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26,802 | Learning Deep Representations Using Convolutional Auto-encoders with
Symmetric Skip Connections | cs.CV | Unsupervised pre-training was a critical technique for training deep neural
networks years ago. With sufficient labeled data and modern training
techniques, it is possible to train very deep neural networks from scratch in a
purely supervised manner nowadays. However, unlabeled data is easier to obtain
and usually of v... | computer science |
26,803 | Large-Scale Shape Retrieval with Sparse 3D Convolutional Neural Networks | cs.CV | In this paper we present results of performance evaluation of S3DCNN - a
Sparse 3D Convolutional Neural Network - on a large-scale 3D Shape benchmark
ModelNet40, and measure how it is impacted by voxel resolution of input shape.
We demonstrate comparable classification and retrieval performance to
state-of-the-art mode... | computer science |
26,804 | Who's that Actor? Automatic Labelling of Actors in TV series starting
from IMDB Images | cs.CV | In this work, we aim at automatically labeling actors in a TV series. Rather
than relying on transcripts and subtitles, as has been demonstrated in the
past, we show how to achieve this goal starting from a set of example images of
each of the main actors involved, collected from the Internet Movie Database
(IMDB). The... | computer science |
26,805 | Computational Mapping of the Ground Reflectivity with Laser Scanners | cs.CV | In this investigation we focus on the problem of mapping the ground
reflectivity with multiple laser scanners mounted on mobile robots/vehicles.
The problem originates because regions of the ground become populated with a
varying number of reflectivity measurements whose value depends on the observer
and its correspond... | computer science |
26,806 | ECO: Efficient Convolution Operators for Tracking | cs.CV | In recent years, Discriminative Correlation Filter (DCF) based methods have
significantly advanced the state-of-the-art in tracking. However, in the
pursuit of ever increasing tracking performance, their characteristic speed and
real-time capability have gradually faded. Further, the increasingly complex
models, with m... | computer science |
26,807 | Gaze Embeddings for Zero-Shot Image Classification | cs.CV | Zero-shot image classification using auxiliary information, such as
attributes describing discriminative object properties, requires time-consuming
annotation by domain experts. We instead propose a method that relies on human
gaze as auxiliary information, exploiting that even non-expert users have a
natural ability t... | computer science |
26,808 | Hierarchical Boundary-Aware Neural Encoder for Video Captioning | cs.CV | The use of Recurrent Neural Networks for video captioning has recently gained
a lot of attention, since they can be used both to encode the input video and
to generate the corresponding description. In this paper, we present a
recurrent video encoding scheme which can discover and leverage the
hierarchical structure of... | computer science |
26,809 | What Is Around The Camera? | cs.CV | How much does a single image reveal about the environment it was taken in? In
this paper, we investigate how much of that information can be retrieved from a
foreground object, combined with the background (i.e. the visible part of the
environment). Assuming it is not perfectly diffuse, the foreground object acts
as a ... | computer science |
26,810 | The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for
Semantic Segmentation | cs.CV | State-of-the-art approaches for semantic image segmentation are built on
Convolutional Neural Networks (CNNs). The typical segmentation architecture is
composed of (a) a downsampling path responsible for extracting coarse semantic
features, followed by (b) an upsampling path trained to recover the input image
resolutio... | computer science |
26,811 | Material Recognition from Local Appearance in Global Context | cs.CV | Recognition of materials has proven to be a challenging problem due to the
wide variation in appearance within and between categories. Global image
context, such as where the material is or what object it makes up, can be
crucial to recognizing the material. Existing methods, however, operate on an
implicit fusion of m... | computer science |
26,812 | Social Behavior Prediction from First Person Videos | cs.CV | This paper presents a method to predict the future movements (location and
gaze direction) of basketball players as a whole from their first person
videos. The predicted behaviors reflect an individual physical space that
affords to take the next actions while conforming to social behaviors by
engaging to joint attenti... | computer science |
26,813 | Inertial-Based Scale Estimation for Structure from Motion on Mobile
Devices | cs.CV | Structure from motion algorithms have an inherent limitation that the
reconstruction can only be determined up to the unknown scale factor. Modern
mobile devices are equipped with an inertial measurement unit (IMU), which can
be used for estimating the scale of the reconstruction. We propose a method
that recovers the ... | computer science |
26,814 | Deep Quantization: Encoding Convolutional Activations with Deep
