. In this paper, we propose the
Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively
reconstruct the sub-band residuals of high-resolution images. At each pyramid
level, our model takes ... | computer science |
27,743 | Asymmetric Feature Maps with Application to Sketch Based Retrieval | cs.CV | We propose a novel concept of asymmetric feature maps (AFM), which allows to
evaluate multiple kernels between a query and database entries without
increasing the memory requirements. To demonstrate the advantages of the AFM
method, we derive a short vector image representation that, due to asymmetric
feature maps, sup... | computer science |
27,744 | Efficient Sparse Subspace Clustering by Nearest Neighbour Filtering | cs.CV | Sparse Subspace Clustering (SSC) has been used extensively for subspace
identification tasks due to its theoretical guarantees and relative ease of
implementation. However SSC has quadratic computation and memory requirements
with respect to the number of input data points. This burden has prohibited
SSCs use for all b... | computer science |
27,745 | Tractable Clustering of Data on the Curve Manifold | cs.CV | In machine learning it is common to interpret each data point as a vector in
Euclidean space. However the data may actually be functional i.e.\ each data
point is a function of some variable such as time and the function is
discretely sampled. The naive treatment of functional data as traditional
multivariate data can ... | computer science |
27,746 | Collaborative Low-Rank Subspace Clustering | cs.CV | In this paper we present Collaborative Low-Rank Subspace Clustering. Given
multiple observations of a phenomenon we learn a unified representation matrix.
This unified matrix incorporates the features from all the observations, thus
increasing the discriminative power compared with learning the representation
matrix on... | computer science |
27,747 | 2D-3D Pose Consistency-based Conditional Random Fields for 3D Human Pose
Estimation | cs.CV | This study considers the 3D human pose estimation problem in a single RGB
image by proposing a conditional random field (CRF) model over 2D poses, in
which the 3D pose is obtained as a byproduct of the inference process. The
unary term of the proposed CRF model is defined based on a powerful heat-map
regression network... | computer science |
27,748 | Interspecies Knowledge Transfer for Facial Keypoint Detection | cs.CV | We present a method for localizing facial keypoints on animals by
transferring knowledge gained from human faces. Instead of directly finetuning
a network trained to detect keypoints on human faces to animal faces (which is
sub-optimal since human and animal faces can look quite different), we propose
to first adapt th... | computer science |
27,749 | Zero-order Reverse Filtering | cs.CV | In this paper, we study an unconventional but practically meaningful
reversibility problem of commonly used image filters. We broadly define filters
as operations to smooth images or to produce layers via global or local
algorithms. And we raise the intriguingly problem if they are reservable to the
status before filte... | computer science |
27,750 | Saliency-guided Adaptive Seeding for Supervoxel Segmentation | cs.CV | We propose a new saliency-guided method for generating supervoxels in 3D
space. Rather than using an evenly distributed spatial seeding procedure, our
method uses visual saliency to guide the process of supervoxel generation. This
results in densely distributed, small, and precise supervoxels in salient
regions which o... | computer science |
27,751 | DCFNet: Discriminant Correlation Filters Network for Visual Tracking | cs.CV | Discriminant Correlation Filters (DCF) based methods now become a kind of
dominant approach to online object tracking. The features used in these
methods, however, are either based on hand-crafted features like HoGs, or
convolutional features trained independently from other tasks like image
classification. In this wor... | computer science |
27,752 | Learning to Estimate Pose by Watching Videos | cs.CV | In this paper we propose a technique for obtaining coarse pose estimation of
humans in an image that does not require any manual supervision. While a
general unsupervised technique would fail to estimate human pose, we suggest
that sufficient information about coarse pose can be obtained by observing
human motion in mu... | computer science |
27,753 | Beyond Face Rotation: Global and Local Perception GAN for Photorealistic
and Identity Preserving Frontal View Synthesis | cs.CV | Photorealistic frontal view synthesis from a single face image has a wide
range of applications in the field of face recognition. Although data-driven
deep learning methods have been proposed to address this problem by seeking
solutions from ample face data, this problem is still challenging because it is
intrinsically... | computer science |
