Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
8,600 | Retinal Vessel Segmentation in Fundoscopic Images with Generative
Adversarial Networks | cs.CV | Retinal vessel segmentation is an indispensable step for automatic detection
of retinal diseases with fundoscopic images. Though many approaches have been
proposed, existing methods tend to miss fine vessels or allow false positives
at terminal branches. Let alone under-segmentation, over-segmentation is also
problemat... | computer science |
8,601 | Automated Lane Detection in Crowds using Proximity Graphs | cs.CV | Studying the behavior of crowds is vital for understanding and predicting
human interactions in public areas. Research has shown that, under certain
conditions, large groups of people can form collective behavior patterns: local
interactions between individuals results in global movements patterns. To
detect these patt... | computer science |
8,602 | TasselNet: Counting maize tassels in the wild via local counts
regression network | cs.CV | Accurately counting maize tassels is important for monitoring the growth
status of maize plants. This tedious task, however, is still mainly done by
manual efforts. In the context of modern plant phenotyping, automating this
task is required to meet the need of large-scale analysis of genotype and
phenotype. In recent ... | computer science |
8,603 | Learning Representations and Generative Models for 3D Point Clouds | cs.CV | Three-dimensional geometric data offer an excellent domain for studying
representation learning and generative modeling. In this paper, we look at
geometric data represented as point clouds. We introduce a deep autoencoder
(AE) network with state-of-the-art reconstruction quality and generalization
ability. The learned... | computer science |
8,604 | On Study of the Reliable Fully Convolutional Networks with Tree Arranged
Outputs (TAO-FCN) for Handwritten String Recognition | cs.CV | The handwritten string recognition is still a challengeable task, though the
powerful deep learning tools were introduced. In this paper, based on TAO-FCN,
we proposed an end-to-end system for handwritten string recognition. Compared
with the conventional methods, there is no preprocess nor manually designed
rules empl... | computer science |
8,605 | Distance-to-Mean Continuous Conditional Random Fields to Enhance
Prediction Problem in Traffic Flow Data | cs.CV | The increase of vehicle in highways may cause traffic congestion as well as
in the normal roadways. Predicting the traffic flow in highways especially, is
demanded to solve this congestion problem. Predictions on time-series
multivariate data, such as in the traffic flow dataset, have been largely
accomplished through ... | computer science |
8,606 | Adversarial Dropout for Supervised and Semi-supervised Learning | cs.LG | Recently, the training with adversarial examples, which are generated by
adding a small but worst-case perturbation on input examples, has been proved
to improve generalization performance of neural networks. In contrast to the
individually biased inputs to enhance the generality, this paper introduces
adversarial drop... | computer science |
8,607 | LinkNet: Exploiting Encoder Representations for Efficient Semantic
Segmentation | cs.CV | Pixel-wise semantic segmentation for visual scene understanding not only
needs to be accurate, but also efficient in order to find any use in real-time
application. Existing algorithms even though are accurate but they do not focus
on utilizing the parameters of neural network efficiently. As a result they are
huge in ... | computer science |
8,608 | Reduced Electron Exposure for Energy-Dispersive Spectroscopy using
Dynamic Sampling | cs.LG | Analytical electron microscopy and spectroscopy of biological specimens,
polymers, and other beam sensitive materials has been a challenging area due to
irradiation damage. There is a pressing need to develop novel imaging and
spectroscopic imaging methods that will minimize such sample damage as well as
reduce the dat... | computer science |
8,609 | Merge or Not? Learning to Group Faces via Imitation Learning | cs.CV | Given a large number of unlabeled face images, face grouping aims at
clustering the images into individual identities present in the data. This task
remains a challenging problem despite the remarkable capability of deep
learning approaches in learning face representation. In particular, grouping
results can still be e... | computer science |
8,610 | Be Careful What You Backpropagate: A Case For Linear Output Activations
& Gradient Boosting | cs.LG | In this work, we show that saturating output activation functions, such as
the softmax, impede learning on a number of standard classification tasks.