Generative Model | cs.CV | Deep convolutional neural networks (CNNs) have proven highly effective for
visual recognition, where learning a universal representation from activations
of convolutional layer plays a fundamental problem. In this paper, we present
Fisher Vector encoding with Variational Auto-Encoder (FV-VAE), a novel deep
architecture... | computer science |
26,815 | Lens Distortion Rectification using Triangulation based Interpolation | cs.CV | Nonlinear lens distortion rectification is a common first step in image
processing applications where the assumption of a linear camera model is
essential. For rectifying the lens distortion, forward distortion model needs
to be known. However, many self-calibration methods estimate the inverse
distortion model. In the... | computer science |
26,816 | Predicting Human Eye Fixations via an LSTM-based Saliency Attentive
Model | cs.CV | Data-driven saliency has recently gained a lot of attention thanks to the use
of Convolutional Neural Networks for predicting gaze fixations. In this paper
we go beyond standard approaches to saliency prediction, in which gaze maps are
computed with a feed-forward network, and we present a novel model which can
predict... | computer science |
26,817 | Occlusion-Aware Video Deblurring with a New Layered Blur Model | cs.CV | We present a deblurring method for scenes with occluding objects using a
carefully designed layered blur model. Layered blur model is frequently used in
the motion deblurring problem to handle locally varying blurs, which is caused
by object motions or depth variations in a scene. However, conventional models
have a li... | computer science |
26,818 | Fast Face-swap Using Convolutional Neural Networks | cs.CV | We consider the problem of face swapping in images, where an input identity
is transformed into a target identity while preserving pose, facial expression,
and lighting. To perform this mapping, we use convolutional neural networks
trained to capture the appearance of the target identity from an unstructured
collection... | computer science |
26,819 | A Large-scale Distributed Video Parsing and Evaluation Platform | cs.CV | Visual surveillance systems have become one of the largest data sources of
Big Visual Data in real world. However, existing systems for video analysis
still lack the ability to handle the problems of scalability, expansibility and
error-prone, though great advances have been achieved in a number of visual
recognition t... | computer science |
26,820 | Surveillance Video Parsing with Single Frame Supervision | cs.CV | Surveillance video parsing, which segments the video frames into several
labels, e.g., face, pants, left-leg, has wide applications.
However,pixel-wisely annotating all frames is tedious and inefficient. In this
paper, we develop a Single frame Video Parsing (SVP) method which requires only
one labeled frame per video ... | computer science |
26,821 | Efficient Linear Programming for Dense CRFs | cs.CV | The fully connected conditional random field (CRF) with Gaussian pairwise
potentials has proven popular and effective for multi-class semantic
segmentation. While the energy of a dense CRF can be minimized accurately using
a linear programming (LP) relaxation, the state-of-the-art algorithm is too
slow to be useful in ... | computer science |
26,822 | Computer Aided Detection of Oral Lesions on CT Images | cs.CV | Oral lesions are important findings on computed tomography (CT) images. In
this study, a fully automatic method to detect oral lesions in mandibular
region from dental CT images is proposed. Two methods were developed to
recognize two types of lesions namely (1) Close border (CB) lesions and (2)
Open border (OB) lesion... | computer science |
26,823 | InterpoNet, A brain inspired neural network for optical flow dense
interpolation | cs.CV | Sparse-to-dense interpolation for optical flow is a fundamental phase in the
pipeline of most of the leading optical flow estimation algorithms. The current
state-of-the-art method for interpolation, EpicFlow, is a local average method
based on an edge aware geodesic distance. We propose a new data-driven
sparse-to-den... | computer science |
26,824 | 3D Ultrasound image segmentation: A Survey | cs.CV | Three-dimensional Ultrasound image segmentation methods are surveyed in this
paper. The focus of this report is to investigate applications of these
techniques and a review of the original ideas and concepts. Although many
two-dimensional image segmentation in the literature have been considered as a
three-dimensional ... | computer science |
26,825 | Monocular 3D Human Pose Estimation In The Wild Using Improved CNN
Supervision | cs.CV | We propose a CNN-based approach for 3D human body pose estimation from single
RGB images that addresses the issue of limited generalizability of models
trained solely on the starkly limited publicly available 3D pose data. Using
only the existing 3D pose data and 2D pose data, we show state-of-the-art
performance on es... | computer science |
26,826 | Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel
Prediction | cs.CV | We propose split-brain autoencoders, a straightforward modification of the
traditional autoencoder architecture, for unsupervised representation learning.
The method adds a split to the network, resulting in two disjoint sub-networks.