27,754 | Recognizing Activities of Daily Living from Egocentric Images | cs.CV | Recognizing Activities of Daily Living (ADLs) has a large number of health
applications, such as characterize lifestyle for habit improvement, nursing and
rehabilitation services. Wearable cameras can daily gather large amounts of
image data that provide rich visual information about ADLs than using other
wearable sens... | computer science |
27,755 | Single Image Super-Resolution based on Wiener Filter in Similarity
Domain | cs.CV | Single image super resolution (SISR) is an ill-posed problem aiming at
estimating a plausible high resolution (HR) image from a single low resolution
(LR) image. Current state-of-the-art SISR methods are patch-based. They use
either external data or internal self-similarity to learn a prior for a HR
image. External dat... | computer science |
27,756 | Neural Face Editing with Intrinsic Image Disentangling | cs.CV | Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled r... | computer science |
27,757 | A Procedural Texture Generation Framework Based on Semantic Descriptions | cs.CV | Procedural textures are normally generated from mathematical models with
parameters carefully selected by experienced users. However, for naive users,
the intuitive way to obtain a desired texture is to provide semantic
descriptions such as "regular," "lacelike," and "repetitive" and then a
procedural model with proper... | computer science |
27,758 | Video Acceleration Magnification | cs.CV | The ability to amplify or reduce subtle image changes over time is useful in
contexts such as video editing, medical video analysis, product quality control
and sports. In these contexts there is often large motion present which
severely distorts current video amplification methods that magnify change
linearly. In this... | computer science |
27,759 | Spatial Memory for Context Reasoning in Object Detection | cs.CV | Modeling instance-level context and object-object relationships is extremely
challenging. It requires reasoning about bounding boxes of different classes,
locations \etc. Above all, instance-level spatial reasoning inherently requires
modeling conditional distributions on previous detections. Unfortunately, our
current... | computer science |
27,760 | Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised
Object and Action Localization | cs.CV | We propose `Hide-and-Seek', a weakly-supervised framework that aims to
improve object localization in images and action localization in videos. Most
existing weakly-supervised methods localize only the most discriminative parts
of an object rather than all relevant parts, which leads to suboptimal
performance. Our key ... | computer science |
27,761 | Visual Recognition of Paper Analytical Device Images for Detection of
Falsified Pharmaceuticals | cs.CV | Falsification of medicines is a big problem in many developing countries,
where technological infrastructure is inadequate to detect these harmful
products. We have developed a set of inexpensive paper cards, called Paper
Analytical Devices (PADs), which can efficiently classify drugs based on their
chemical compositio... | computer science |
27,762 | FastVentricle: Cardiac Segmentation with ENet | cs.CV | Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac
structure and function. One disadvantage of CMR is that post-processing of
exams is tedious. Without automation, precise assessment of cardiac function
via CMR typically requires an annotator to spend tens of minutes per case
manually contourin... | computer science |
27,763 | Dataset Augmentation for Pose and Lighting Invariant Face Recognition | cs.CV | The performance of modern face recognition systems is a function of the
dataset on which they are trained. Most datasets are largely biased toward
"near-frontal" views with benign lighting conditions, negatively effecting
recognition performance on images that do not meet these criteria. The proposed
approach demonstra... | computer science |
27,764 | Camera Calibration by Global Constraints on the Motion of Silhouettes | cs.CV | We address the problem of epipolar geometry using the motion of silhouettes.
Such methods match epipolar lines or frontier points across views, which are
then used as the set of putative correspondences. We introduce an approach that
improves by two orders of magnitude the performance over state-of-the-art
methods, by ... | computer science |
27,765 | DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting
Agents | cs.CV | We introduce a Deep Stochastic IOC RNN Encoderdecoder framework, DESIRE, for
the task of future predictions of multiple interacting agents in dynamic
scenes. DESIRE effectively predicts future locations of objects in multiple
scenes by 1) accounting for the multi-modal nature of the future prediction
(i.e., given the s... | computer science |
27,766 | Deep Structured Learning for Facial Action Unit Intensity Estimation | cs.CV | We consider the task of automated estimation of facial expression intensity.