Moreover, we present results showing that the utility of softmax does not stem
from the normalization, as some have speculated. In fact, the normalization
makes things wo... | computer science |
8,611 | Guiding InfoGAN with Semi-Supervision | cs.CV | In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN)
for image synthesis that leverages information from few labels (as little as
0.22%, max. 10% of the dataset) to learn semantically meaningful and
controllable data representations where latent variables correspond to label
categories. The arch... | computer science |
8,612 | The Reversible Residual Network: Backpropagation Without Storing
Activations | cs.CV | Deep residual networks (ResNets) have significantly pushed forward the
state-of-the-art on image classification, increasing in performance as networks
grow both deeper and wider. However, memory consumption becomes a bottleneck,
as one needs to store the activations in order to calculate gradients using
backpropagation... | computer science |
8,613 | Freehand Ultrasound Image Simulation with Spatially-Conditioned
Generative Adversarial Networks | cs.LG | Sonography synthesis has a wide range of applications, including medical
procedure simulation, clinical training and multimodality image registration.
In this paper, we propose a machine learning approach to simulate ultrasound
images at given 3D spatial locations (relative to the patient anatomy), based
on conditional... | computer science |
8,614 | Vision-based Real Estate Price Estimation | cs.CV | Since the advent of online real estate database companies like Zillow, Trulia
and Redfin, the problem of automatic estimation of market values for houses has
received considerable attention. Several real estate websites provide such
estimates using a proprietary formula. Although these estimates are often close
to the ... | computer science |
8,615 | Deformable Registration through Learning of Context-Specific Metric
Aggregation | cs.CV | We propose a novel weakly supervised discriminative algorithm for learning
context specific registration metrics as a linear combination of conventional
similarity measures. Conventional metrics have been extensively used over the
past two decades and therefore both their strengths and limitations are known.
The challe... | computer science |
8,616 | Deep Layer Aggregation | cs.CV | Visual recognition requires rich representations that span levels from low to
high, scales from small to large, and resolutions from fine to coarse. Even
with the depth of features in a convolutional network, a layer in isolation is
not enough: compounding and aggregating these representations improves
inference of wha... | computer science |
8,617 | Wavelet Convolutional Neural Networks for Texture Classification | cs.CV | Texture classification is an important and challenging problem in many image
processing applications. While convolutional neural networks (CNNs) achieved
significant successes for image classification, texture classification remains
a difficult problem since textures usually do not contain enough information
regarding ... | computer science |
8,618 | Automatic breast cancer grading in lymph nodes using a deep neural
network | cs.CV | The progression of breast cancer can be quantified in lymph node whole-slide
images (WSIs). We describe a novel method for effectively performing
classification of whole-slide images and patient level breast cancer grading.
Our method utilises a deep neural network. The method performs classification
on small patches a... | computer science |
8,619 | Graph-Based Classification of Omnidirectional Images | cs.CV | Omnidirectional cameras are widely used in such areas as robotics and virtual
reality as they provide a wide field of view. Their images are often processed
with classical methods, which might unfortunately lead to non-optimal solutions
as these methods are designed for planar images that have different geometrical
pro... | computer science |
8,620 | A Downsampled Variant of ImageNet as an Alternative to the CIFAR
datasets | cs.CV | The original ImageNet dataset is a popular large-scale benchmark for training
Deep Neural Networks. Since the cost of performing experiments (e.g, algorithm
design, architecture search, and hyperparameter tuning) on the original dataset
might be prohibitive, we propose to consider a downsampled version of ImageNet.
In ... | computer science |
8,621 | Human Pose Forecasting via Deep Markov Models | cs.CV | Human pose forecasting is an important problem in computer vision with
applications to human-robot interaction, visual surveillance, and autonomous
driving. Usually, forecasting algorithms use 3D skeleton sequences and are
trained to forecast for a few milliseconds into the future. Long-range
forecasting is challenging... | computer science |
8,622 | Improved Face Detection and Alignment using Cascade Deep Convolutional
Network | cs.CV | Real-world face detection and alignment demand an advanced discriminative
model to address challenges by pose, lighting and expression. Illuminated by
the deep learning algorithm, some convolutional neural networks based face
detection and alignment methods have been proposed. Recent studies have
utilized the relation ... | computer science |
8,623 | Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia
with Deep Learning Pose Estimation | cs.CV | Objective: To apply deep learning pose estimation algorithms for vision-based
assessment of parkinsonism and levodopa-induced dyskinesia (LID). Methods: Nine
participants with Parkinson's disease (PD) and LID completed a levodopa
infusion protocol, where symptoms were assessed at regular intervals using the
Unified Dys... | computer science |
8,624 | Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes | cs.CV | During the last half decade, convolutional neural networks (CNNs) have
triumphed over semantic segmentation, which is a core task of various emerging
industrial applications such as autonomous driving and medical imaging.