Each sub-network is trained to perform a difficult task -- predicting one
subset of t... | computer science |
26,827 | Weakly-supervised Discriminative Patch Learning via CNN for Fine-grained
Recognition | cs.CV | Research on fine-grained recognition has recently shifted from multistage
frameworks to convolutional neural networks (CNN) that are trained end-to-end.
Many previous end-to-end deep approaches typically consist of a recognition
network and an auxiliary localization network trained with additional part
annotations to d... | computer science |
26,828 | Efficient Likelihood Bayesian Constrained Local Model | cs.CV | The constrained local model (CLM) proposes a paradigm that the locations of a
set of local landmark detectors are constrained to lie in a subspace, spanned
by a shape point distribution model (PDM). Fitting the model to an object
involves two steps. A response map, which represents the likelihood of the
location of a l... | computer science |
26,829 | Attend in groups: a weakly-supervised deep learning framework for
learning from web data | cs.CV | Large-scale datasets have driven the rapid development of deep neural
networks for visual recognition. However, annotating a massive dataset is
expensive and time-consuming. Web images and their labels are, in comparison,
much easier to obtain, but direct training on such automatically harvested
images can lead to unsa... | computer science |
26,830 | Semantic Facial Expression Editing using Autoencoded Flow | cs.CV | High-level manipulation of facial expressions in images --- such as changing
a smile to a neutral expression --- is challenging because facial expression
changes are highly non-linear, and vary depending on the appearance of the
face. We present a fully automatic approach to editing faces that combines the
advantages o... | computer science |
26,831 | Sequential Person Recognition in Photo Albums with a Recurrent Network | cs.CV | Recognizing the identities of people in everyday photos is still a very
challenging problem for machine vision, due to non-frontal faces, changes in
clothing, location, lighting and similar. Recent studies have shown that rich
relational information between people in the same photo can help in recognizing
their identit... | computer science |
26,832 | High-Resolution Image Inpainting using Multi-Scale Neural Patch
Synthesis | cs.CV | Recent advances in deep learning have shown exciting promise in filling large
holes in natural images with semantically plausible and context aware details,
impacting fundamental image manipulation tasks such as object removal. While
these learning-based methods are significantly more effective in capturing
high-level ... | computer science |
26,833 | Modeling Relationships in Referential Expressions with Compositional
Modular Networks | cs.CV | People often refer to entities in an image in terms of their relationships
with other entities. For example, "the black cat sitting under the table"
refers to both a "black cat" entity and its relationship with another "table"
entity. Understanding these relationships is essential for interpreting and
grounding such na... | computer science |
26,834 | Deep Cuboid Detection: Beyond 2D Bounding Boxes | cs.CV | We present a Deep Cuboid Detector which takes a consumer-quality RGB image of
a cluttered scene and localizes all 3D cuboids (box-like objects). Contrary to
classical approaches which fit a 3D model from low-level cues like corners,
edges, and vanishing points, we propose an end-to-end deep learning system to
detect cu... | computer science |
26,835 | Speed/accuracy trade-offs for modern convolutional object detectors | cs.CV | The goal of this paper is to serve as a guide for selecting a detection
architecture that achieves the right speed/memory/accuracy balance for a given
application and platform. To this end, we investigate various ways to trade
accuracy for speed and memory usage in modern convolutional object detection
systems. A numbe... | computer science |
26,836 | Wider or Deeper: Revisiting the ResNet Model for Visual Recognition | cs.CV | The trend towards increasingly deep neural networks has been driven by a
general observation that increasing depth increases the performance of a
network. Recently, however, evidence has been amassing that simply increasing
depth may not be the best way to increase performance, particularly given other
limitations. Inv... | computer science |
26,837 | User Dependent Features in Online Signature Verification | cs.CV | In this paper, we propose a novel approach for verification of on-line
signatures based on user dependent feature selection and symbolic
representation. Unlike other signature verification methods, which work with
same features for all users, the proposed approach introduces the concept of
user dependent features. It e... | computer science |
26,838 | Combining Data-driven and Model-driven Methods for Robust Facial
Landmark Detection | cs.CV | Facial landmark detection is an important yet challenging task for real-world
computer vision applications. This paper proposes an effective and robust
approach for facial landmark detection by combining data- and model-driven
methods. Firstly, a Fully Convolutional Network (FCN) is trained to compute
response maps of ... | computer science |
26,839 | POSEidon: Face-from-Depth for Driver Pose Estimation | cs.CV | Fast and accurate upper-body and head pose estimation is a key task for
automatic monitoring of driver attention, a challenging context characterized
by severe illumination changes, occlusions and extreme poses. In this work, we
present a new deep learning framework for head localization and pose estimation
on depth im... | computer science |
26,840 | End-to-End Training of Hybrid CNN-CRF Models for Stereo | cs.CV | We propose a novel and principled hybrid CNN+CRF model for stereo estimation.
