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30,002 | Convolutional neural networks pretrained on large face recognition
datasets for emotion classification from video | cs.CV | In this paper we describe a solution to our entry for the emotion recognition
challenge EmotiW 2017. We propose an ensemble of several models, which capture
spatial and audio features from videos. Spatial features are captured by
convolutional neural networks, pretrained on large face recognition datasets.
We show that... | computer science |
30,003 | UCT: Learning Unified Convolutional Networks for Real-time Visual
Tracking | cs.CV | Convolutional neural networks (CNN) based tracking approaches have shown
favorable performance in recent benchmarks. Nonetheless, the chosen CNN
features are always pre-trained in different task and individual components in
tracking systems are learned separately, thus the achieved tracking performance
may be suboptima... | computer science |
30,004 | Denoising Imaging Polarimetry by an Adapted BM3D Method | cs.CV | Imaging polarimetry allows more information to be extracted from a scene than
conventional intensity or colour imaging. However, a major challenge of imaging
polarimetry is image degradation due to noise. This paper investigates the
mitigation of noise through denoising algorithms and compares existing
denoising algori... | computer science |
30,005 | 3D Shape Classification Using Collaborative Representation based
Projections | cs.CV | A novel 3D shape classification scheme, based on collaborative representation
learning, is investigated in this work. A data-driven feature-extraction
procedure, taking the form of a simple projection operator, is in the core of
our methodology. Provided a shape database, a graph encapsulating the
structural relationsh... | computer science |
30,006 | A Multiple Radar Approach for Automatic Target Recognition of Aircraft
using Inverse Synthetic Aperture Radar | cs.CV | Along with the improvement of radar technologies, Automatic Target
Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR)
has come to be an active research area. SAR/ISAR are radar techniques to
generate a two-dimensional high-resolution image of a target. Unlike other
similar experiments using C... | computer science |
30,007 | Capturing Localized Image Artifacts through a CNN-based Hyper-image
Representation | cs.CV | Training deep CNNs to capture localized image artifacts on a relatively small
dataset is a challenging task. With enough images at hand, one can hope that a
deep CNN characterizes localized artifacts over the entire data and their
effect on the output. However, on smaller datasets, such deep CNNs may overfit
and shallo... | computer science |
30,008 | Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants | cs.CV | The increase in awareness of people towards their nutritional habits has
drawn considerable attention to the field of automatic food analysis. Focusing
on self-service restaurants environment, automatic food analysis is not only
useful for extracting nutritional information from foods selected by customers,
it is also ... | computer science |
30,009 | XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings | cs.CV | Style transfer usually refers to the task of applying color and texture
information from a specific style image to a given content image while
preserving the structure of the latter. Here we tackle the more generic problem
of semantic style transfer: given two unpaired collections of images, we aim to
learn a mapping b... | computer science |
30,010 | Robust Keyframe-based Dense SLAM with an RGB-D Camera | cs.CV | In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM
approach for an RGB-D camera that can robustly handle fast motion and dense
loop closure, and run without time limitation in a moderate size scene. It not
only can be used to scan high-quality 3D models, but also can satisfy the
demand of VR and AR ... | computer science |
30,011 | Conditional Autoencoders with Adversarial Information Factorization | cs.CV | Generative models, such as variational auto-encoders (VAE) and generative
adversarial networks (GAN), have been immensely successful in approximating
image statistics in computer vision. VAEs are useful for unsupervised feature
learning, while GANs alleviate supervision by penalizing inaccurate samples
using an adversa... | computer science |
30,012 | Dynamic Zoom-in Network for Fast Object Detection in Large Images | cs.CV | We introduce a generic framework that reduces the computational cost of
object detection while retaining accuracy for scenarios where objects with
varied sizes appear in high resolution images. Detection progresses in a
coarse-to-fine manner, first on a down-sampled version of the image and then on
a sequence of higher... | computer science |
30,013 | Interpretable R-CNN | cs.CV | This paper presents a method of learning qualitatively interpretable models
in object detection using popular two-stage region-based ConvNet detection
systems (i.e., R-CNN). R-CNN consists of a region proposal network and a RoI
(Region-of-Interest) prediction network.By interpretable models, we focus on
weakly-supervis... | computer science |
