pwc-archive/papers-with-abstracts huggingface dataset:
Sensor Adaptation for Improved Semantic Segmentation of Overhead Imagery
Authors: Marc Bosch, Gordon A. Christie, Christopher M. Gifford
Date: 2018-11-20 00:00:00
Tasks: Scene Understanding, Segmentation, Semantic Segmentation

Semantic segmentation is a powerful method to facilitate visual scene
understanding. Each pixel is assigned a label according to a pre-defined list
of object classes and semantic entities. This becomes very useful as a means to
summarize large scale overhead imagery. In this paper we present our work on
semantic segmentation with applications to overhead imagery. We propose an
algorithm that builds and extends upon the DeepLab framework to be able to
refine and resolve small objects (relative to the image size) such as vehicles.
We have also investigated sensor adaptation as a means to augment available
training data to be able to reduce some of the shortcomings of neural networks
when deployed in new environments and to new sensors. We report results on
several datasets and compare performance with other state-of-the-art
architectures.
Stability Based Filter Pruning for Accelerating Deep CNNs
Authors: Pravendra Singh, Vinay Sameer Raja Kadi, Nikhil Verma, Vinay P. Namboodiri
Date: 2018-11-20 00:00:00
Tasks: GPU, Model Compression

Convolutional neural networks (CNN) have achieved impressive performance on
the wide variety of tasks (classification, detection, etc.) across multiple
domains at the cost of high computational and memory requirements. Thus,
leveraging CNNs for real-time applications necessitates model compression
approaches that not only reduce the total number of parameters but reduce the
overall computation as well. In this work, we present a stability-based
approach for filter-level pruning of CNNs. We evaluate our proposed approach on
different architectures (LeNet, VGG-16, ResNet, and Faster RCNN) and datasets
and demonstrate its generalizability through extensive experiments. Moreover,
our compressed models can be used at run-time without requiring any special
libraries or hardware. Our model compression method reduces the number of FLOPS
by an impressive factor of 6.03X and GPU memory footprint by more than 17X,
significantly outperforming other state-of-the-art filter pruning methods.
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling
Authors: Giorgia Ramponi, Pavlos Protopapas, Marco Brambilla, Ryan Janssen
Date: 2018-11-20 00:00:00
Tasks: Data Augmentation, Generative Adversarial Network, Time Series, Time Series Analysis

In this paper we propose a data augmentation method for time series with
irregular sampling, Time-Conditional Generative Adversarial Network (T-CGAN).
Our approach is based on Conditional Generative Adversarial Networks (CGAN),
where the generative step is implemented by a deconvolutional NN and the
discriminative step by a convolutional NN. Both the generator and the
discriminator are conditioned on the sampling timestamps, to learn the hidden
relationship between data and timestamps, and consequently to generate new time
series. We evaluate our model with synthetic and real-world datasets. For the
synthetic data, we compare the performance of a classifier trained with
T-CGAN-generated data, against the performance of the same classifier trained
on the original data. Results show that classifiers trained on T-CGAN-generated
data perform the same as classifiers trained on real data, even with very short
time series and small training sets. For the real world datasets, we compare
our method with other techniques of data augmentation for time series, such as
time slicing and time warping, over a classification problem with unbalanced
datasets. Results show that our method always outperforms the other approaches,
both in case of regularly sampled and irregularly sampled time series. We
achieve particularly good performance in case with a small training set and
short, noisy, irregularly-sampled time series.
Transferable Interactiveness Knowledge for Human-Object Interaction Detection
Authors: Yong-Lu Li, Siyuan Zhou, Xijie Huang, Liang Xu, Ze Ma, Hao-Shu Fang, Yan-Feng Wang, Cewu Lu
Date: 2018-11-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: Human-Object Interaction Detection, Object

Human-Object Interaction (HOI) Detection is an important problem to understand how humans interact with objects. In this paper, we explore Interactiveness Knowledge which indicates whether human and object interact with each other or not. We found that interactiveness knowledge can be learned across HOI datasets, regardless of HOI category settings. Our core idea is to exploit an Interactiveness Network to learn the general interactiveness knowledge from multiple HOI datasets and perform Non-Interaction Suppression before HOI classification in inference. On account of the generalization of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results. We extensively evaluate the proposed method on HICO-DET and V-COCO datasets. Our framework outperforms state-of-the-art HOI detection results by a great margin, verifying its efficacy and flexibility. Code is available at https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network.
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound
Authors: Oren Solomon, Regev Cohen, Yi Zhang, Yi Yang, He Qiong, Jianwen Luo, Ruud J. G. van Sloun, Yonina C. Eldar
Date: 2018-11-20 00:00:00
Tasks: Super-Resolution

