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8,400 | Understanding and Improving Convolutional Neural Networks via
Concatenated Rectified Linear Units | cs.LG | Recently, convolutional neural networks (CNNs) have been used as a powerful
tool to solve many problems of machine learning and computer vision. In this
paper, we aim to provide insight on the property of convolutional neural
networks, as well as a generic method to improve the performance of many CNN
architectures. Sp... | computer science |
8,401 | DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout | cs.CV | Domain adaptation deals with adapting behaviour of machine learning based
systems trained using samples in source domain to their deployment in target
domain where the statistics of samples in both domains are dissimilar. The task
of directly training or adapting a learner in the target domain is challenged
by lack of ... | computer science |
8,402 | Deep video gesture recognition using illumination invariants | cs.CV | In this paper we present architectures based on deep neural nets for gesture
recognition in videos, which are invariant to local scaling. We amalgamate
autoencoder and predictor architectures using an adaptive weighting scheme
coping with a reduced size labeled dataset, while enriching our models from
enormous unlabele... | computer science |
8,403 | Multi-velocity neural networks for gesture recognition in videos | cs.CV | We present a new action recognition deep neural network which adaptively
learns the best action velocities in addition to the classification. While deep
neural networks have reached maturity for image understanding tasks, we are
still exploring network topologies and features to handle the richer
environment of video c... | computer science |
8,404 | Global-Local Face Upsampling Network | cs.CV | Face hallucination, which is the task of generating a high-resolution face
image from a low-resolution input image, is a well-studied problem that is
useful in widespread application areas. Face hallucination is particularly
challenging when the input face resolution is very low (e.g., 10 x 12 pixels)
and/or the image ... | computer science |
8,405 | Co-occurrence Feature Learning for Skeleton based Action Recognition
using Regularized Deep LSTM Networks | cs.CV | Skeleton based action recognition distinguishes human actions using the
trajectories of skeleton joints, which provide a very good representation for
describing actions. Considering that recurrent neural networks (RNNs) with Long
Short-Term Memory (LSTM) can learn feature representations and model long-term
temporal de... | computer science |
8,406 | Perceptual Losses for Real-Time Style Transfer and Super-Resolution | cs.CV | We consider image transformation problems, where an input image is
transformed into an output image. Recent methods for such problems typically
train feed-forward convolutional neural networks using a \emph{per-pixel} loss
between the output and ground-truth images. Parallel work has shown that
high-quality images can ... | computer science |
8,407 | Human Pose Estimation using Deep Consensus Voting | cs.CV | In this paper we consider the problem of human pose estimation from a single
still image. We propose a novel approach where each location in the image votes
for the position of each keypoint using a convolutional neural net. The voting
scheme allows us to utilize information from the whole image, rather than rely
on a ... | computer science |
8,408 | Hierarchical Gaussian Mixture Model with Objects Attached to Terminal
and Non-terminal Dendrogram Nodes | cs.LG | A hierarchical clustering algorithm based on Gaussian mixture model is
presented. The key difference to regular hierarchical mixture models is the
ability to store objects in both terminal and nonterminal nodes. Upper levels
of the hierarchy contain sparsely distributed objects, while lower levels
contain densely repre... | computer science |
8,409 | Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation
with Deep Gaussian CRFs | cs.CV | In this work we propose a structured prediction technique that combines the
virtues of Gaussian Conditional Random Fields (G-CRF) with Deep Learning: (a)
our structured prediction task has a unique global optimum that is obtained
exactly from the solution of a linear system (b) the gradients of our model
parameters are... | computer science |
8,410 | Attend, Infer, Repeat: Fast Scene Understanding with Generative Models | cs.CV | We present a framework for efficient inference in structured image models
that explicitly reason about objects. We achieve this by performing
probabilistic inference using a recurrent neural network that attends to scene
elements and processes them one at a time. Crucially, the model itself learns
to choose the appropr... | computer science |
8,411 | A ParaBoost Stereoscopic Image Quality Assessment (PBSIQA) System | cs.CV | The problem of stereoscopic image quality assessment, which finds
applications in 3D visual content delivery such as 3DTV, is investigated in
this work. Specifically, we propose a new ParaBoost (parallel-boosting)
stereoscopic image quality assessment (PBSIQA) system. The system consists of
two stages. In the first sta... | computer science |
8,412 | Classification of Human Whole-Body Motion using Hidden Markov Models | cs.LG | Human motion plays an important role in many fields. Large databases exist
that store and make available recordings of human motions. However, annotating
each motion with multiple labels is a cumbersome and error-prone process. This
bachelor's thesis presents different approaches to solve the multi-label
classification... | computer science |
8,413 | Multimodal Sparse Coding for Event Detection | cs.LG | Unsupervised feature learning methods have proven effective for
classification tasks based on a single modality. We present multimodal sparse
coding for learning feature representations shared across multiple modalities.