30,014 | C-WSL: Count-guided Weakly Supervised Localization | cs.CV | We introduce a count-guided weakly supervised localization (C-WSL) framework
with per-class object count as an additional form of image-level supervision to
improve weakly supervised localization (WSL). C-WSL uses a simple count-based
region selection algorithm to select high quality regions, each of which covers
a sin... | computer science |
30,015 | A Novel SDASS Descriptor for Fully Encoding the Information of 3D Local
Surface | cs.CV | Local feature description is a fundamental yet challenging task in 3D
computer vision. This paper proposes a novel descriptor, named Statistic of
Deviation Angles on Subdivided Space (SDASS), for comprehensive encoding
geometrical and spatial in-formation of local surface on Local Reference Axis
(LRA). The SDASS descri... | computer science |
30,016 | Deep Epitome for Unravelling Generalized Hamming Network: A Fuzzy Logic
Interpretation of Deep Learning | cs.CV | This paper gives a rigorous analysis of trained Generalized Hamming
Networks(GHN) proposed by Fan (2017) and discloses an interesting finding about
GHNs, i.e., stacked convolution layers in a GHN is equivalent to a single yet
wide convolution layer. The revealed equivalence, on the theoretical side, can
be regarded as ... | computer science |
30,017 | DNA-GAN: Learning Disentangled Representations from Multi-Attribute
Images | cs.CV | Disentangling factors of variation has always been a challenging problem in
representation learning. Existing algorithms suffer from many limitations, such
as unpredictable disentangling factors, bad quality of generated images from
encodings, lack of identity information, etc. In this paper, we propose a
supervised al... | computer science |
30,018 | Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single
Image Super-Resolution | cs.CV | With exploiting contextual information over large image regions in an
efficient way, the deep convolutional neural network has shown an impressive
performance for single image super-resolution (SR). In this paper, we propose a
deep convolutional network by cascading the well-designed inception-residual
blocks within th... | computer science |
30,019 | A Public Image Database for Benchmark of Plant Seedling Classification
Algorithms | cs.CV | A database of images of approximately 960 unique plants belonging to 12
species at several growth stages is made publicly available. It comprises
annotated RGB images with a physical resolution of roughly 10 pixels per mm. To
standardise the evaluation of classification results obtained with the
database, a benchmark b... | computer science |
30,020 | On the Utility of Context (or the Lack Thereof) for Object Detection | cs.CV | The recurring context in which objects appear holds valuable information that
can be employed to predict their existence. This intuitive observation indeed
led many researchers to endow appearance-based detectors with explicit
reasoning about context. The underlying thesis suggests that with stronger
contextual relatio... | computer science |
30,021 | Squeeze-SegNet: A new fast Deep Convolutional Neural Network for
Semantic Segmentation | cs.CV | The recent researches in Deep Convolutional Neural Network have focused their
attention on improving accuracy that provide significant advances. However, if
they were limited to classification tasks, nowadays with contributions from
Scientific Communities who are embarking in this field, they have become very
useful in... | computer science |
30,022 | Convolutional Neural Networks and Data Augmentation for Spectral-Spatial
Classification of Hyperspectral Images | cs.CV | Spectral-spatial classification of remotely sensed hyperspectral images has
been the subject of many studies in recent years. Current methods achieve
excellent performance on benchmark hyperspectral image labeling tasks when a
sufficient number of labeled pixels is available. However, in the presence of
only very few l... | computer science |
30,023 | A Correlation Based Feature Representation for First-Person Activity
Recognition | cs.CV | In this paper, a simple yet efficient activity recognition method for
first-person video is introduced. The proposed method is appropriate for
representation of high-dimensional features such as those extracted from
convolutional neural networks (CNNs). The per-frame (per-segment) extracted
features are considered as a... | computer science |
30,024 | People, Penguins and Petri Dishes: Adapting Object Counting Models To
New Visual Domains And Object Types Without Forgetting | cs.CV | In this paper we propose a technique to adapt a convolutional neural network
(CNN) based object counter to additional visual domains and object types while
still preserving the original counting function. Domain-specific normalisation
and scaling operators are trained to allow the model to adjust to the
statistical dis... | computer science |
30,025 | Interpreting Deep Visual Representations via Network Dissection | cs.CV | The success of recent deep convolutional neural networks (CNNs) depends on
learning hidden representations that can summarize the important factors of
variation behind the data. However, CNNs often criticized as being black boxes