However, to train CNNs requires a huge amount of data, which is difficult to
collect and laborious... | computer science |
8,625 | Feature Extraction via Recurrent Random Deep Ensembles and its
Application in Gruop-level Happiness Estimation | cs.CV | This paper presents a novel ensemble framework to extract highly
discriminative feature representation of image and its application for
group-level happpiness intensity prediction in wild. In order to generate
enough diversity of decisions, n convolutional neural networks are trained by
bootstrapping the training set a... | computer science |
8,626 | SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM
Recurrent Neural Networks | cs.CV | The outstanding pattern recognition performance of deep learning brings new
vitality to the synthetic aperture radar (SAR) automatic target recognition
(ATR). However, there is a limitation in current deep learning based ATR
solution that each learning process only handle one SAR image, namely learning
the static scatt... | computer science |
8,627 | Video Object Segmentation with Re-identification | cs.CV | Conventional video segmentation methods often rely on temporal continuity to
propagate masks. Such an assumption suffers from issues like drifting and
inability to handle large displacement. To overcome these issues, we formulate
an effective mechanism to prevent the target from being lost via adaptive
object re-identi... | computer science |
8,628 | Sensor Transformation Attention Networks | cs.LG | Recent work on encoder-decoder models for sequence-to-sequence mapping has
shown that integrating both temporal and spatial attention mechanisms into
neural networks increases the performance of the system substantially. In this
work, we report on the application of an attentional signal not on temporal and
spatial reg... | computer science |
8,629 | DSOD: Learning Deeply Supervised Object Detectors from Scratch | cs.CV | We present Deeply Supervised Object Detector (DSOD), a framework that can
learn object detectors from scratch. State-of-the-art object objectors rely
heavily on the off-the-shelf networks pre-trained on large-scale classification
datasets like ImageNet, which incurs learning bias due to the difference on
both the loss ... | computer science |
8,630 | Unconstrained Fashion Landmark Detection via Hierarchical Recurrent
Transformer Networks | cs.CV | Fashion landmarks are functional key points defined on clothes, such as
corners of neckline, hemline, and cuff. They have been recently introduced as
an effective visual representation for fashion image understanding. However,
detecting fashion landmarks are challenging due to background clutters, human
poses, and scal... | computer science |
8,631 | Image Quality Assessment Techniques Show Improved Training and
Evaluation of Autoencoder Generative Adversarial Networks | cs.CV | We propose a training and evaluation approach for autoencoder Generative
Adversarial Networks (GANs), specifically the Boundary Equilibrium Generative
Adversarial Network (BEGAN), based on methods from the image quality assessment
literature. Our approach explores a multidimensional evaluation criterion that
utilizes t... | computer science |
8,632 | Learning Graph While Training: An Evolving Graph Convolutional Neural
Network | cs.LG | Convolution Neural Networks on Graphs are important generalization and
extension of classical CNNs. While previous works generally assumed that the
graph structures of samples are regular with unified dimensions, in many
applications, they are highly diverse or even not well defined. Under some
circumstances, e.g. chem... | computer science |
8,633 | Practical Block-wise Neural Network Architecture Generation | cs.CV | Convolutional neural networks have gained a remarkable success in computer
vision. However, most usable network architectures are hand-crafted and usually
require expertise and elaborate design. In this paper, we provide a block-wise
network generation pipeline called BlockQNN which automatically builds
high-performanc... | computer science |
8,634 | Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable
Sensors | cs.CV | Human activity recognition (HAR) has become a popular topic in research
because of its wide application. With the development of deep learning, new
ideas have appeared to address HAR problems. Here, a deep network architecture
using residual bidirectional long short-term memory (LSTM) cells is proposed.