Contrast enhanced ultrasound is a radiation-free imaging modality which uses
encapsulated gas microbubbles for improved visualization of the vascular bed
deep within the tissue. It has recently been used to enable imaging with
unprecedented subwavelength spatial resolution by relying on super-resolution
techniques. A typical preprocessing step in super-resolution ultrasound is to
separate the microbubble signal from the cluttering tissue signal. This step
has a crucial impact on the final image quality. Here, we propose a new
approach to clutter removal based on robust principle component analysis (PCA)
and deep learning. We begin by modeling the acquired contrast enhanced
ultrasound signal as a combination of a low rank and sparse components. This
model is used in robust PCA and was previously suggested in the context of
ultrasound Doppler processing and dynamic magnetic resonance imaging. We then
illustrate that an iterative algorithm based on this model exhibits improved
separation of microbubble signal from the tissue signal over commonly practiced
methods. Next, we apply the concept of deep unfolding to suggest a deep network
architecture tailored to our clutter filtering problem which exhibits improved
convergence speed and accuracy with respect to its iterative counterpart. We
compare the performance of the suggested deep network on both simulations and
in-vivo rat brain scans, with a commonly practiced deep-network architecture
and the fast iterative shrinkage algorithm, and show that our architecture
exhibits better image quality and contrast.
Geometry of Friston's active inference
Authors: Martin Biehl
Date: 2018-11-20 00:00:00

We reconstruct Karl Friston's active inference and give a geometrical
interpretation of it.
Event-based High Dynamic Range Image and Very High Frame Rate Video Generation using Conditional Generative Adversarial Networks
Authors: S. Mohammad Mostafavi I., Lin Wang, Yo-Sung Ho, Kuk-Jin Yoon
Date: 2018-11-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: Video Generation, Vocal Bursts Intensity Prediction

Event cameras have a lot of advantages over traditional cameras, such as low
latency, high temporal resolution, and high dynamic range. However, since the
outputs of event cameras are the sequences of asynchronous events overtime
rather than actual intensity images, existing algorithms could not be directly
applied. Therefore, it is demanding to generate intensity images from events
for other tasks. In this paper, we unlock the potential of event camera-based
conditional generative adversarial networks to create images/videos from an
adjustable portion of the event data stream. The stacks of space-time
coordinates of events are used as inputs and the network is trained to
reproduce images based on the spatio-temporal intensity changes. The usefulness
of event cameras to generate high dynamic range(HDR) images even in extreme
illumination conditions and also non blurred images under rapid motion is also
shown.In addition, the possibility of generating very high frame rate videos is
demonstrated, theoretically up to 1 million frames per second (FPS) since the
temporal resolution of event cameras are about 1{\mu}s. Proposed methods are
evaluated by comparing the results with the intensity images captured on the
same pixel grid-line of events using online available real datasets and
synthetic datasets produced by the event camera simulator.
Analytic Network Learning
Authors: Kar-Ann Toh
Date: 2018-11-20 00:00:00

Based on the property that solving the system of linear matrix equations via
the column space and the row space projections boils down to an approximation
in the least squares error sense, a formulation for learning the weight
matrices of the multilayer network can be derived. By exploiting into the vast
number of feasible solutions of these interdependent weight matrices, the
learning can be performed analytically layer by layer without needing of
gradient computation after an initialization. Possible initialization schemes
include utilizing the data matrix as initial weights and random initialization.
The study is followed by an investigation into the representation capability
and the output variance of the learning scheme. An extensive experimentation on
synthetic and real-world data sets validates its numerical feasibility.
Self Organizing Classifiers and Niched Fitness
Authors: Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata
Date: 2018-11-20 00:00:00

Learning classifier systems are adaptive learning systems which have been
widely applied in a multitude of application domains. However, there are still
some generalization problems unsolved. The hurdle is that fitness and niching
pressures are difficult to balance. Here, a new algorithm called Self
Organizing Classifiers is proposed which faces this problem from a different
perspective. Instead of balancing the pressures, both pressures are separated
and no balance is necessary. In fact, the proposed algorithm possesses a
dynamical population structure that self-organizes itself to better project the
input space into a map. The niched fitness concept is defined along with its
dynamical population structure, both are indispensable for the understanding of
the proposed method. Promising results are shown on two continuous multi-step
problems. One of which is yet more challenging than previous problems of this
class in the literature.
Self Organizing Classifiers: First Steps in Structured Evolutionary Machine Learning
Authors: Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata
Date: 2018-11-20 00:00:00
Tasks: BIG-bench Machine Learning