that lack interpretability, since they have millions of unexplained model
parameters. In t... | computer science |
30,026 | Brain Extraction from Normal and Pathological Images: A Joint
PCA/Image-Reconstruction Approach | cs.CV | Brain extraction from 3D medical images is a common pre-processing step. A
variety of approaches exist, but they are frequently only designed to perform
brain extraction from images without strong pathologies. Extracting the brain
from images exhibiting strong pathologies, for example, the presence of a brain
tumor or ... | computer science |
30,027 | Contextual Object Detection with a Few Relevant Neighbors | cs.CV | A natural way to improve the detection of objects is to consider the
contextual constraints imposed by the detection of additional objects in a
given scene. In this work, we exploit the spatial relations between objects in
order to improve detection capacity, as well as analyze various properties of
the contextual obje... | computer science |
30,028 | PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning | cs.CV | This paper presents a method for adding multiple tasks to a single deep
neural network while avoiding catastrophic forgetting. Inspired by network
pruning techniques, we exploit redundancies in large deep networks to free up
parameters that can then be employed to learn new tasks. By performing
iterative pruning and ne... | computer science |
30,029 | End-to-end Training for Whole Image Breast Cancer Diagnosis using An All
Convolutional Design | cs.CV | We develop an end-to-end training algorithm for whole-image breast cancer
diagnosis based on mammograms. It requires lesion annotations only at the first
stage of training. After that, a whole image classifier can be trained using
only image level labels. This greatly reduced the reliance on lesion
annotations. Our app... | computer science |
30,030 | AOGNets: Deep AND-OR Grammar Networks for Visual Recognition | cs.CV | This paper presents a method of learning deep AND-OR Grammar (AOG) networks
for visual recognition, which we term AOGNets. An AOGNet consists of a number
of stages each of which is composed of a number of AOG building blocks. An AOG
building block is designed based on a principled AND-OR grammar and represented
by a hi... | computer science |
30,031 | Modal Regression based Atomic Representation for Robust Face Recognition | cs.CV | Representation based classification (RC) methods such as sparse RC (SRC) have
shown great potential in face recognition in recent years. Most previous RC
methods are based on the conventional regression models, such as lasso
regression, ridge regression or group lasso regression. These regression models
essentially imp... | computer science |
30,032 | Real-Time Document Image Classification using Deep CNN and Extreme
Learning Machines | cs.CV | This paper presents an approach for real-time training and testing for
document image classification. In production environments, it is crucial to
perform accurate and (time-)efficient training. Existing deep learning
approaches for classifying documents do not meet these requirements, as they
require much time for tra... | computer science |
30,033 | Occlusion Aware Unsupervised Learning of Optical Flow | cs.CV | It has been recently shown that a convolutional neural network can learn
optical flow estimation with unsupervised learning. However, the performance of
the unsupervised methods still has a relatively large gap compared to its
supervised counterpart. Occlusion and large motion are some of the major
factors that limit t... | computer science |
30,034 | NISP: Pruning Networks using Neuron Importance Score Propagation | cs.CV | To reduce the significant redundancy in deep Convolutional Neural Networks
(CNNs), most existing methods prune neurons by only considering statistics of
an individual layer or two consecutive layers (e.g., prune one layer to
minimize the reconstruction error of the next layer), ignoring the effect of
error propagation ... | computer science |
30,035 | Learning Deeply Supervised Visual Descriptors for Dense Monocular
Reconstruction | cs.CV | Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on
handcrafted visual features or raw RGB values for establishing correspondences
between images. These features, while suitable for sparse mapping, often lead
to ambiguous matches at texture-less regions when performing dense
reconstruction due... | computer science |
30,036 | Defense against Universal Adversarial Perturbations | cs.CV | Recent advances in Deep Learning show the existence of image-agnostic
quasi-imperceptible perturbations that when applied to `any' image can fool a
state-of-the-art network classifier to change its prediction about the image
label. These `Universal Adversarial Perturbations' pose a serious threat to the
success of Deep... | computer science |
30,037 | Skepxels: Spatio-temporal Image Representation of Human Skeleton Joints
for Action Recognition | cs.CV | Human skeleton joints are popular for action analysis since they can be
easily extracted from videos to discard background noises. However, current
skeleton representations do not fully benefit from machine learning with CNNs.