The advantages ... | computer science |
8,635 | EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and
Land Cover Classification | cs.CV | In this paper, we address the challenge of land use and land cover
classification using remote sensing satellite images. For this challenging
task, we use the openly and freely accessible Sentinel-2 satellite images
provided within the scope of the Earth observation program Copernicus. The key
contributions are as foll... | computer science |
8,636 | Deep Learning-Guided Image Reconstruction from Incomplete Data | cs.CV | An approach to incorporate deep learning within an iterative image
reconstruction framework to reconstruct images from severely incomplete
measurement data is presented. Specifically, we utilize a convolutional neural
network (CNN) as a quasi-projection operator within a least squares
minimization procedure. The CNN is... | computer science |
8,637 | Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D
Convolutional Neural Networks | cs.CV | Automatic analysis of the video is one of most complex problems in the fields
of computer vision and machine learning. A significant part of this research
deals with (human) activity recognition (HAR) since humans, and the activities
that they perform, generate most of the video semantics. Video-based HAR has
applicati... | computer science |
8,638 | Integrating Specialized Classifiers Based on Continuous Time Markov
Chain | cs.LG | Specialized classifiers, namely those dedicated to a subset of classes, are
often adopted in real-world recognition systems. However, integrating such
classifiers is nontrivial. Existing methods, e.g. weighted average, usually
implicitly assume that all constituents of an ensemble cover the same set of
classes. Such me... | computer science |
8,639 | Visual Cues to Improve Myoelectric Control of Upper Limb Prostheses | cs.CV | The instability of myoelectric signals over time complicates their use to
control highly articulated prostheses. To address this problem, studies have
tried to combine surface electromyography with modalities that are less
affected by the amputation and environment, such as accelerometry or gaze
information. In the lat... | computer science |
8,640 | Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial
Classification of Hyperspectral Image | cs.CV | As a new machine learning approach, extreme learning machine (ELM) has
received wide attentions due to its good performances. However, when directly
applied to the hyperspectral image (HSI) classification, the recognition rate
is too low. This is because ELM does not use the spatial information which is
very important ... | computer science |
8,641 | Intraoperative Organ Motion Models with an Ensemble of Conditional
Generative Adversarial Networks | cs.CV | In this paper, we describe how a patient-specific, ultrasound-probe-induced
prostate motion model can be directly generated from a single preoperative MR
image. Our motion model allows for sampling from the conditional distribution
of dense displacement fields, is encoded by a generative neural network
conditioned on a... | computer science |
8,642 | Embedded Binarized Neural Networks | cs.CV | We study embedded Binarized Neural Networks (eBNNs) with the aim of allowing
current binarized neural networks (BNNs) in the literature to perform
feedforward inference efficiently on small embedded devices. We focus on
minimizing the required memory footprint, given that these devices often have
memory as small as ten... | computer science |
8,643 | Robust Sparse Coding via Self-Paced Learning | cs.LG | Sparse coding (SC) is attracting more and more attention due to its
comprehensive theoretical studies and its excellent performance in many signal
processing applications. However, most existing sparse coding algorithms are
nonconvex and are thus prone to becoming stuck into bad local minima,
especially when there are ... | computer science |
8,644 | Deep Mean-Shift Priors for Image Restoration | cs.CV | In this paper we introduce a natural image prior that directly represents a
Gaussian-smoothed version of the natural image distribution. We include our
prior in a formulation of image restoration as a Bayes estimator that also
allows us to solve noise-blind image restoration problems. We show that the
gradient of our p... | computer science |
8,645 | Neural Affine Grayscale Image Denoising | cs.CV | We propose a new grayscale image denoiser, dubbed as Neural Affine Image
Denoiser (Neural AIDE), which utilizes neural network in a novel way. Unlike
other neural network based image denoising methods, which typically apply
simple supervised learning to learn a mapping from a noisy patch to a clean
patch, we formulate ... | computer science |
8,646 | Estimated Depth Map Helps Image Classification | cs.CV | We consider image classification with estimated depth. This problem falls
into the domain of transfer learning, since we are using a model trained on a
set of depth images to generate depth maps (additional features) for use in
another classification problem using another disjoint set of images. It's
challenging as no ... | computer science |
8,647 | Cascaded Region-based Densely Connected Network for Event Detection: A
Seismic Application | cs.LG | Automatic event detection from time series signals has wide applications,
such as abnormal event detection in video surveillance and event detection in
geophysical data. Traditional detection methods detect events primarily by the
use of similarity and correlation in data. Those methods can be inefficient and
yield low... | computer science |
8,648 | Connectivity Learning in Multi-Branch Networks | cs.LG | While much of the work in the design of convolutional networks over the last
five years has revolved around the empirical investigation of the importance of
depth, filter sizes, and number of feature channels, recent studies have shown
that branching, i.e., splitting the computation along parallel but distinct
threads ... | computer science |
8,649 | Learning Affinity via Spatial Propagation Networks | cs.CV | In this paper, we propose spatial propagation networks for learning the
affinity matrix for vision tasks. We show that by constructing a row/column
linear propagation model, the spatially varying transformation matrix exactly
constitutes an affinity matrix that models dense, global pairwise relationships
of an image. S... | computer science |
8,650 | Toward Multi-Diversified Ensemble Clustering of High-Dimensional Data | cs.LG | The emergence of high-dimensional data in various areas has brought new
challenges to the ensemble clustering research. To deal with the curse of
dimensionality, considerable efforts in ensemble clustering have been made by
incorporating various subspace-based techniques. Besides the emphasis on