This involves estimation of multiple output variables (facial action units ---
AUs) that are structurally dependent. Their structure arises from statistically
induced co-occurrence patterns of AU intensity levels. Modeling this structure
is cr... | computer science |
27,767 | TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering | cs.CV | Vision and language understanding has emerged as a subject undergoing intense
study in Artificial Intelligence. Among many tasks in this line of research,
visual question answering (VQA) has been one of the most successful ones, where
the goal is to learn a model that understands visual content at region-level
details ... | computer science |
27,768 | Soft-NMS -- Improving Object Detection With One Line of Code | cs.CV | Non-maximum suppression is an integral part of the object detection pipeline.
First, it sorts all detection boxes on the basis of their scores. The detection
box M with the maximum score is selected and all other detection boxes with a
significant overlap (using a pre-defined threshold) with M are suppressed. This
proc... | computer science |
27,769 | Recovery of damped exponentials using structured low rank matrix
completion | cs.CV | We introduce a structured low rank matrix completion algorithm to recover a
series of images from their under-sampled measurements, where the signal along
the parameter dimension at every pixel is described by a linear combination of
exponentials. We exploit the exponential behavior of the signal at every pixel,
along ... | computer science |
27,770 | Interpretable 3D Human Action Analysis with Temporal Convolutional
Networks | cs.CV | The discriminative power of modern deep learning models for 3D human action
recognition is growing ever so potent. In conjunction with the recent
resurgence of 3D human action representation with 3D skeletons, the quality and
the pace of recent progress have been significant. However, the inner workings
of state-of-the... | computer science |
27,771 | Integrating Scene Text and Visual Appearance for Fine-Grained Image
Classification | cs.CV | Text in natural images contains rich semantics that are often highly relevant
to objects or scene. In this paper, we focus on the problem of fully exploiting
scene text for visual understanding. The main idea is combining word
representations and deep visual features into a globally trainable deep
convolutional neural ... | computer science |
27,772 | Temporal Action Localization by Structured Maximal Sums | cs.CV | We address the problem of temporal action localization in videos. We pose
action localization as a structured prediction over arbitrary-length temporal
windows, where each window is scored as the sum of frame-wise classification
scores. Additionally, our model classifies the start, middle, and end of each
action as sep... | computer science |
27,773 | Video Fill In the Blank using LR/RL LSTMs with Spatial-Temporal
Attentions | cs.CV | Given a video and a description sentence with one missing word (we call it
the "source sentence"), Video-Fill-In-the-Blank (VFIB) problem is to find the
missing word automatically. The contextual information of the sentence, as well
as visual cues from the video, are important to infer the missing word
accurately. Sinc... | computer science |
27,774 | AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive
Features For Semantic Matching | cs.CV | Despite significant progress of deep learning in recent years,
state-of-the-art semantic matching methods still rely on legacy features such
as SIFT or HoG. We argue that the strong invariance properties that are key to
the success of recent deep architectures on the classification task make them
unfit for dense corres... | computer science |
27,775 | Harvesting Multiple Views for Marker-less 3D Human Pose Annotations | cs.CV | Recent advances with Convolutional Networks (ConvNets) have shifted the
bottleneck for many computer vision tasks to annotated data collection. In this
paper, we present a geometry-driven approach to automatically collect
annotations for human pose prediction tasks. Starting from a generic ConvNet
for 2D human pose, an... | computer science |
27,776 | Replicator Equation: Applications Revisited | cs.CV | The replicator equation is a simple model of evolution that leads to stable
form of Nash Equilibrium, Evolutionary Stable Strategy (ESS). It has been
studied in connection with Evolutionary Game Theory and was originally
developed for symmetric games. Beyond its first emphasis in biological use,
evolutionary game theor... | computer science |
27,777 | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
Applications | cs.CV | We present a class of efficient models called MobileNets for mobile and
embedded vision applications. MobileNets are based on a streamlined
architecture that uses depth-wise separable convolutions to build light weight
deep neural networks. We introduce two simple global hyper-parameters that