The shared representations are applied to multimedia event detection (MED) and
evaluated in compari... | computer science |
8,414 | Inter-Battery Topic Representation Learning | cs.LG | In this paper, we present the Inter-Battery Topic Model (IBTM). Our approach
extends traditional topic models by learning a factorized latent variable
representation. The structured representation leads to a model that marries
benefits traditionally associated with a discriminative approach, such as
feature selection, ... | computer science |
8,415 | Action Classification via Concepts and Attributes | cs.CV | Classes in natural images tend to follow long tail distributions. This is
problematic when there are insufficient training examples for rare classes.
This effect is emphasized in compound classes, involving the conjunction of
several concepts, such as those appearing in action-recognition datasets. In
this paper, we pr... | computer science |
8,416 | Video Summarization with Long Short-term Memory | cs.CV | We propose a novel supervised learning technique for summarizing videos by
automatically selecting keyframes or key subshots. Casting the problem as a
structured prediction problem on sequential data, our main idea is to use Long
Short-Term Memory (LSTM), a special type of recurrent neural networks to model
the variabl... | computer science |
8,417 | k2-means for fast and accurate large scale clustering | cs.LG | We propose k^2-means, a new clustering method which efficiently copes with
large numbers of clusters and achieves low energy solutions. k^2-means builds
upon the standard k-means (Lloyd's algorithm) and combines a new strategy to
accelerate the convergence with a new low time complexity divisive
initialization. The acc... | computer science |
8,418 | End-to-End Instance Segmentation with Recurrent Attention | cs.LG | While convolutional neural networks have gained impressive success recently
in solving structured prediction problems such as semantic segmentation, it
remains a challenge to differentiate individual object instances in the scene.
Instance segmentation is very important in a variety of applications, such as
autonomous ... | computer science |
8,419 | Dynamic Filter Networks | cs.LG | In a traditional convolutional layer, the learned filters stay fixed after
training. In contrast, we introduce a new framework, the Dynamic Filter
Network, where filters are generated dynamically conditioned on an input. We
show that this architecture is a powerful one, with increased flexibility
thanks to its adaptive... | computer science |
8,420 | Generalized Multi-view Embedding for Visual Recognition and Cross-modal
Retrieval | cs.CV | In this paper, the problem of multi-view embedding from different visual cues
and modalities is considered. We propose a unified solution for subspace
learning methods using the Rayleigh quotient, which is extensible for multiple
views, supervised learning, and non-linear embeddings. Numerous methods
including Canonica... | computer science |
8,421 | Self-Paced Learning: an Implicit Regularization Perspective | cs.LG | Self-paced learning (SPL) mimics the cognitive mechanism of humans and
animals that gradually learns from easy to hard samples. One key issue in SPL
is to obtain better weighting strategy that is determined by minimizer
function. Existing methods usually pursue this by artificially designing the
explicit form of SPL re... | computer science |
8,422 | Comparison of 14 different families of classification algorithms on 115
binary datasets | cs.LG | We tested 14 very different classification algorithms (random forest,
gradient boosting machines, SVM - linear, polynomial, and RBF - 1-hidden-layer
neural nets, extreme learning machines, k-nearest neighbors and a bagging of
knn, naive Bayes, learning vector quantization, elastic net logistic
regression, sparse linear... | computer science |
8,423 | Active Regression with Adaptive Huber Loss | cs.LG | This paper addresses the scalar regression problem through a novel solution
to exactly optimize the Huber loss in a general semi-supervised setting, which
combines multi-view learning and manifold regularization. We propose a
principled algorithm to 1) avoid computationally expensive iterative schemes
while 2) adapting... | computer science |
8,424 | Fast and Extensible Online Multivariate Kernel Density Estimation | cs.LG | We present xokde++, a state-of-the-art online kernel density estimation
approach that maintains Gaussian mixture models input data streams. The
approach follows state-of-the-art work on online density estimation, but was
redesigned with computational efficiency, numerical robustness, and
extensibility in mind. Our appr... | computer science |