subspaces, rather limit... | computer science |
8,651 | Regularizing Deep Neural Networks by Noise: Its Interpretation and
Optimization | cs.LG | Overfitting is one of the most critical challenges in deep neural networks,
and there are various types of regularization methods to improve generalization
performance. Injecting noises to hidden units during training, e.g., dropout,
is known as a successful regularizer, but it is still not clear enough why such
traini... | computer science |
8,652 | CNNComparator: Comparative Analytics of Convolutional Neural Networks | cs.LG | Convolutional neural networks (CNNs) are widely used in many image
recognition tasks due to their extraordinary performance. However, training a
good CNN model can still be a challenging task. In a training process, a CNN
model typically learns a large number of parameters over time, which usually
results in different ... | computer science |
8,653 | Deep Self-Paced Learning for Person Re-Identification | cs.CV | Person re-identification (Re-ID) usually suffers from noisy samples with
background clutter and mutual occlusion, which makes it extremely difficult to
distinguish different individuals across the disjoint camera views. In this
paper, we propose a novel deep self-paced learning (DSPL) algorithm to
alleviate this proble... | computer science |
8,654 | Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks | cs.CV | In this paper, we investigate the effectiveness of deep learning techniques
for lung nodule classification in computed tomography scans. Using less than
10,000 training examples, our deep networks perform two times better than a
standard radiology software. Visualization of the networks' neurons reveals
semantically me... | computer science |
8,655 | Pushing the envelope in deep visual recognition for mobile platforms | cs.CV | Image classification is the task of assigning to an input image a label from
a fixed set of categories. One of its most important applicative fields is that
of robotics, in particular the needing of a robot to be aware of what's around
and the consequent exploitation of that information as a benefit for its tasks.
In t... | computer science |
8,656 | A Generative Restricted Boltzmann Machine Based Method for
High-Dimensional Motion Data Modeling | cs.CV | Many computer vision applications involve modeling complex spatio-temporal
patterns in high-dimensional motion data. Recently, restricted Boltzmann
machines (RBMs) have been widely used to capture and represent spatial patterns
in a single image or temporal patterns in several time slices. To model global
dynamics and ... | computer science |
8,657 | A Survey of Model Compression and Acceleration for Deep Neural Networks | cs.LG | Deep convolutional neural networks (CNNs) have recently achieved great
success in many visual recognition tasks. However, existing deep convolutional
neural network models are computationally expensive and memory intensive,
hindering their deployment in devices with low memory resources or in
applications with strict l... | computer science |
8,658 | A Bayesian Data Augmentation Approach for Learning Deep Models | cs.CV | Data augmentation is an essential part of the training process applied to
deep learning models. The motivation is that a robust training process for deep
learning models depends on large annotated datasets, which are expensive to be
acquired, stored and processed. Therefore a reasonable alternative is to be
able to aut... | computer science |
8,659 | Modeling Attention in Panoramic Video: A Deep Reinforcement Learning
Approach | cs.CV | Panoramic video provides immersive and interactive experience by enabling
humans to control the field of view (FoV) through head movement (HM). Thus, HM
plays a key role in modeling human attention on panoramic video. This paper
establishes a database collecting subjects' HM positions on panoramic video
sequences. From... | computer science |
8,660 | Log-DenseNet: How to Sparsify a DenseNet | cs.CV | Skip connections are increasingly utilized by deep neural networks to improve
accuracy and cost-efficiency. In particular, the recent DenseNet is efficient
in computation and parameters, and achieves state-of-the-art predictions by
directly connecting each feature layer to all previous ones. However,
DenseNet's extreme... | computer science |
8,661 | Medical Image Segmentation Based on Multi-Modal Convolutional Neural
Network: Study on Image Fusion Schemes | cs.CV | Image analysis using more than one modality (i.e. multi-modal) has been
increasingly applied in the field of biomedical imaging. One of the challenges
in performing the multimodal analysis is that there exist multiple schemes for
fusing the information from different modalities, where such schemes are
application-depen... | computer science |
8,662 | A multitask deep learning model for real-time deployment in embedded
systems | cs.CV | We propose an approach to Multitask Learning (MTL) to make deep learning
models faster and lighter for applications in which multiple tasks need to be
solved simultaneously, which is particularly useful in embedded, real-time
systems. We develop a multitask model for both Object Detection and Semantic
Segmentation and ... | computer science |
8,663 | Structured Generative Adversarial Networks | cs.LG | We study the problem of conditional generative modeling based on designated
semantics or structures. Existing models that build conditional generators
either require massive labeled instances as supervision or are unable to
accurately control the semantics of generated samples. We propose structured
generative adversar... | computer science |
8,664 | Label-driven weakly-supervised learning for multimodal deformable image
registration | cs.CV | Spatially aligning medical images from different modalities remains a
challenging task, especially for intraoperative applications that require fast
and robust algorithms. We propose a weakly-supervised, label-driven formulation
for learning 3D voxel correspondence from higher-level label correspondence,
thereby bypass... | computer science |
8,665 | Characterizing Sparse Connectivity Patterns in Neural Networks | cs.LG | We propose a novel way of reducing the number of parameters in the
storage-hungry fully connected layers of a neural network by using pre-defined
sparsity, where the majority of connections are absent prior to starting
training. Our results indicate that convolutional neural networks can operate
without any loss of acc... | computer science |
8,666 | D-PCN: Parallel Convolutional Networks for Image Recognition via a
Discriminator | cs.CV | In this paper, we introduce a simple but quite effective recognition
framework dubbed D-PCN, aiming at enhancing feature extracting ability of CNN.