We propose "Skepxels" a spatio-temporal representation for skeleton sequences
to fully exploi... | computer science |
30,038 | Learning from Millions of 3D Scans for Large-scale 3D Face Recognition | cs.CV | Deep networks trained on millions of facial images are believed to be closely
approaching human-level performance in face recognition. However, open world
face recognition still remains a challenge. Although, 3D face recognition has
an inherent edge over its 2D counterpart, it has not benefited from the recent
developm... | computer science |
30,039 | HandSeg: A Dataset for Hand Segmentation from Depth Images | cs.CV | We introduce a large-scale RGBD hand segmentation dataset, with detailed and
automatically generated high-quality ground-truth annotations. Existing
real-world datasets are limited in quantity due to the difficulty in manually
annotating ground-truth labels. By leveraging a pair of brightly colored gloves
and an RGBD c... | computer science |
30,040 | 3D Face Reconstruction from Light Field Images: A Model-free Approach | cs.CV | Reconstructing 3D facial geometry from a single RGB image has recently
instigated wide research interest. However, it is still an ill-posed problem
and most methods rely on prior models hence undermining the accuracy of the
recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI)
obtained from ligh... | computer science |
30,041 | Zero-Annotation Object Detection with Web Knowledge Transfer | cs.CV | Object detection is one of the major problems in computer vision, and has
been extensively studied. Most of of the existing detection works rely on
labor-intensive supervisions, such as ground truth bounding boxes of objects or
at least image-level annotations. On the contrary, we propose an object
detection method tha... | computer science |
30,042 | Learning to Find Good Correspondences | cs.CV | We develop a deep architecture to learn to find good correspondences for
wide-baseline stereo. Given a set of putative sparse matches and the camera
intrinsics, we train our network in an end-to-end fashion to label the
correspondences as inliers or outliers, while simultaneously using them to
recover the relative pose... | computer science |
30,043 | Superpixel clustering with deep features for unsupervised road
segmentation | cs.CV | Vision-based autonomous driving requires classifying each pixel as
corresponding to road or not, which can be addressed using semantic
segmentation. Semantic segmentation works well when used with a fully
supervised model, but in practice, the required work of creating pixel-wise
annotations is very expensive. Although... | computer science |
30,044 | Parametric Manifold Learning Via Sparse Multidimensional Scaling | cs.CV | We propose a metric-learning framework for computing distance-preserving maps
that generate low-dimensional embeddings for a certain class of manifolds. We
employ Siamese networks to solve the problem of least squares multidimensional
scaling for generating mappings that preserve geodesic distances on the
manifold. In ... | computer science |
30,045 | A Revisit on Deep Hashings for Large-scale Content Based Image Retrieval | cs.CV | There is a growing trend in studying deep hashing methods for content-based
image retrieval (CBIR), where hash functions and binary codes are learnt using
deep convolutional neural networks and then the binary codes can be used to do
approximate nearest neighbor (ANN) search. All the existing deep hashing papers
report... | computer science |
30,046 | Global versus Localized Generative Adversarial Nets | cs.CV | In this paper, we present a novel localized Generative Adversarial Net (GAN)
to learn on the manifold of real data. Compared with the classic GAN that {\em
globally} parameterizes a manifold, the Localized GAN (LGAN) uses local
coordinate charts to parameterize distinct local geometry of how data points
can transform a... | computer science |
30,047 | Learning to Compare: Relation Network for Few-Shot Learning | cs.CV | We present a conceptually simple, flexible, and general framework for
few-shot learning, where a classifier must learn to recognise new classes given
only few examples from each. Our method, called the Relation Network (RN), is
trained end-to-end from scratch. During meta-learning, it learns to learn a
deep distance me... | computer science |
30,048 | Natural Language Guided Visual Relationship Detection | cs.CV | Reasoning about the relationships between object pairs in images is a crucial
task for holistic scene understanding. Most of the existing works treat this
task as a pure visual classification task: each type of relationship or phrase
is classified as a relation category based on the extracted visual features.