8,425 | IDNet: Smartphone-based Gait Recognition with Convolutional Neural
Networks | cs.CV | Here, we present IDNet, a user authentication framework from
smartphone-acquired motion signals. Its goal is to recognize a target user from
their way of walking, using the accelerometer and gyroscope (inertial) signals
provided by a commercial smartphone worn in the front pocket of the user's
trousers. IDNet features ... | computer science |
8,426 | DCNNs on a Diet: Sampling Strategies for Reducing the Training Set Size | cs.CV | Large-scale supervised classification algorithms, especially those based on
deep convolutional neural networks (DCNNs), require vast amounts of training
data to achieve state-of-the-art performance. Decreasing this data requirement
would significantly speed up the training process and possibly improve
generalization. M... | computer science |
8,427 | Max-Margin Feature Selection | cs.LG | Many machine learning applications such as in vision, biology and social
networking deal with data in high dimensions. Feature selection is typically
employed to select a subset of features which im- proves generalization
accuracy as well as reduces the computational cost of learning the model. One
of the criteria used... | computer science |
8,428 | Learning feed-forward one-shot learners | cs.CV | One-shot learning is usually tackled by using generative models or
discriminative embeddings. Discriminative methods based on deep learning, which
are very effective in other learning scenarios, are ill-suited for one-shot
learning as they need large amounts of training data. In this paper, we propose
a method to learn... | computer science |
8,429 | Conditional Image Generation with PixelCNN Decoders | cs.CV | This work explores conditional image generation with a new image density
model based on the PixelCNN architecture. The model can be conditioned on any
vector, including descriptive labels or tags, or latent embeddings created by
other networks. When conditioned on class labels from the ImageNet database,
the model is a... | computer science |
8,430 | Augmenting Supervised Neural Networks with Unsupervised Objectives for
Large-scale Image Classification | cs.LG | Unsupervised learning and supervised learning are key research topics in deep
learning. However, as high-capacity supervised neural networks trained with a
large amount of labels have achieved remarkable success in many computer vision
tasks, the availability of large-scale labeled images reduced the significance
of un... | computer science |
8,431 | Deep Learning Markov Random Field for Semantic Segmentation | cs.CV | Semantic segmentation tasks can be well modeled by Markov Random Field (MRF).
This paper addresses semantic segmentation by incorporating high-order
relations and mixture of label contexts into MRF. Unlike previous works that
optimized MRFs using iterative algorithm, we solve MRF by proposing a
Convolutional Neural Net... | computer science |
8,432 | Training LDCRF model on unsegmented sequences using Connectionist
Temporal Classification | cs.LG | Many machine learning problems such as speech recognition, gesture
recognition, and handwriting recognition are concerned with simultaneous
segmentation and labeling of sequence data. Latent-dynamic conditional random
field (LDCRF) is a well-known discriminative method that has been successfully
used for this task. How... | computer science |
8,433 | Overcoming Challenges in Fixed Point Training of Deep Convolutional
Networks | cs.LG | It is known that training deep neural networks, in particular, deep
convolutional networks, with aggressively reduced numerical precision is
challenging. The stochastic gradient descent algorithm becomes unstable in the
presence of noisy gradient updates resulting from arithmetic with limited
numeric precision. One of ... | computer science |
8,434 | Visual Dynamics: Probabilistic Future Frame Synthesis via Cross
Convolutional Networks | cs.CV | We study the problem of synthesizing a number of likely future frames from a
single input image. In contrast to traditional methods, which have tackled this
problem in a deterministic or non-parametric way, we propose a novel approach
that models future frames in a probabilistic manner. Our probabilistic model
makes it... | computer science |
8,435 | DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing | cs.CV | In this paper, we propose a novel encoder-decoder neural network model
referred to as DeepBinaryMask for video compressive sensing. In video
compressive sensing one frame is acquired using a set of coded masks (sensing
matrix) from which a number of video frames is reconstructed, equal to the
number of coded masks. The... | computer science |