The framework consists of two parallel CNNs, a discriminator and an extra
classifier which takes integrated features from parallel networks and gives
final prediction. The ... | computer science |
8,667 | Zero-Shot Learning via Class-Conditioned Deep Generative Models | cs.LG | We present a deep generative model for learning to predict classes not seen
at training time. Unlike most existing methods for this problem, that represent
each class as a point (via a semantic embedding), we represent each seen/unseen
class using a class-specific latent-space distribution, conditioned on class
attribu... | computer science |
8,668 | Hybrid Approach of Relation Network and Localized Graph Convolutional
Filtering for Breast Cancer Subtype Classification | cs.CV | Network biology has been successfully used to help reveal complex mechanisms
of disease, especially cancer. On the other hand, network biology requires
in-depth knowledge to construct disease-specific networks, but our current
knowledge is very limited even with the recent advances in human cancer
biology. Deep learnin... | computer science |
8,669 | Priming Neural Networks | cs.CV | Visual priming is known to affect the human visual system to allow detection
of scene elements, even those that may have been near unnoticeable before, such
as the presence of camouflaged animals. This process has been shown to be an
effect of top-down signaling in the visual system triggered by the said cue. In
this p... | computer science |
8,670 | Less-forgetful Learning for Domain Expansion in Deep Neural Networks | cs.LG | Expanding the domain that deep neural network has already learned without
accessing old domain data is a challenging task because deep neural networks
forget previously learned information when learning new data from a new domain.
In this paper, we propose a less-forgetful learning method for the domain
expansion scena... | computer science |
8,671 | Learning Compositional Visual Concepts with Mutual Consistency | cs.CV | Compositionality of semantic concepts in image synthesis and analysis is
appealing as it can help in decomposing known and generatively recomposing
unknown data. For instance, we may learn concepts of changing illumination,
geometry or albedo of a scene, and try to recombine them to generate physically
meaningful, but ... | computer science |
8,672 | xUnit: Learning a Spatial Activation Function for Efficient Image
Restoration | cs.CV | In recent years, deep neural networks (DNNs) achieved unprecedented
performance in many low-level vision tasks. However, state-of-the-art results
are typically achieved by very deep networks, which can reach tens of layers
with tens of millions of parameters. To make DNNs implementable on platforms
with limited resourc... | computer science |
8,673 | Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep
Object Detection | cs.CV | We propose a new method for fusing a LIDAR point cloud and camera-captured
images in the deep convolutional neural network (CNN). The proposed method
constructs a new layer called non-homogeneous pooling layer to transform
features between bird view map and front view map. The sparse LIDAR point cloud
is used to constr... | computer science |
8,674 | DLTK: State of the Art Reference Implementations for Deep Learning on
Medical Images | cs.CV | We present DLTK, a toolkit providing baseline implementations for efficient
experimentation with deep learning methods on biomedical images. It builds on
top of TensorFlow and its high modularity and easy-to-use examples allow for a
low-threshold access to state-of-the-art implementations for typical medical
imaging pr... | computer science |
8,675 | Learning Steerable Filters for Rotation Equivariant CNNs | cs.LG | In many machine learning tasks it is desirable that a model's prediction
transforms in an equivariant way under transformations of its input.