However, ... | computer science |
30,049 | Frame Interpolation with Multi-Scale Deep Loss Functions and Generative
Adversarial Networks | cs.CV | Frame interpolation attempts to synthesise intermediate frames given one or
more consecutive video frames. In recent years, deep learning approaches, and
in particular convolutional neural networks, have succeeded at tackling low-
and high-level computer vision problems including frame interpolation. There
are two main... | computer science |
30,050 | Integrated Face Analytics Networks through Cross-Dataset Hybrid Training | cs.CV | Face analytics benefits many multimedia applications. It consists of a number
of tasks, such as facial emotion recognition and face parsing, and most
existing approaches generally treat these tasks independently, which limits
their deployment in real scenarios. In this paper we propose an integrated Face
Analytics Netw... | computer science |
30,051 | The Perception-Distortion Tradeoff | cs.CV | Image restoration algorithms are typically evaluated by some distortion
measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify
perceived perceptual quality. In this paper, we prove mathematically that
distortion and perceptual quality are at odds with each other. Specifically, we
study the optimal... | computer science |
30,052 | Two Birds with One Stone: Transforming and Generating Facial Images with
Iterative GAN | cs.CV | Generating high fidelity identity-preserving faces with different facial
attributes has a wide range of applications. Although a number of generative
models have been developed to tackle this problem, there is still much room for
further improvement.In paticular, the current solutions usually ignore the
perceptual info... | computer science |
30,053 | Improving Consistency and Correctness of Sequence Inpainting using
Semantically Guided Generative Adversarial Network | cs.CV | Contemporary benchmark methods for image inpainting are based on deep
generative models and specifically leverage adversarial loss for yielding
realistic reconstructions. However, these models cannot be directly applied on
image/video sequences because of an intrinsic drawback- the reconstructions
might be independentl... | computer science |
30,054 | 3D Trajectory Reconstruction of Dynamic Objects Using Planarity
Constraints | cs.CV | We present a method to reconstruct the three-dimensional trajectory of a
moving instance of a known object category in monocular video data. We track
the two-dimensional shape of objects on pixel level exploiting instance-aware
semantic segmentation techniques and optical flow cues. We apply Structure from
Motion techn... | computer science |
30,055 | Zero-Shot Learning via Category-Specific Visual-Semantic Mapping | cs.CV | Zero-Shot Learning (ZSL) aims to classify a test instance from an unseen
category based on the training instances from seen categories, in which the gap
between seen categories and unseen categories is generally bridged via
visual-semantic mapping between the low-level visual feature space and the
intermediate semantic... | computer science |
30,056 | LDMNet: Low Dimensional Manifold Regularized Neural Networks | cs.CV | Deep neural networks have proved very successful on archetypal tasks for
which large training sets are available, but when the training data are scarce,
their performance suffers from overfitting. Many existing methods of reducing
overfitting are data-independent, and their efficacy is often limited when the
training s... | computer science |
30,057 | Grammatical facial expression recognition using customized deep neural
network architecture | cs.CV | This paper proposes to expand the visual understanding capacity of computers
by helping it recognize human sign language more efficiently. This is carried
out through recognition of facial expressions, which accompany the hand signs
used in this language. This paper specially focuses on the popular Brazilian
sign langu... | computer science |
30,058 | Attend and Interact: Higher-Order Object Interactions for Video
Understanding | cs.CV | Human actions often involve complex interactions across several inter-related
objects in the scene. However, existing approaches to fine-grained video
understanding or visual relationship detection often rely on single object
representation or pairwise object relationships. Furthermore, learning
interactions across mul... | computer science |
30,059 | Grounded Objects and Interactions for Video Captioning | cs.CV | We address the problem of video captioning by grounding language generation
on object interactions in the video. Existing work mostly focuses on overall
scene understanding with often limited or no emphasis on object interactions to
address the problem of video understanding. In this paper, we propose
SINet-Caption tha... | computer science |
30,060 | Mobile Video Object Detection with Temporally-Aware Feature Maps | cs.CV | This paper introduces an online model for object detection in videos designed
to run in real-time on low-powered mobile and embedded devices. Our approach
combines fast single-image object detection with convolutional long short term
memory (LSTM) layers to create an interweaved recurrent-convolutional
architecture. Ad... | computer science |
30,061 | Parallel Attention: A Unified Framework for Visual Object Discovery
through Dialogs and Queries | cs.CV | Recognising objects according to a pre-defined fixed set of class labels has
been well studied in the Computer Vision. There are a great many practical
applications where the subjects that may be of interest are not known
beforehand, or so easily delineated, however. In many of these cases natural
language dialog is a ... | computer science |
30,062 | Shape Inpainting using 3D Generative Adversarial Network and Recurrent
Convolutional Networks | cs.CV | Recent advances in convolutional neural networks have shown promising results
in 3D shape completion. But due to GPU memory limitations, these methods can
only produce low-resolution outputs. To inpaint 3D models with semantic