Our model allows to exploit the advantages of both, convolutional neural
networks (CNNs) and conditional random fields (CRFs) in an unified approach.
The CNNs compute expressive features for matching and distinctive color edges,
which in turn... | computer science |
26,841 | Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive
Architectures | cs.CV | This paper introduces a novel approach for generating videos called
Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also
perform text-to-video generation which, to the best of our knowledge, makes it
the first approach of its kind. It combines a Variational Autoencoder~(VAE)
with a Recurrent Att... | computer science |
26,842 | An Artificial Agent for Robust Image Registration | cs.CV | 3-D image registration, which involves aligning two or more images, is a
critical step in a variety of medical applications from diagnosis to therapy.
Image registration is commonly performed by optimizing an image matching metric
as a cost function. However, this task is challenging due to the non-convex
nature of the... | computer science |
26,843 | Super-Resolution Reconstruction of Electrical Impedance Tomography
Images | cs.CV | Electrical Impedance Tomography (EIT) systems are becoming popular because
they present several advantages over competing systems. However, EIT leads to
images with very low resolution. Moreover, the nonuniform sampling
characteristic of EIT precludes the straightforward application of traditional
image ruper-resolutio... | computer science |
26,844 | p-DLA: A Predictive System Model for Onshore Oil and Gas Pipeline
Dataset Classification and Monitoring - Part 1 | cs.CV | With the rise in militant activity and rogue behaviour in oil and gas regions
around the world, oil pipeline disturbances is on the increase leading to huge
losses to multinational operators and the countries where such facilities
exist. However, this situation can be averted if adequate predictive monitoring
schemes a... | computer science |
26,845 | EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras
(Extended Abstract) | cs.CV | Marker-based and marker-less optical skeletal motion-capture methods use an
outside-in arrangement of cameras placed around a scene, with viewpoints
converging on the center. They often create discomfort by possibly needed
marker suits, and their recording volume is severely restricted and often
constrained to indoor s... | computer science |
26,846 | Improved Stereo Matching with Constant Highway Networks and Reflective
Confidence Learning | cs.CV | We present an improved three-step pipeline for the stereo matching problem
and introduce multiple novelties at each stage. We propose a new highway
network architecture for computing the matching cost at each possible
disparity, based on multilevel weighted residual shortcuts, trained with a
hybrid loss that supports m... | computer science |
26,847 | Video-based Person Re-identification with Accumulative Motion Context | cs.CV | Video based person re-identification plays a central role in realistic
security and video surveillance. In this paper we propose a novel Accumulative
Motion Context (AMOC) network for addressing this important problem, which
effectively exploits the long-range motion context for robustly identifying the
same person und... | computer science |
26,848 | Lifting from the Deep: Convolutional 3D Pose Estimation from a Single
Image | cs.CV | We propose a unified formulation for the problem of 3D human pose estimation
from a single raw RGB image that reasons jointly about 2D joint estimation and
3D pose reconstruction to improve both tasks. We take an integrated approach
that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN
architecture... | computer science |
26,849 | Weakly Supervised Semantic Segmentation using Web-Crawled Videos | cs.CV | We propose a novel algorithm for weakly supervised semantic segmentation
based on image-level class labels only. In weakly supervised setting, it is
commonly observed that trained model overly focuses on discriminative parts
rather than the entire object area. Our goal is to overcome this limitation
with no additional ... | computer science |
26,850 | Adversarially Tuned Scene Generation | cs.CV | Generalization performance of trained computer vision systems that use
computer graphics (CG) generated data is not yet effective due to the concept
of 'domain-shift' between virtual and real data. Although simulated data
augmented with a few real world samples has been shown to mitigate domain shift
and improve transf... | computer science |
26,851 | Retrieving Similar X-Ray Images from Big Image Data Using Radon Barcodes
with Single Projections | cs.CV | The idea of Radon barcodes (RBC) has been introduced recently. In this paper,
we propose a content-based image retrieval approach for big datasets based on
Radon barcodes. Our method (Single Projection Radon Barcode, or SP-RBC) uses
only a few Radon single projections for each image as global features that can
serve as... | computer science |
26,852 | Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation | cs.CV | Visual tracking is a fundamental problem in computer vision. Recently, some
deep-learning-based tracking algorithms have been achieving record-breaking
performances. However, due to the high complexity of deep learning, most deep
trackers suffer from low tracking speed, and thus are impractical in many
real-world appli... | computer science |
26,853 | Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel
Density Estimation in the Product Space | cs.CV | Many algorithms for the computation of correspondences between deformable
shapes rely on some variant of nearest neighbor matching in a descriptor space.