8,436 | Improved Multi-Class Cost-Sensitive Boosting via Estimation of the
Minimum-Risk Class | cs.CV | We present a simple unified framework for multi-class cost-sensitive
boosting. The minimum-risk class is estimated directly, rather than via an
approximation of the posterior distribution. Our method jointly optimizes
binary weak learners and their corresponding output vectors, requiring classes
to share features at ea... | computer science |
8,437 | Exploiting Multi-modal Curriculum in Noisy Web Data for Large-scale
Concept Learning | cs.CV | Learning video concept detectors automatically from the big but noisy web
data with no additional manual annotations is a novel but challenging area in
the multimedia and the machine learning community. A considerable amount of
videos on the web are associated with rich but noisy contextual information,
such as the tit... | computer science |
8,438 | gvnn: Neural Network Library for Geometric Computer Vision | cs.CV | We introduce gvnn, a neural network library in Torch aimed towards bridging
the gap between classic geometric computer vision and deep learning. Inspired
by the recent success of Spatial Transformer Networks, we propose several new
layers which are often used as parametric transformations on the data in
geometric compu... | computer science |
8,439 | Deep FisherNet for Object Classification | cs.CV | Despite the great success of convolutional neural networks (CNN) for the
image classification task on datasets like Cifar and ImageNet, CNN's
representation power is still somewhat limited in dealing with object images
that have large variation in size and clutter, where Fisher Vector (FV) has
shown to be an effective ... | computer science |
8,440 | Learning Robust Features using Deep Learning for Automatic Seizure
Detection | cs.LG | We present and evaluate the capacity of a deep neural network to learn robust
features from EEG to automatically detect seizures. This is a challenging
problem because seizure manifestations on EEG are extremely variable both
inter- and intra-patient. By simultaneously capturing spectral, temporal and
spatial informati... | computer science |
8,441 | A study of the effect of JPG compression on adversarial images | cs.CV | Neural network image classifiers are known to be vulnerable to adversarial
images, i.e., natural images which have been modified by an adversarial
perturbation specifically designed to be imperceptible to humans yet fool the
classifier. Not only can adversarial images be generated easily, but these
images will often be... | computer science |
8,442 | Leveraging Union of Subspace Structure to Improve Constrained Clustering | cs.LG | Many clustering problems in computer vision and other contexts are also
classification problems, where each cluster shares a meaningful label. Subspace
clustering algorithms in particular are often applied to problems that fit this
description, for example with face images or handwritten digits. While it is
straightfor... | computer science |
8,443 | Stacked Approximated Regression Machine: A Simple Deep Learning Approach | cs.LG | With the agreement of my coauthors, I Zhangyang Wang would like to withdraw
the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning
Approach". Some experimental procedures were not included in the manuscript,
which makes a part of important claims not meaningful. In the relevant
research, I was ... | computer science |
8,444 | Dynamic Hand Gesture Recognition for Wearable Devices with Low
Complexity Recurrent Neural Networks | cs.CV | Gesture recognition is a very essential technology for many wearable devices.
While previous algorithms are mostly based on statistical methods including the
hidden Markov model, we develop two dynamic hand gesture recognition techniques
using low complexity recurrent neural network (RNN) algorithms. One is based on
vi... | computer science |
8,445 | Local Binary Convolutional Neural Networks | cs.LG | We propose local binary convolution (LBC), an efficient alternative to
convolutional layers in standard convolutional neural networks (CNN). The
design principles of LBC are motivated by local binary patterns (LBP). The LBC
layer comprises of a set of fixed sparse pre-defined binary convolutional
filters that are not u... | computer science |
8,446 | Deep Double Sparsity Encoder: Learning to Sparsify Not Only Features But
Also Parameters | cs.LG | This paper emphasizes the significance to jointly exploit the problem
structure and the parameter structure, in the context of deep modeling. As a
specific and interesting example, we describe the deep double sparsity encoder
(DDSE), which is inspired by the double sparsity model for dictionary learning.