plausibility and contextual details, we introduce a hybrid framework that
combines a 3D Encod... | computer science |
30,063 | Dimensionality Reduction on Grassmannian via Riemannian Optimization: A
Generalized Perspective | cs.CV | This paper proposes a generalized framework with joint normalization which
learns lower-dimensional subspaces with maximum discriminative power by making
use of the Riemannian geometry. In particular, we model the
similarity/dissimilarity between subspaces using various metrics defined on
Grassmannian and formulate dim... | computer science |
30,064 | VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection | cs.CV | Accurate detection of objects in 3D point clouds is a central problem in many
applications, such as autonomous navigation, housekeeping robots, and
augmented/virtual reality. To interface a highly sparse LiDAR point cloud with
a region proposal network (RPN), most existing efforts have focused on
hand-crafted feature r... | computer science |
30,065 | Training a network to attend like human drivers saves it from common but
misleading loss functions | cs.CV | We proposed a novel FCN-ConvLSTM model to predict multi-focal human driver's
attention merely from monocular dash camera videos. Our model has surpassed the
state-of-the-art performance and demonstrated sophisticated behaviors such as
watching out for a driver exiting from a parked car. In addition, we have
demonstrate... | computer science |
30,066 | Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval
with Generative Models | cs.CV | Textual-visual cross-modal retrieval has been a hot research topic in both
computer vision and natural language processing communities. Learning
appropriate representations for multi-modal data is crucial for the cross-modal
retrieval performance. Unlike existing image-text retrieval approaches that
embed image-text pa... | computer science |
30,067 | Vision Based Railway Track Monitoring using Deep Learning | cs.CV | Computer vision based methods have been explored in the past for detection of
railway track defects, but full automation has always been a challenge because
both traditional image processing methods and deep learning classifiers trained
from scratch fail to generalize that well to infinite novel scenarios seen in
the r... | computer science |
30,068 | Action-Attending Graphic Neural Network | cs.CV | The motion analysis of human skeletons is crucial for human action
recognition, which is one of the most active topics in computer vision. In this
paper, we propose a fully end-to-end action-attending graphic neural network
(A$^2$GNN) for skeleton-based action recognition, in which each irregular
skeleton is structured... | computer science |
30,069 | Towards dense volumetric pancreas segmentation in CT using 3D fully
convolutional networks | cs.CV | Pancreas segmentation in computed tomography imaging has been historically
difficult for automated methods because of the large shape and size variations
between patients. In this work, we describe a custom-build 3D fully
convolutional network (FCN) that can process a 3D image including the whole
pancreas and produce a... | computer science |
30,070 | Chinese Typeface Transformation with Hierarchical Adversarial Network | cs.CV | In this paper, we explore automated typeface generation through image style
transfer which has shown great promise in natural image generation. Existing
style transfer methods for natural images generally assume that the source and
target images share similar high-frequency features. However, this assumption
is no long... | computer science |
30,071 | A Fusion-based Gender Recognition Method Using Facial Images | cs.CV | This paper proposes a fusion-based gender recognition method which uses
facial images as input. Firstly, this paper utilizes pre-processing and a
landmark detection method in order to find the important landmarks of faces.
Thereafter, four different frameworks are proposed which are inspired by
state-of-the-art gender ... | computer science |
30,072 | Separating Style and Content for Generalized Style Transfer | cs.CV | Neural style transfer has drawn broad attention in recent years. However,
most existing methods aim to explicitly model the transformation between
different styles, and the learned model is thus not generalizable to new
styles. We here attempt to separate the representations for styles and
contents, and propose a gener... | computer science |
30,073 | Fast Recurrent Fully Convolutional Networks for Direct Perception in
Autonomous Driving | cs.CV | Deep convolutional neural networks (CNNs) have been shown to perform
extremely well at a variety of tasks including subtasks of autonomous driving
such as image segmentation and object classification. However, networks
designed for these tasks typically require vast quantities of training data and
long training periods... | computer science |
30,074 | Grounding Visual Explanations (Extended Abstract) | cs.CV | Existing models which generate textual explanations enforce task relevance
through a discriminative term loss function, but such mechanisms only weakly
constrain mentioned object parts to actually be present in the image. In this
paper, a new model is proposed for generating explanations by utilizing
localized groundin... | computer science |
30,075 | AI Challenger : A Large-scale Dataset for Going Deeper in Image
Understanding | cs.CV | Significant progress has been achieved in Computer Vision by leveraging
large-scale image datasets. However, large-scale datasets for complex Computer
Vision tasks beyond classification are still limited. This paper proposed a
large-scale dataset named AIC (AI Challenger) with three sub-datasets, human
keypoint detecti... | computer science |
30,076 | High-Resolution Deep Convolutional Generative Adversarial Networks | cs.CV | Generative Adversarial Networks (GANs) convergence in a high-resolution
setting with a computational constrain of GPU memory capacity (from 12GB to 24
GB) has been beset with difficulty due to the known lack of convergence rate