Such are, for example, various point-wise correspondence recovery algorithms
used as a post-processing stage in the functional correspondence framework.
Such frequent... | computer science |
26,854 | Image denoising using group sparsity residual and external nonlocal
self-similarity prior | cs.CV | Nonlocal image representation has been successfully used in many
image-related inverse problems including denoising, deblurring and deblocking.
However, a majority of reconstruction methods only exploit the nonlocal
self-similarity (NSS) prior of the degraded observation image, it is very
challenging to reconstruct the... | computer science |
26,855 | Constrained Deep Weak Supervision for Histopathology Image Segmentation | cs.CV | In this paper, we develop a new weakly-supervised learning algorithm to learn
to segment cancerous regions in histopathology images. Our work is under a
multiple instance learning framework (MIL) with a new formulation, deep weak
supervision (DWS); we also propose an effective way to introduce constraints to
our neural... | computer science |
26,856 | Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures
Via Semantic Representation | cs.CV | This paper proposes a new hyperspectral unmixing method for nonlinearly mixed
hyperspectral data using a semantic representation in a semi-supervised
fashion, assuming the availability of a spectral reference library. Existing
semi-supervised unmixing algorithms select members from an endmember library
that are present... | computer science |
26,857 | Learning a Mixture of Deep Networks for Single Image Super-Resolution | cs.CV | Single image super-resolution (SR) is an ill-posed problem which aims to
recover high-resolution (HR) images from their low-resolution (LR)
observations. The crux of this problem lies in learning the complex mapping
between low-resolution patches and the corresponding high-resolution patches.
Prior arts have used eithe... | computer science |
26,858 | A Hierarchical Image Matting Model for Blood Vessel Segmentation in
Fundus images | cs.CV | In this paper, a hierarchical image matting model is proposed to extract
blood vessels from fundus images. More specifically, a hierarchical strategy
utilizing the continuity and extendibility of retinal blood vessels is
integrated into the image matting model for blood vessel segmentation. Normally
the matting models ... | computer science |
26,859 | A Concave Optimization Algorithm for Matching Partially Overlapping
Point Sets | cs.CV | Point matching refers to the process of finding spatial transformation and
correspondences between two sets of points. In this paper, we focus on the case
that there is only partial overlap between two point sets. Following the
approach of the robust point matching method, we model point matching as a
mixed linear assi... | computer science |
26,860 | An Evaluation Framework and Database for MoCap-Based Gait Recognition
Methods | cs.CV | As a contribution to reproducible research, this paper presents a framework
and a database to improve the development, evaluation and comparison of methods
for gait recognition from motion capture (MoCap) data. The evaluation framework
provides implementation details and source codes of state-of-the-art
human-interpret... | computer science |
26,861 | Path-following based Point Matching using Similarity Transformation | cs.CV | To address the problem of 3D point matching where the poses of two point sets
are unknown, we adapt a recently proposed path following based method to use
similarity transformation instead of the original affine transformation. The
reduced number of transformation parameters leads to more constrained and
desirable matc... | computer science |
26,862 | Transforming Sensor Data to the Image Domain for Deep Learning - an
Application to Footstep Detection | cs.CV | Convolutional Neural Networks (CNNs) have become the state-of-the-art in
various computer vision tasks, but they are still premature for most sensor
data, especially in pervasive and wearable computing. A major reason for this
is the limited amount of annotated training data. In this paper, we propose the
idea of lever... | computer science |
26,863 | SalGAN: Visual Saliency Prediction with Generative Adversarial Networks | cs.CV | We introduce SalGAN, a deep convolutional neural network for visual saliency
prediction trained with adversarial examples. The first stage of the network
consists of a generator model whose weights are learned by back-propagation
computed from a binary cross entropy (BCE) loss over downsampled versions of
the saliency ... | computer science |