Convolutional neural networks (CNNs) implement translational equivariance by
construction; for other transformations, however, they are compelled to learn
the proper mapping. In ... | computer science |
8,676 | Neural 3D Mesh Renderer | cs.CV | For modeling the 3D world behind 2D images, which 3D representation is most
appropriate? A polygon mesh is a promising candidate for its compactness and
geometric properties. However, it is not straightforward to model a polygon
mesh from 2D images using neural networks because the conversion from a mesh to
an image, o... | computer science |
8,677 | BlockDrop: Dynamic Inference Paths in Residual Networks | cs.CV | Very deep convolutional neural networks offer excellent recognition results,
yet their computational expense limits their impact for many real-world
applications. We introduce BlockDrop, an approach that learns to dynamically
choose which layers of a deep network to execute during inference so as to best
reduce total c... | computer science |
8,678 | In Defense of Product Quantization | cs.CV | Despite their widespread adoption, Product Quantization techniques were
recently shown to be inferior to other hashing techniques. In this work, we
present an improved Deep Product Quantization (DPQ) technique that leads to
more accurate retrieval and classification than the latest state of the art
methods, while havin... | computer science |
8,679 | Wasserstein Introspective Neural Networks | cs.CV | We present Wasserstein introspective neural networks (WINN) that are both a
generator and a discriminator within a single model. WINN provides a
significant improvement over the recent introspective neural networks (INN)
method by enhancing INN's generative modeling capability. WINN has three
interesting properties: (1... | computer science |
8,680 | Geometric robustness of deep networks: analysis and improvement | cs.CV | Deep convolutional neural networks have been shown to be vulnerable to
arbitrary geometric transformations. However, there is no systematic method to
measure the invariance properties of deep networks to such transformations. We
propose ManiFool as a simple yet scalable algorithm to measure the invariance
of deep netwo... | computer science |
8,681 | Attention Clusters: Purely Attention Based Local Feature Integration for
Video Classification | cs.CV | Recently, substantial research effort has focused on how to apply CNNs or
RNNs to better extract temporal patterns from videos, so as to improve the
accuracy of video classification. In this paper, however, we show that temporal
information, especially longer-term patterns, may not be necessary to achieve
competitive r... | computer science |
8,682 | Convolutional Networks with Adaptive Computation Graphs | cs.CV | Do convolutional networks really need a fixed feed-forward structure? Often,
a neural network is already confident after a few layers about the high-level
concept shown in the image. However, due to the fixed network structure, all
remaining layers still need to be evaluated. What if the network could jump
right to a l... | computer science |
8,683 | Hybrid VAE: Improving Deep Generative Models using Partial Observations | cs.LG | Deep neural network models trained on large labeled datasets are the
state-of-the-art in a large variety of computer vision tasks. In many
applications, however, labeled data is expensive to obtain or requires a time
consuming manual annotation process. In contrast, unlabeled data is often
abundant and available in lar... | computer science |
8,684 | Deep Learning for Metagenomic Data: using 2D Embeddings and
Convolutional Neural Networks | cs.CV | Deep learning (DL) techniques have had unprecedented success when applied to
images, waveforms, and texts to cite a few. In general, when the sample size
(N) is much greater than the number of features (d), DL outperforms previous
machine learning (ML) techniques, often through the use of convolution neural
networks (C... | computer science |
8,685 | Semi-Adversarial Networks: Convolutional Autoencoders for Imparting
Privacy to Face Images | cs.CV | In this paper, we design and evaluate a convolutional autoencoder that
perturbs an input face image to impart privacy to a subject. Specifically, the
proposed autoencoder transforms an input face image such that the transformed
image can be successfully used for face recognition but not for gender
classification. In or... | computer science |
8,686 | Probabilistic Adaptive Computation Time | cs.LG | We present a probabilistic model with discrete latent variables that control
the computation time in deep learning models such as ResNets and LSTMs. A prior
on the latent variables expresses the preference for faster computation. The
amount of computation for an input is determined via amortized maximum a
posteriori (M... | computer science |
8,687 | Mix-and-Match Tuning for Self-Supervised Semantic Segmentation | cs.CV | Deep convolutional networks for semantic image segmentation typically require
large-scale labeled data, e.g. ImageNet and MS COCO, for network pre-training.