DDSE simultane... | computer science |
8,447 | Densely Connected Convolutional Networks | cs.CV | Recent work has shown that convolutional networks can be substantially
deeper, more accurate, and efficient to train if they contain shorter
connections between layers close to the input and those close to the output. In
this paper, we embrace this observation and introduce the Dense Convolutional
Network (DenseNet), w... | computer science |
8,448 | Pruning Filters for Efficient ConvNets | cs.CV | The success of CNNs in various applications is accompanied by a significant
increase in the computation and parameter storage costs. Recent efforts toward
reducing these overheads involve pruning and compressing the weights of various
layers without hurting original accuracy. However, magnitude-based pruning of
weights... | computer science |
8,449 | Semantic Video Trailers | cs.LG | Query-based video summarization is the task of creating a brief visual
trailer, which captures the parts of the video (or a collection of videos) that
are most relevant to the user-issued query. In this paper, we propose an
unsupervised label propagation approach for this task. Our approach effectively
captures the mul... | computer science |
8,450 | DAiSEE: Towards User Engagement Recognition in the Wild | cs.CV | We introduce DAiSEE, the largest multi-label video classification dataset
comprising of over two-and-a-half million video frames (2,723,882), 9068 video
snippets (about 25 hours of recording) captured from 112 users for recognizing
user affective states, including engagement, in the wild. In addition to
engagement, it ... | computer science |
8,451 | Automatic Selection of Stochastic Watershed Hierarchies | cs.CV | The segmentation, seen as the association of a partition with an image, is a
difficult task. It can be decomposed in two steps: at first, a family of
contours associated with a series of nested partitions (or hierarchy) is
created and organized, then pertinent contours are extracted. A coarser
partition is obtained by ... | computer science |
8,452 | Geometry-Based Next Frame Prediction from Monocular Video | cs.LG | We consider the problem of next frame prediction from video input. A
recurrent convolutional neural network is trained to predict depth from
monocular video input, which, along with the current video image and the camera
trajectory, can then be used to compute the next frame. Unlike prior next-frame
prediction approach... | computer science |
8,453 | PixelNet: Towards a General Pixel-level Architecture | cs.CV | We explore architectures for general pixel-level prediction problems, from
low-level edge detection to mid-level surface normal estimation to high-level
semantic segmentation. Convolutional predictors, such as the
fully-convolutional network (FCN), have achieved remarkable success by
exploiting the spatial redundancy o... | computer science |
8,454 | A Rotation Invariant Latent Factor Model for Moveme Discovery from
Static Poses | cs.CV | We tackle the problem of learning a rotation invariant latent factor model
when the training data is comprised of lower-dimensional projections of the
original feature space. The main goal is the discovery of a set of 3-D bases
poses that can characterize the manifold of primitive human motions, or
movemes, from a trai... | computer science |
8,455 | Deep Structured Features for Semantic Segmentation | cs.CV | We propose a highly structured neural network architecture for semantic
segmentation with an extremely small model size, suitable for low-power
embedded and mobile platforms. Specifically, our architecture combines i) a
Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random
layer realizing a radi... | computer science |
8,456 | Simultaneous Low-rank Component and Graph Estimation for
High-dimensional Graph Signals: Application to Brain Imaging | cs.CV | We propose an algorithm to uncover the intrinsic low-rank component of a
high-dimensional, graph-smooth and grossly-corrupted dataset, under the
situations that the underlying graph is unknown. Based on a model with a
low-rank component plus a sparse perturbation, and an initial graph estimation,
our proposed algorithm... | computer science |
8,457 | Similarity Mapping with Enhanced Siamese Network for Multi-Object
Tracking | cs.CV | Multi-object tracking has recently become an important area of computer
vision, especially for Advanced Driver Assistance Systems (ADAS). Despite
growing attention, achieving high performance tracking is still challenging,
with state-of-the- art systems resulting in high complexity with a large number
of hyper paramete... | computer science |
8,458 | OPML: A One-Pass Closed-Form Solution for Online Metric Learning | cs.LG | To achieve a low computational cost when performing online metric learning
for large-scale data, we present a one-pass closed-form solution namely OPML in
this paper. Typically, the proposed OPML first adopts a one-pass triplet
construction strategy, which aims to use only a very small number of triplets
to approximate... | computer science |
8,459 | Structure-Aware Classification using Supervised Dictionary Learning | cs.LG | In this paper, we propose a supervised dictionary learning algorithm that
aims to preserve the local geometry in both dimensions of the data. A
graph-based regularization explicitly takes into account the local manifold
structure of the data points. A second graph regularization gives similar
treatment to the feature d... | computer science |
8,460 | Video Pixel Networks | cs.CV | We propose a probabilistic video model, the Video Pixel Network (VPN), that
estimates the discrete joint distribution of the raw pixel values in a video.
The model and the neural architecture reflect the time, space and color
structure of video tensors and encode it as a four-dimensional dependency
chain. The VPN appro... | computer science |
8,461 | Kernel Selection using Multiple Kernel Learning and Domain Adaptation in
Reproducing Kernel Hilbert Space, for Face Recognition under Surveillance
Scenario | cs.CV | Face Recognition (FR) has been the interest to several researchers over the
past few decades due to its passive nature of biometric authentication. Despite
high accuracy achieved by face recognition algorithms under controlled
conditions, achieving the same performance for face images obtained in
surveillance scenarios... | computer science |
8,462 | Predicting the dynamics of 2d objects with a deep residual network | cs.CV | We investigate how a residual network can learn to predict the dynamics of
interacting shapes purely as an image-to-image regression task.