26,864 | The Dem@Care Experiments and Datasets: a Technical Report | cs.CV | The objective of Dem@Care is the development of a complete system providing
personal health services to people with dementia, as well as medical
professionals and caregivers, by using a multitude of sensors, for
context-aware, multi-parametric monitoring of lifestyle, ambient environment,
and health parameters. Multi-s... | computer science |
26,865 | Learning from Synthetic Humans | cs.CV | Estimating human pose, shape, and motion from images and videos are
fundamental challenges with many applications. Recent advances in 2D human pose
estimation use large amounts of manually-labeled training data for learning
convolutional neural networks (CNNs). Such data is time consuming to acquire
and difficult to ex... | computer science |
26,866 | Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset
and Comparative Study | cs.CV | Automatic photo cropping is an important tool for improving visual quality of
digital photos without resorting to tedious manual selection. Traditionally,
photo cropping is accomplished by determining the best proposal window through
visual quality assessment or saliency detection. In essence, the performance of
an ima... | computer science |
26,867 | Motion Deblurring in the Wild | cs.CV | The task of image deblurring is a very ill-posed problem as both the image
and the blur are unknown. Moreover, when pictures are taken in the wild, this
task becomes even more challenging due to the blur varying spatially and the
occlusions between the object. Due to the complexity of the general image model
we propose... | computer science |
26,868 | Abnormal Event Detection in Videos using Spatiotemporal Autoencoder | cs.CV | We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We ... | computer science |
26,869 | Distinguishing Posed and Spontaneous Smiles by Facial Dynamics | cs.CV | Smile is one of the key elements in identifying emotions and present state of
mind of an individual. In this work, we propose a cluster of approaches to
classify posed and spontaneous smiles using deep convolutional neural network
(CNN) face features, local phase quantization (LPQ), dense optical flow and
histogram of ... | computer science |
26,870 | Learning From Noisy Large-Scale Datasets With Minimal Supervision | cs.CV | We present an approach to effectively use millions of images with noisy
annotations in conjunction with a small subset of cleanly-annotated images to
learn powerful image representations. One common approach to combine clean and
noisy data is to first pre-train a network using the large noisy dataset and
then fine-tune... | computer science |
26,871 | Deep Convolutional Denoising of Low-Light Images | cs.CV | Poisson distribution is used for modeling noise in photon-limited imaging.
While canonical examples include relatively exotic types of sensing like
spectral imaging or astronomy, the problem is relevant to regular photography
now more than ever due to the booming market for mobile cameras. Restricted
form factor limits... | computer science |
26,872 | To Boost or Not to Boost? On the Limits of Boosted Trees for Object
Detection | cs.CV | We aim to study the modeling limitations of the commonly employed boosted
decision trees classifier. Inspired by the success of large, data-hungry visual
recognition models (e.g. deep convolutional neural networks), this paper
focuses on the relationship between modeling capacity of the weak learners,
dataset size, and... | computer science |
26,873 | Deep Class Aware Denoising | cs.CV | The increasing demand for high image quality in mobile devices brings forth
the need for better computational enhancement techniques, and image denoising
in particular. At the same time, the images captured by these devices can be
categorized into a small set of semantic classes. However simple, this
observation has no... | computer science |
26,874 | Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion
Maps | cs.CV | A map-guided superpixel segmentation method for hyperspectral imagery is
developed and introduced. The proposed approach develops a
hyperspectral-appropriate version of the SLIC superpixel segmentation
algorithm, leverages map information to guide segmentation, and incorporates
the semi-supervised Partial Membership La... | computer science |
26,875 | Towards Accurate Multi-person Pose Estimation in the Wild | cs.CV | We propose a method for multi-person detection and 2-D pose estimation that
achieves state-of-art results on the challenging COCO keypoints task. It is a
simple, yet powerful, top-down approach consisting of two stages.