stability. In order to boost network convergence of DCGAN (Deep Convolutional
Generative Adv... | computer science |
30,077 | Pseudo-positive regularization for deep person re-identification | cs.CV | An intrinsic challenge of person re-identification (re-ID) is the annotation
difficulty. This typically means 1) few training samples per identity, and 2)
thus the lack of diversity among the training samples. Consequently, we face
high risk of over-fitting when training the convolutional neural network (CNN),
a state-... | computer science |
30,078 | Image Matters: Visually modeling user behaviors using Advanced Model
Server | cs.CV | In Taobao, the largest e-commerce platform in China, billions of items are
provided and typically displayed with their images. For better user experience
and business effectiveness, Click Through Rate (CTR) prediction in online
advertising system exploits abundant user historical behaviors to identify
whether a user is... | computer science |
30,079 | Multi-Label Zero-Shot Learning with Structured Knowledge Graphs | cs.CV | In this paper, we propose a novel deep learning architecture for multi-label
zero-shot learning (ML-ZSL), which is able to predict multiple unseen class
labels for each input instance. Inspired by the way humans utilize semantic
knowledge between objects of interests, we propose a framework that
incorporates knowledge ... | computer science |
30,080 | Learning a Robust Representation via a Deep Network on Symmetric
Positive Definite Manifolds | cs.CV | Recent studies have shown that aggregating convolutional features of a
pre-trained Convolutional Neural Network (CNN) can obtain impressive
performance for a variety of visual tasks. The symmetric Positive Definite
(SPD) matrix becomes a powerful tool due to its remarkable ability to learn an
appropriate statistic repr... | computer science |
30,081 | Efficient Diverse Ensemble for Discriminative Co-Tracking | cs.CV | Ensemble discriminative tracking utilizes a committee of classifiers, to
label data samples, which are in turn, used for retraining the tracker to
localize the target using the collective knowledge of the committee. Committee
members could vary in their features, memory update schemes, or training data,
however, it is ... | computer science |
30,082 | Deep Local Binary Patterns | cs.CV | Local Binary Pattern (LBP) is a traditional descriptor for texture analysis
that gained attention in the last decade. Being robust to several properties
such as invariance to illumination translation and scaling, LBPs achieved
state-of-the-art results in several applications. However, LBPs are not able to
capture high-... | computer science |
30,083 | Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via
Adversarial Training | cs.CV | To realize the full potential of deep learning for medical imaging, large
annotated datasets are required for training. Such datasets are difficult to
acquire because labeled medical images are not usually available due to privacy
issues, lack of experts available for annotation, underrepresentation of rare
conditions ... | computer science |
30,084 | Superpixels Based Segmentation and SVM Based Classification Method to
Distinguish Five Diseases from Normal Regions in Wireless Capsule Endoscopy | cs.CV | Wireless Capsule Endoscopy (WCE) is relatively a new technology to examine
the entire GI trace. During an examination, it captures more than 55,000
frames. Reviewing all these images is time-consuming and prone to human error.
It has been a challenge to develop intelligent methods assisting physicians to
review the fra... | computer science |
30,085 | Depth Assisted Full Resolution Network for Single Image-based View
Synthesis | cs.CV | Researches in novel viewpoint synthesis majorly focus on interpolation from
multi-view input images. In this paper, we focus on a more challenging and
ill-posed problem that is to synthesize novel viewpoints from one single input
image. To achieve this goal, we propose a novel deep learning-based technique.
We design a... | computer science |
30,086 | Segmenting Brain Tumors with Symmetry | cs.CV | We explore encoding brain symmetry into a neural network for a brain tumor
segmentation task. A healthy human brain is symmetric at a high level of
abstraction, and the high-level asymmetric parts are more likely to be tumor
regions. Paying more attention to asymmetries has the potential to boost the
performance in bra... | computer science |
30,087 | Neural Motifs: Scene Graph Parsing with Global Context | cs.CV | We investigate the problem of producing structured graph representations of
visual scenes. Our work analyzes the role of motifs: regularly appearing
substructures in scene graphs. We present new quantitative insights on such
repeated structures in the Visual Genome dataset. Our analysis shows that
object labels are hig... | computer science |
30,088 | Multiresolution and Hierarchical Analysis of Astronomical Spectroscopic
Cubes using 3D Discrete Wavelet Transform | cs.CV | The intrinsically hierarchical and blended structure of interstellar
molecular clouds, plus the always increasing resolution of astronomical
instruments, demand advanced and automated pattern recognition techniques for
identifying and connecting source components in spectroscopic cubes. We extend
the work done in multi... | computer science |
30,089 | ADVISE: Symbolism and External Knowledge for Decoding Advertisements | cs.CV | In order to convey the most content in their limited space, advertisements
embed references to outside knowledge via symbolism. For example, a motorcycle
stands for adventure (a positive property the ad wants associated with the
product being sold), and a gun stands for danger (a negative property to
dissuade viewers f... | computer science |
30,090 | Integrating Disparate Sources of Experts for Robust Image Denoising | cs.CV | We study an image denoising problem: Given a set of image denoisers, each
having a different denoising capability, can we design a framework that allows
us to integrate the individual denoisers to produce an overall better result?