With a simple 2d physics simulator, we generate short sequences composed of
rectangles put in motion by applying a pulling force at a point picked at
random. The network is traine... | computer science |
8,463 | Encoding the Local Connectivity Patterns of fMRI for Cognitive State
Classification | cs.CV | In this work, we propose a novel framework to encode the local connectivity
patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated
Descriptors (VLAD) and Bag-of-Words (BoW) methods. We first obtain local
descriptors, called Mesh Arc Descriptors (MADs) from fMRI data, by forming
local meshes around a... | computer science |
8,464 | Fast L1-NMF for Multiple Parametric Model Estimation | cs.CV | In this work we introduce a comprehensive algorithmic pipeline for multiple
parametric model estimation. The proposed approach analyzes the information
produced by a random sampling algorithm (e.g., RANSAC) from a machine
learning/optimization perspective, using a \textit{parameterless} biclustering
algorithm based on ... | computer science |
8,465 | Utilization of Deep Reinforcement Learning for saccadic-based object
visual search | cs.CV | The paper focuses on the problem of learning saccades enabling visual object
search. The developed system combines reinforcement learning with a neural
network for learning to predict the possible outcomes of its actions. We
validated the solution in three types of environment consisting of
(pseudo)-randomly generated ... | computer science |
8,466 | Exercise Motion Classification from Large-Scale Wearable Sensor Data
Using Convolutional Neural Networks | cs.CV | The ability to accurately identify human activities is essential for
developing automatic rehabilitation and sports training systems. In this paper,
large-scale exercise motion data obtained from a forearm-worn wearable sensor
are classified with a convolutional neural network (CNN). Time-series data
consisting of acce... | computer science |
8,467 | Learning a Probabilistic Latent Space of Object Shapes via 3D
Generative-Adversarial Modeling | cs.CV | We study the problem of 3D object generation. We propose a novel framework,
namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects
from a probabilistic space by leveraging recent advances in volumetric
convolutional networks and generative adversarial nets. The benefits of our
model are three-fol... | computer science |
8,468 | A Learned Representation For Artistic Style | cs.CV | The diversity of painting styles represents a rich visual vocabulary for the
construction of an image. The degree to which one may learn and parsimoniously
capture this visual vocabulary measures our understanding of the higher level
features of paintings, if not images in general. In this work we investigate
the const... | computer science |
8,469 | Local Similarity-Aware Deep Feature Embedding | cs.CV | Existing deep embedding methods in vision tasks are capable of learning a
compact Euclidean space from images, where Euclidean distances correspond to a
similarity metric. To make learning more effective and efficient, hard sample
mining is usually employed, with samples identified through computing the
Euclidean featu... | computer science |
8,470 | Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All
Networks | cs.LG | We propose a convolutional recurrent neural network, with Winner-Take-All
dropout for high dimensional unsupervised feature learning in multi-dimensional
time series. We apply the proposedmethod for object recognition with temporal
context in videos and obtain better results than comparable methods in the
literature, i... | computer science |
8,471 | Embedding Deep Metric for Person Re-identication A Study Against Large
Variations | cs.CV | Person re-identification is challenging due to the large variations of pose,
illumination, occlusion and camera view. Owing to these variations, the
pedestrian data is distributed as highly-curved manifolds in the feature space,
despite the current convolutional neural networks (CNN)'s capability of feature
extraction.... | computer science |
8,472 | Learning Identity Mappings with Residual Gates | cs.CV | We propose a new layer design by adding a linear gating mechanism to shortcut
connections. By using a scalar parameter to control each gate, we provide a way
to learn identity mappings by optimizing only one parameter. We build upon the
motivation behind Residual Networks, where a layer is reformulated in order to
make... | computer science |
8,473 | Fixed-point Factorized Networks | cs.CV | In recent years, Deep Neural Networks (DNN) based methods have achieved
remarkable performance in a wide range of tasks and have been among the most
powerful and widely used techniques in computer vision. However, DNN-based
methods are both computational-intensive and resource-consuming, which hinders
the application o... | computer science |
8,474 | Domain Adaptation with L2 constraints for classifying images from
different endoscope systems | cs.CV | This paper proposes a method for domain adaptation that extends the maximum
margin domain transfer (MMDT) proposed by Hoffman et al., by introducing L2
distance constraints between samples of different domains; thus, our method is
denoted as MMDTL2. Motivated by the differences between the images taken by
narrow band i... | computer science |