In the first stage, we predict the location and scale of boxes which are
likely to contain people;... | computer science |
26,876 | Large-scale Isolated Gesture Recognition Using Convolutional Neural
Networks | cs.CV | This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI). These dynamic images are constructed from a sequence of depth maps
using bidirectional ra... | computer science |
26,877 | Unsupervised Learning of Long-Term Motion Dynamics for Videos | cs.CV | We present an unsupervised representation learning approach that compactly
encodes the motion dependencies in videos. Given a pair of images from a video
clip, our framework learns to predict the long-term 3D motions. To reduce the
complexity of the learning framework, we propose to describe the motion as a
sequence of... | computer science |
26,878 | Oriented Response Networks | cs.CV | Deep Convolution Neural Networks (DCNNs) are capable of learning
unprecedentedly effective image representations. However, their ability in
handling significant local and global image rotations remains limited. In this
paper, we propose Active Rotating Filters (ARFs) that actively rotate during
convolution and produce ... | computer science |
26,879 | Sign Language Recognition Using Temporal Classification | cs.CV | Devices like the Myo armband available in the market today enable us to
collect data about the position of a user's hands and fingers over time. We can
use these technologies for sign language translation since each sign is roughly
a combination of gestures across time. In this work, we utilize a dataset
collected by a... | computer science |
26,880 | DeepFace: Face Generation using Deep Learning | cs.CV | We use CNNs to build a system that both classifies images of faces based on a
variety of different facial attributes and generates new faces given a set of
desired facial characteristics. After introducing the problem and providing
context in the first section, we discuss recent work related to image
generation in Sect... | computer science |
26,881 | Greedy Search for Descriptive Spatial Face Features | cs.CV | Facial expression recognition methods use a combination of geometric and
appearance-based features. Spatial features are derived from displacements of
facial landmarks, and carry geometric information. These features are either
selected based on prior knowledge, or dimension-reduced from a large pool. In
this study, we... | computer science |
26,882 | Group Visual Sentiment Analysis | cs.CV | In this paper, we introduce a framework for classifying images according to
high-level sentiment. We subdivide the task into three primary problems:
emotion classification on faces, human pose estimation, and 3D estimation and
clustering of groups of people. We introduce novel algorithms for matching body
parts to a co... | computer science |
26,883 | Urban Scene Segmentation with Laser-Constrained CRFs | cs.CV | Robots typically possess sensors of different modalities, such as colour
cameras, inertial measurement units, and 3D laser scanners. Often, solving a
particular problem becomes easier when more than one modality is used. However,
while there are undeniable benefits to combine sensors of different modalities
the process... | computer science |
26,884 | Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term
Dependencies | cs.CV | The majority of existing solutions to the Multi-Target Tracking (MTT) problem
do not combine cues in a coherent end-to-end fashion over a long period of
time. However, we present an online method that encodes long-term temporal
dependencies across multiple cues. One key challenge of tracking methods is to
accurately tr... | computer science |
26,885 | Random Sampling for Fast Face Sketch Synthesis | cs.CV | Exemplar-based face sketch synthesis plays an important role in both digital
entertainment and law enforcement. It generally consists of two parts: neighbor
selection and reconstruction weight representation. The most time-consuming or
main computation complexity for exemplar-based face sketch synthesis methods
lies in... | computer science |
26,886 | On Classification of Distorted Images with Deep Convolutional Neural
Networks | cs.CV | Image blur and image noise are common distortions during image acquisition.
In this paper, we systematically study the effect of image distortions on the
deep neural network (DNN) image classifiers. First, we examine the DNN
classifier performance under four types of distortions. Second, we propose two
approaches to al... | computer science |
26,887 | Stage 4 validation of the Satellite Image Automatic Mapper lightweight
computer program for Earth observation Level 2 product generation, Part 1
Theory | cs.CV | The European Space Agency (ESA) defines an Earth Observation (EO) Level 2
product as a multispectral (MS) image corrected for geometric, atmospheric,
adjacency and topographic effects, stacked with its scene classification map
(SCM), whose legend includes quality layers such as cloud and cloud-shadow. No
ESA EO Level 2... | computer science |
26,888 | Stage 4 validation of the Satellite Image Automatic Mapper lightweight
computer program for Earth observation Level 2 product generation, Part 2
Validation | cs.CV | The European Space Agency (ESA) defines an Earth Observation (EO) Level 2
product as a multispectral (MS) image corrected for geometric, atmospheric,
adjacency and topographic effects, stacked with its scene classification map
(SCM) whose legend includes quality layers such as cloud and cloud-shadow. No
ESA EO Level 2 ... | computer science |
26,889 | Automated Linear-Time Detection and Quality Assessment of Superpixels in