efficiently trade off betw... | computer science |
27,778 | Least square ellipsoid fitting using iterative orthogonal
transformations | cs.CV | We describe a generalised method for ellipsoid fitting against a minimum set
of data points. The proposed method is numerically stable and applies to a wide
range of ellipsoidal shapes, including highly elongated and arbitrarily
oriented ellipsoids. This new method also provides for the retrieval of
rotational angle an... | computer science |
27,779 | AMTnet: Action-Micro-Tube Regression by End-to-end Trainable Deep
Architecture | cs.CV | Dominant approaches to action detection can only provide sub-optimal
solutions to the problem, as they rely on seeking frame-level detections, to
later compose them into "action tubes" in a post-processing step. With this
paper we radically depart from current practice, and take a first step towards
the design and impl... | computer science |
27,780 | End-to-end 3D face reconstruction with deep neural networks | cs.CV | Monocular 3D facial shape reconstruction from a single 2D facial image has
been an active research area due to its wide applications. Inspired by the
success of deep neural networks (DNN), we propose a DNN-based approach for
End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different
from recent work... | computer science |
27,781 | A Gabor Filter Texture Analysis Approach for Histopathological Brain
Tumor Subtype Discrimination | cs.CV | Meningioma brain tumour discrimination is challenging as many histological
patterns are mixed between the different subtypes. In clinical practice,
dominant patterns are investigated for signs of specific meningioma pathology;
however the simple observation could result in inter- and intra-observer
variation due to the... | computer science |
27,782 | Video Object Segmentation using Supervoxel-Based Gerrymandering | cs.CV | Pixels operate locally. Superpixels have some potential to collect
information across many pixels; supervoxels have more potential by implicitly
operating across time. In this paper, we explore this well established notion
thoroughly analyzing how supervoxels can be used in place of and in conjunction
with other means ... | computer science |
27,783 | Deep Self-Taught Learning for Weakly Supervised Object Localization | cs.CV | Most existing weakly supervised localization (WSL) approaches learn detectors
by finding positive bounding boxes based on features learned with image-level
supervision. However, those features do not contain spatial location related
information and usually provide poor-quality positive samples for training a
detector. ... | computer science |
27,784 | Fast 2-D Complex Gabor Filter with Kernel Decomposition | cs.CV | 2-D complex Gabor filtering has found numerous applications in the fields of
computer vision and image processing. Especially, in some applications, it is
often needed to compute 2-D complex Gabor filter bank consisting of the 2-D
complex Gabor filtering outputs at multiple orientations and frequencies.
Although severa... | computer science |
27,785 | Robust Optical Flow Estimation in Rainy Scenes | cs.CV | Optical flow estimation in the rainy scenes is challenging due to background
degradation introduced by rain streaks and rain accumulation effects in the
scene. Rain accumulation effect refers to poor visibility of remote objects due
to the intense rainfall. Most existing optical flow methods are erroneous when
applied ... | computer science |
27,786 | A Comment on "Analysis of Video Image Sequences Using Point and Line
Correspondences" | cs.CV | In this paper we would like to deny the results of Wang et al. raising two
fundamental claims:
* A line does not contribute anything to recognition of motion parameters
from two images
* Four traceable points are not sufficient to recover motion parameters from
two perspective
To be constructive, however, we show... | computer science |
27,787 | Light Field Blind Motion Deblurring | cs.CV | We study the problem of deblurring light fields of general 3D scenes captured
under 3D camera motion and present both theoretical and practical
contributions. By analyzing the motion-blurred light field in the primal and
Fourier domains, we develop intuition into the effects of camera motion on the
light field, show th... | computer science |
27,788 | Learning to Reason: End-to-End Module Networks for Visual Question
Answering | cs.CV | Natural language questions are inherently compositional, and many are most
easily answered by reasoning about their decomposition into modular
sub-problems. For example, to answer "is there an equal number of balls and
boxes?" we can look for balls, look for boxes, count them, and compare the
results. The recently prop... | computer science |
27,789 | Annotating Object Instances with a Polygon-RNN | cs.CV | We propose an approach for semi-automatic annotation of object instances.