8,475 | Gradients of Counterfactuals | cs.LG | Gradients have been used to quantify feature importance in machine learning
models. Unfortunately, in nonlinear deep networks, not only individual neurons
but also the whole network can saturate, and as a result an important input
feature can have a tiny gradient. We study various networks, and observe that
this phenom... | computer science |
8,476 | Hierarchical Object Detection with Deep Reinforcement Learning | cs.CV | We present a method for performing hierarchical object detection in images
guided by a deep reinforcement learning agent. The key idea is to focus on
those parts of the image that contain richer information and zoom on them. We
train an intelligent agent that, given an image window, is capable of deciding
where to focu... | computer science |
8,477 | Constrained Low-Rank Learning Using Least Squares-Based Regularization | cs.CV | Low-rank learning has attracted much attention recently due to its efficacy
in a rich variety of real-world tasks, e.g., subspace segmentation and image
categorization. Most low-rank methods are incapable of capturing
low-dimensional subspace for supervised learning tasks, e.g., classification
and regression. This pape... | computer science |
8,478 | S3Pool: Pooling with Stochastic Spatial Sampling | cs.LG | Feature pooling layers (e.g., max pooling) in convolutional neural networks
(CNNs) serve the dual purpose of providing increasingly abstract
representations as well as yielding computational savings in subsequent
convolutional layers. We view the pooling operation in CNNs as a two-step
procedure: first, a pooling windo... | computer science |
8,479 | Fast On-Line Kernel Density Estimation for Active Object Localization | cs.CV | A major goal of computer vision is to enable computers to interpret visual
situations---abstract concepts (e.g., "a person walking a dog," "a crowd
waiting for a bus," "a picnic") whose image instantiations are linked more by
their common spatial and semantic structure than by low-level visual
similarity. In this paper... | computer science |
8,480 | Fully-adaptive Feature Sharing in Multi-Task Networks with Applications
in Person Attribute Classification | cs.CV | Multi-task learning aims to improve generalization performance of multiple
prediction tasks by appropriately sharing relevant information across them. In
the context of deep neural networks, this idea is often realized by
hand-designed network architectures with layers that are shared across tasks
and branches that enc... | computer science |
8,481 | Optical Flow Requires Multiple Strategies (but only one network) | cs.CV | We show that the matching problem that underlies optical flow requires
multiple strategies, depending on the amount of image motion and other factors.
We then study the implications of this observation on training a deep neural
network for representing image patches in the context of descriptor based
optical flow. We p... | computer science |
8,482 | Inverting The Generator Of A Generative Adversarial Network | cs.CV | Generative adversarial networks (GANs) learn to synthesise new samples from a
high-dimensional distribution by passing samples drawn from a latent space
through a generative network. When the high-dimensional distribution describes
images of a particular data set, the network should learn to generate visually
similar i... | computer science |
8,483 | Temporal Generative Adversarial Nets with Singular Value Clipping | cs.LG | In this paper, we propose a generative model, Temporal Generative Adversarial
Nets (TGAN), which can learn a semantic representation of unlabeled videos, and
is capable of generating videos. Unlike existing Generative Adversarial Nets
(GAN)-based methods that generate videos with a single generator consisting of
3D dec... | computer science |
8,484 | Training Sparse Neural Networks | cs.CV | Deep neural networks with lots of parameters are typically used for
large-scale computer vision tasks such as image classification. This is a
result of using dense matrix multiplications and convolutions. However, sparse
computations are known to be much more efficient. In this work, we train and
build neural networks ... | computer science |
8,485 | Effective Deterministic Initialization for $k$-Means-Like Methods via
Local Density Peaks Searching | cs.LG | The $k$-means clustering algorithm is popular but has the following main
drawbacks: 1) the number of clusters, $k$, needs to be provided by the user in
advance, 2) it can easily reach local minima with randomly selected initial
centers, 3) it is sensitive to outliers, and 4) it can only deal with well
separated hypersp... | computer science |
8,486 | Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel
Machines for Efficient Recognition of Isolated Gestures | cs.CV | In the scope of gestural action recognition, the size of the feature vector
representing movements is in general quite large especially when full body
movements are considered. Furthermore, this feature vector evolves during the
movement performance so that a complete movement is fully represented by a
matrix M of size... | computer science |
8,487 | Image Based Appraisal of Real Estate Properties | cs.CV | Real estate appraisal, which is the process of estimating the price for real
estate properties, is crucial for both buys and sellers as the basis for