Uncalibrated True- or False-Color RGB Images | cs.CV | Capable of automated near real time superpixel detection and quality
assessment in an uncalibrated monitor typical red green blue (RGB) image,
depicted in either true or false colors, an original low level computer vision
(CV) lightweight computer program, called RGB Image Automatic Mapper (RGBIAM),
is designed and imp... | computer science |
26,890 | Multi-Objective Software Suite of Two-Dimensional Shape Descriptors for
Object-Based Image Analysis | cs.CV | In recent years two sets of planar (2D) shape attributes, provided with an
intuitive physical meaning, were proposed to the remote sensing community by,
respectively, Nagao & Matsuyama and Shackelford & Davis in their seminal works
on the increasingly popular geographic object based image analysis (GEOBIA)
paradigm. Th... | computer science |
26,891 | Multi-spectral Image Panchromatic Sharpening, Outcome and Process
Quality Assessment Protocol | cs.CV | Multispectral (MS) image panchromatic (PAN) sharpening algorithms proposed to
the remote sensing community are ever increasing in number and variety. Their
aim is to sharpen a coarse spatial resolution MS image with a fine spatial
resolution PAN image acquired simultaneously by a spaceborne or airborne Earth
observatio... | computer science |
26,892 | MS and PAN image fusion by combining Brovey and wavelet methods | cs.CV | Among the existing fusion algorithms, the wavelet fusion method is the most
frequently discussed one in recent publications because the wavelet approach
preserves the spectral characteristics of the multispectral image better than
other methods. The Brovey is also a popular fusion method used for its ability
in preserv... | computer science |
26,893 | Improved Texture Networks: Maximizing Quality and Diversity in
Feed-forward Stylization and Texture Synthesis | cs.CV | The recent work of Gatys et al., who characterized the style of an image by
the statistics of convolutional neural network filters, ignited a renewed
interest in the texture generation and image stylization problems. While their
image generation technique uses a slow optimization process, recently several
authors have ... | computer science |
26,894 | Discrete approximations of the affine Gaussian derivative model for
visual receptive fields | cs.CV | The affine Gaussian derivative model can in several respects be regarded as a
canonical model for receptive fields over a spatial image domain: (i) it can be
derived by necessity from scale-space axioms that reflect structural properties
of the world, (ii) it constitutes an excellent model for the receptive fields
of s... | computer science |
26,895 | A Learning-based Variable Size Part Extraction Architecture for 6D
Object Pose Recovery in Depth | cs.CV | State-of-the-art techniques for 6D object pose recovery depend on
occlusion-free point clouds to accurately register objects in 3D space. To deal
with this shortcoming, we introduce a novel architecture called Iterative Hough
Forest with Histogram of Control Points that is capable of estimating the 6D
pose of occluded ... | computer science |
26,896 | Multiple Instance Hybrid Estimator for Learning Target Signatures | cs.CV | Signature-based detectors for hyperspectral target detection rely on knowing
the specific target signature in advance. However, target signature are often
difficult or impossible to obtain. Furthermore, common methods for obtaining
target signatures, such as from laboratory measurements or manual selection
from an imag... | computer science |
26,897 | Visual Multiple-Object Tracking for Unknown Clutter Rate | cs.CV | In multi-object tracking applications, model parameter tuning is a
prerequisite for reliable performance. In particular, it is difficult to know
statistics of false measurements due to various sensing conditions and changes
in the field of views. In this paper we are interested in designing a
multi-object tracking algo... | computer science |
26,898 | MonoCap: Monocular Human Motion Capture using a CNN Coupled with a
Geometric Prior | cs.CV | Recovering 3D full-body human pose is a challenging problem with many
applications. It has been successfully addressed by motion capture systems with
body worn markers and multiple cameras. In this paper, we address the more
challenging case of not only using a single camera but also not leveraging
markers: going direc... | computer science |
26,899 | Visualizing Residual Networks | cs.CV | Residual networks are the current state of the art on ImageNet. Similar work
in the direction of utilizing shortcut connections has been done extremely
recently with derivatives of residual networks and with highway networks. This
work potentially challenges our understanding that CNNs learn layers of local
features th... | computer science |
26,900 | Scene Graph Generation by Iterative Message Passing | cs.CV | Understanding a visual scene goes beyond recognizing individual objects in
isolation. Relationships between objects also constitute rich semantic
information about the scene. In this work, we explicitly model the objects and
their relationships using scene graphs, a visually-grounded graphical structure
of an image. We... | computer science |
26,901 | Unite the People: Closing the Loop Between 3D and 2D Human
Representations | cs.CV | 3D models provide a common ground for different representations of human
bodies. In turn, robust 2D estimation has proven to be a powerful tool to
obtain 3D fits "in-the- wild". However, depending on the level of detail, it
can be hard to impossible to acquire labeled data for training 2D estimators on
large scale. We ... | computer science |
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