If we can do so, then potentially we can integrate multiple weak denoisers to
denoise com... | computer science |
30,091 | Learning SO(3) Equivariant Representations with Spherical CNNs | cs.CV | We address the problem of 3D rotation equivariance in convolutional neural
networks. 3D rotations have been a challenging nuisance in 3D classification
tasks requiring higher capacity and extended data augmentation in order to
tackle it. We model 3D data with multi-valued spherical functions and we
propose a novel sphe... | computer science |
30,092 | Wing Loss for Robust Facial Landmark Localisation with Convolutional
Neural Networks | cs.CV | We present a new loss function, namely Wing loss, for robust facial landmark
localisation with Convolutional Neural Networks (CNNs). We first compare and
analyse different objective functions and show that the L1 and smooth L1 loss
functions perform much better than the widely used L2 loss function in facial
landmark l... | computer science |
30,093 | Excitation Backprop for RNNs | cs.CV | Deep models are state-of-the-art for many vision tasks including video action
recognition and video captioning. Models are trained to caption or classify
activity in videos, but little is known about the evidence used to make such
decisions. Grounding decisions made by deep networks has been studied in
spatial visual c... | computer science |
30,094 | Learning Aggregated Transmission Propagation Networks for Haze Removal
and Beyond | cs.CV | Single image dehazing is an important low-level vision task with many
applications. Early researches have investigated different kinds of visual
priors to address this problem. However, they may fail when their assumptions
are not valid on specific images. Recent deep networks also achieve relatively
good performance i... | computer science |
30,095 | A Color Quantization Optimization Approach for Image Representation
Learning | cs.CV | Over the last two decades, hand-crafted feature extractors have been used in
order to compose image representations. Recently, data-driven feature learning
have been explored as a way of producing more representative visual features.
In this work, we proposed two approaches to learn image visual representations
which a... | computer science |
30,096 | Transferable Semi-supervised Semantic Segmentation | cs.CV | The performance of deep learning based semantic segmentation models heavily
depends on sufficient data with careful annotations. However, even the largest
public datasets only provide samples with pixel-level annotations for rather
limited semantic categories. Such data scarcity critically limits scalability
and applic... | computer science |
30,097 | Single-Shot Refinement Neural Network for Object Detection | cs.CV | For object detection, the two-stage approach (e.g., Faster R-CNN) has been
achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has
the advantage of high efficiency. To inherit the merits of both while
overcoming their disadvantages, in this paper, we propose a novel single-shot
based detector, ca... | computer science |
30,098 | Gazing into the Abyss: Real-time Gaze Estimation | cs.CV | Gaze and face tracking algorithms have traditionally battled a compromise
between computational complexity and accuracy; the most accurate neural net
algorithms cannot be implemented in real time, but less complex real-time
algorithms suffer from higher error. This project seeks to better bridge that
gap by improving o... | computer science |
30,099 | A novel total variation model based on kernel functions and its
application | cs.CV | The total variation (TV) model and its related variants have already been
proposed for image processing in previous literature. In this paper a novel
total variation model based on kernel functions is proposed. In this novel
model, we first map each pixel value of an image into a Hilbert space by using
a nonlinear map,... | computer science |
30,100 | Kill Two Birds with One Stone: Weakly-Supervised Neural Network for
Image Annotation and Tag Refinement | cs.CV | The number of social images has exploded by the wide adoption of social
networks, and people like to share their comments about them. These comments
can be a description of the image, or some objects, attributes, scenes in it,
which are normally used as the user-provided tags. However, it is well-known
that user-provid... | computer science |
30,101 | MicroExpNet: An Extremely Small and Fast Model For Expression
Recognition From Frontal Face Images | cs.CV | This paper is aimed at creating extremely small and fast convolutional neural
networks (CNN) for the problem of facial expression recognition (FER) from
frontal face images. We show that, for this problem, translation invariance
(achieved through max-pooling layers) degrades performance, especially when the
network is ... | computer science |
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