To reduce annotation efforts, self-supervised semantic segmentation is recently
proposed to pre-train a network without any human-provided labels. The key of
this ... | computer science |
8,688 | Spatial PixelCNN: Generating Images from Patches | cs.CV | In this paper we propose Spatial PixelCNN, a conditional autoregressive model
that generates images from small patches. By conditioning on a grid of pixel
coordinates and global features extracted from a Variational Autoencoder (VAE),
we are able to train on patches of images, and reproduce the full-sized image.
We sho... | computer science |
8,689 | Deep learning for semantic segmentation of remote sensing images with
rich spectral content | cs.CV | With the rapid development of Remote Sensing acquisition techniques, there is
a need to scale and improve processing tools to cope with the observed increase
of both data volume and richness. Among popular techniques in remote sensing,
Deep Learning gains increasing interest but depends on the quality of the
training d... | computer science |
8,690 | Fully-Convolutional Measurement Network for Compressive Sensing Image
Reconstruction | cs.CV | Recently, deep learning methods have made a significant improvement in com-
pressive sensing image reconstruction task. However, it still remains a problem
of block effect which degrades the reconstruction results. In this paper, we
pro- pose a fully-convolutional network, where the full image is directly
measured with... | computer science |
8,691 | Three-Stream Convolutional Networks for Video-based Person
Re-Identification | cs.CV | This paper aims to develop a new architecture that can make full use of the
feature maps of convolutional networks. To this end, we study a number of
methods for video-based person re-identification and make the following
findings: 1) Max-pooling only focuses on the maximum value of a receptive
field, wasting a lot of ... | computer science |
8,692 | Context Augmentation for Convolutional Neural Networks | cs.CV | Recent enhancements of deep convolutional neural networks (ConvNets)
empowered by enormous amounts of labeled data have closed the gap with human
performance for many object recognition tasks. These impressive results have
generated interest in understanding and visualization of ConvNets. In this
work, we study the eff... | computer science |
8,693 | A Pitfall of Unsupervised Pre-Training | cs.CV | The point of this paper is to question typical assumptions in deep learning
and suggest alternatives. A particular contribution is to prove that even if a
Stacked Convolutional Auto-Encoder is good at reconstructing pictures, it is
not necessarily good at discriminating their classes. When using Auto-Encoders,
intuitiv... | computer science |
8,694 | An Ensemble of Deep Convolutional Neural Networks for Alzheimer's
Disease Detection and Classification | cs.CV | Alzheimer's Disease destroys brain cells causing people to lose their memory,
mental functions and ability to continue daily activities. It is a severe
neurological brain disorder which is not curable, but earlier detection of
Alzheimer's Disease can help for proper treatment and to prevent brain tissue
damage. Detecti... | computer science |
8,695 | Towards Recovery of Conditional Vectors from Conditional Generative
Adversarial Networks | cs.CV | A conditional Generative Adversarial Network allows for generating samples
conditioned on certain external information. Being able to recover latent and
conditional vectors from a condi- tional GAN can be potentially valuable in
various applications, ranging from image manipulation for entertaining purposes
to diagnosi... | computer science |
8,696 | Lung Nodule Classification by the Combination of Fusion Classifier and
Cascaded Convolutional Neural Networks | cs.CV | Lung nodule classification is a class imbalanced problem, as nodules are
found with much lower frequency than non-nodules. In the class imbalanced
problem, conventional classifiers tend to be overwhelmed by the majority class
and ignore the minority class. We showed that cascaded convolutional neural
networks can class... | computer science |
8,697 | Take it in your stride: Do we need striding in CNNs? | cs.LG | Since their inception, CNNs have utilized some type of striding operator to
reduce the overlap of receptive fields and spatial dimensions. Although having
clear heuristic motivations (i.e. lowering the number of parameters to learn)
the mathematical role of striding within CNN learning remains unclear. This
paper offer... | computer science |
8,698 | Per-Pixel Feedback for improving Semantic Segmentation | cs.LG | Semantic segmentation is the task of assigning a label to each pixel in the
image.In recent years, deep convolutional neural networks have been driving
advances in multiple tasks related to cognition. Although, DCNNs have resulted
in unprecedented visual recognition performances, they offer little
transparency. To unde... | computer science |
8,699 | StrassenNets: Deep learning with a multiplication budget | cs.LG | A large fraction of the arithmetic operations required to evaluate deep
neural networks (DNNs) consist of matrix multiplications, in both convolution
and fully connected layers. We perform end-to-end learning of low-cost
approximations of matrix multiplications in DNN layers by casting matrix
multiplications as $2$-lay... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.