While most current methods treat object segmentation as a pixel-labeling
problem, we here cast it as a polygon prediction task, mimicking how most
current datasets have been annotated. In particular, our approach takes as
input an image crop and ... | computer science |
27,790 | Illuminant Spectra-based Source Separation Using Flash Photography | cs.CV | Real-world lighting often consists of multiple illuminants with different
spectra. Separating and manipulating these illuminants in post-process is a
challenging problem that requires either significant manual input or calibrated
scene geometry and lighting. In this work, we leverage a flash/no-flash image
pair to anal... | computer science |
27,791 | FSITM: A Feature Similarity Index For Tone-Mapped Images | cs.CV | In this work, based on the local phase information of images, an objective
index, called the feature similarity index for tone-mapped images (FSITM), is
proposed. To evaluate a tone mapping operator (TMO), the proposed index
compares the locally weighted mean phase angle map of an original high dynamic
range (HDR) to t... | computer science |
27,792 | ConvNet-Based Localization of Anatomical Structures in 3D Medical Images | cs.CV | Localization of anatomical structures is a prerequisite for many tasks in
medical image analysis. We propose a method for automatic localization of one
or more anatomical structures in 3D medical images through detection of their
presence in 2D image slices using a convolutional neural network (ConvNet).
A single Con... | computer science |
27,793 | Skeleton Boxes: Solving skeleton based action detection with a single
deep convolutional neural network | cs.CV | Action recognition from well-segmented 3D skeleton video has been intensively
studied. However, due to the difficulty in representing the 3D skeleton video
and the lack of training data, action detection from streaming 3D skeleton
video still lags far behind its recognition counterpart and image based object
detection.... | computer science |
27,794 | Skeleton based action recognition using translation-scale invariant
image mapping and multi-scale deep cnn | cs.CV | This paper presents an image classification based approach for skeleton-based
video action recognition problem. Firstly, A dataset independent
translation-scale invariant image mapping method is proposed, which transformes
the skeleton videos to colour images, named skeleton-images. Secondly, A
multi-scale deep convolu... | computer science |
27,795 | Unsupervised object segmentation in video by efficient selection of
highly probable positive features | cs.CV | We address an essential problem in computer vision, that of unsupervised
object segmentation in video, where a main object of interest in a video
sequence should be automatically separated from its background. An efficient
solution to this task would enable large-scale video interpretation at a high
semantic level in t... | computer science |
27,796 | Design of low-cost, compact and weather-proof whole sky imagers for
high-dynamic-range captures | cs.CV | Ground-based whole sky imagers are popular for monitoring cloud formations,
which is necessary for various applications. We present two new Wide Angle
High-Resolution Sky Imaging System (WAHRSIS) models, which were designed
especially to withstand the hot and humid climate of Singapore. The first uses
a fully sealed ca... | computer science |
27,797 | Learning Video Object Segmentation with Visual Memory | cs.CV | This paper addresses the task of segmenting moving objects in unconstrained
videos. We introduce a novel two-stream neural network with an explicit memory
module to achieve this. The two streams of the network encode spatial and
temporal features in a video sequence respectively, while the memory module
captures the ev... | computer science |
27,798 | A location-aware embedding technique for accurate landmark recognition | cs.CV | The current state of the research in landmark recognition highlights the good
accuracy which can be achieved by embedding techniques, such as Fisher vector
and VLAD. All these techniques do not exploit spatial information, i.e.
consider all the features and the corresponding descriptors without embedding
their location... | computer science |
27,799 | Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection | cs.CV | People detection in single 2D images has improved greatly in recent years.
However, comparatively little of this progress has percolated into multi-camera
multi-people tracking algorithms, whose performance still degrades severely
when scenes become very crowded. In this work, we introduce a new architecture
that combi... | computer science |
27,800 | Accurate Single Stage Detector Using Recurrent Rolling Convolution | cs.CV | Most of the recent successful methods in accurate object detection and
localization used some variants of R-CNN style two stage Convolutional Neural
Networks (CNN) where plausible regions were proposed in the first stage then
followed by a second stage for decision refinement. Despite the simplicity of
training and the... | computer science |
27,801 | Learn to Model Motion from Blurry Footages | cs.CV | It is difficult to recover the motion field from a real-world footage given a
mixture of camera shake and other photometric effects. In this paper we propose
a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a
traditional optical flow energy. We first conduct a CNN architecture using a
novel l... | computer science |
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