negotiation and transaction. Traditionally, the repeat sales model has been
widely adopted to estimate real estate price. However, it depends the design
and calculation o... | computer science |
8,488 | Effective Quantization Methods for Recurrent Neural Networks | cs.LG | Reducing bit-widths of weights, activations, and gradients of a Neural
Network can shrink its storage size and memory usage, and also allow for faster
training and inference by exploiting bitwise operations. However, previous
attempts for quantization of RNNs show considerable performance degradation
when using low bit... | computer science |
8,489 | Two-Bit Networks for Deep Learning on Resource-Constrained Embedded
Devices | cs.LG | With the rapid proliferation of Internet of Things and intelligent edge
devices, there is an increasing need for implementing machine learning
algorithms, including deep learning, on resource-constrained mobile embedded
devices with limited memory and computation power. Typical large Convolutional
Neural Networks (CNNs... | computer science |
8,490 | Overlapping Cover Local Regression Machines | cs.LG | We present the Overlapping Domain Cover (ODC) notion for kernel machines, as
a set of overlapping subsets of the data that covers the entire training set
and optimized to be spatially cohesive as possible. We show how this notion
benefit the speed of local kernel machines for regression in terms of both
speed while ach... | computer science |
8,491 | Unsupervised Image-to-Image Translation with Generative Adversarial
Networks | cs.CV | It's useful to automatically transform an image from its original form to
some synthetic form (style, partial contents, etc.), while keeping the original
structure or semantics. We define this requirement as the "image-to-image
translation" problem, and propose a general approach to achieve it, based on
deep convolutio... | computer science |
8,492 | Scaling Binarized Neural Networks on Reconfigurable Logic | cs.CV | Binarized neural networks (BNNs) are gaining interest in the deep learning
community due to their significantly lower computational and memory cost. They
are particularly well suited to reconfigurable logic devices, which contain an
abundance of fine-grained compute resources and can result in smaller, lower
power impl... | computer science |
8,493 | Geometric features for voxel-based surface recognition | cs.CV | We introduce a library of geometric voxel features for CAD surface
recognition/retrieval tasks. Our features include local versions of the
intrinsic volumes (the usual 3D volume, surface area, integrated mean and
Gaussian curvature) and a few closely related quantities. We also compute Haar
wavelet and statistical dist... | computer science |
8,494 | When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum | cs.LG | The study of complex systems benefits from graph models and their analysis.
In particular, the eigendecomposition of the graph Laplacian lets emerge
properties of global organization from local interactions; e.g., the Fiedler
vector has the smallest non-zero eigenvalue and plays a key role for graph
clustering. Graph s... | computer science |
8,495 | Transformation-Based Models of Video Sequences | cs.LG | In this work we propose a simple unsupervised approach for next frame
prediction in video. Instead of directly predicting the pixels in a frame given
past frames, we predict the transformations needed for generating the next
frame in a sequence, given the transformations of the past frames. This leads
to sharper result... | computer science |
8,496 | Fully Convolutional Architectures for Multi-Class Segmentation in Chest
Radiographs | cs.CV | The success of deep convolutional neural networks on image classification and
recognition tasks has led to new applications in very diversified contexts,
including the field of medical imaging. In this paper we investigate and
propose neural network architectures for automated multi-class segmentation of
anatomical org... | computer science |
8,497 | Emergence of Selective Invariance in Hierarchical Feed Forward Networks | cs.LG | Many theories have emerged which investigate how in- variance is generated in
hierarchical networks through sim- ple schemes such as max and mean pooling.
The restriction to max/mean pooling in theoretical and empirical studies has
diverted attention away from a more general way of generating invariance to
nuisance tra... | computer science |
8,498 | SenseGen: A Deep Learning Architecture for Synthetic Sensor Data
Generation | cs.LG | Our ability to synthesize sensory data that preserves specific statistical
properties of the real data has had tremendous implications on data privacy and
big data analytics. The synthetic data can be used as a substitute for
selective real data segments,that are sensitive to the user, thus protecting
privacy and resul... | computer science |
8,499 | Deep Reinforcement Learning for Visual Object Tracking in Videos | cs.CV | In this paper we introduce a fully end-to-end approach for visual tracking in
videos that learns to predict the bounding box locations of a target object at
every frame. An important insight is that the tracking problem can be
considered as a sequential decision-making process and historical semantics
encode highly rel... | computer science |
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