Learning classifier systems (LCSs) are evolutionary machine learning
algorithms, flexible enough to be applied to reinforcement, supervised and
unsupervised learning problems with good performance. Recently, self organizing
classifiers were proposed which are similar to LCSs but have the advantage that
in its structured population no balance between niching and fitness pressure is
necessary. However, more tests and analysis are required to verify its
benefits. Here, a variation of the first algorithm is proposed which uses a
parameterless self organizing map (SOM). This algorithm is applied in
challenging problems such as big, noisy as well as dynamically changing
continuous input-action mazes (growing and compressing mazes are included) with
good performance. Moreover, a genetic operator is proposed which utilizes the
topological information of the SOM's population structure, improving the
results. Thus, the first steps in structured evolutionary machine learning are
shown, nonetheless, the problems faced are more difficult than the state-of-art
continuous input-action multi-step ones.
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images
Authors: Ramanarayan Mohanty, SL Happy, Aurobinda Routray
Date: 2018-11-20 00:00:00
Tasks: Dimensionality Reduction, General Classification

Hyperspectral images (HSI) contain a wealth of information over hundreds of
contiguous spectral bands, making it possible to classify materials through
subtle spectral discrepancies. However, the classification of this rich
spectral information is accompanied by the challenges like high dimensionality,
singularity, limited training samples, lack of labeled data samples,
heteroscedasticity and nonlinearity. To address these challenges, we propose a
semi-supervised graph based dimensionality reduction method named
`semi-supervised spatial spectral regularized manifold local scaling cut'
(S3RMLSC). The underlying idea of the proposed method is to exploit the limited
labeled information from both the spectral and spatial domains along with the
abundant unlabeled samples to facilitate the classification task by retaining
the original distribution of the data. In S3RMLSC, a hierarchical guided filter
(HGF) is initially used to smoothen the pixels of the HSI data to preserve the
spatial pixel consistency. This step is followed by the construction of linear
patches from the nonlinear manifold by using the maximal linear patch (MLP)
criterion. Then the inter-patch and intra-patch dissimilarity matrices are
constructed in both spectral and spatial domains by regularized manifold local
scaling cut (RMLSC) and neighboring pixel manifold local scaling cut (NPMLSC)
respectively. Finally, we obtain the projection matrix by optimizing the
updated semi-supervised spatial-spectral between-patch and total-patch
dissimilarity. The effectiveness of the proposed DR algorithm is illustrated
with publicly available real-world HSI datasets.
Contingency Training
Authors: Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata
Date: 2018-11-20 00:00:00
Tasks: feature selection

When applied to high-dimensional datasets, feature selection algorithms might
still leave dozens of irrelevant variables in the dataset. Therefore, even
after feature selection has been applied, classifiers must be prepared to the
presence of irrelevant variables. This paper investigates a new training method
called Contingency Training which increases the accuracy as well as the
robustness against irrelevant attributes. Contingency training is classifier
independent. By subsampling and removing information from each sample, it
creates a set of constraints. These constraints aid the method to automatically
find proper importance weights of the dataset's features. Experiments are
conducted with the contingency training applied to neural networks over
traditional datasets as well as datasets with additional irrelevant variables.
For all of the tests, contingency training surpassed the unmodified training on
datasets with irrelevant variables and even outperformed slightly when only a
few or no irrelevant variables were present.
Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization
Authors: Christelle Marfaing, Alexandre Garcia
Date: 2018-11-20 00:00:00
Tasks: Active Learning, Fraud Detection

The automatic detection of frauds in banking transactions has been recently
studied as a way to help the analysts finding fraudulent operations. Due to the
availability of a human feedback, this task has been studied in the framework
of active learning: the fraud predictor is allowed to sequentially call on an
oracle. This human intervention is used to label new examples and improve the
classification accuracy of the latter. Such a setting is not adapted in the
case of fraud detection with financial data in European countries. Actually, as
a human verification is mandatory to consider a fraud as really detected, it is
not necessary to focus on improving the classifier. We introduce the setting of
'Computer-assisted fraud detection' where the goal is to minimize the number of
non fraudulent operations submitted to an oracle. The existing methods are
applied to this task and we show that a simple meta-algorithm provides
competitive results in this scenario on benchmark datasets.
Brain-Inspired Stigmergy Learning
Authors: Xing Hsu, Zhifeng Zhao, Rongpeng Li, Honggang Zhang
Date: 2018-11-20 00:00:00

Stigmergy has proved its great superiority in terms of distributed control,
robustness and adaptability, thus being regarded as an ideal solution for
large-scale swarm control problems. Based on new discoveries on astrocytes in
regulating synaptic transmission in the brain, this paper has mapped stigmergy
mechanism into the interaction between synapses and investigated its
characteristics and advantages. Particularly, we have divided the interaction
between synapses which are not directly connected into three phases and
proposed a stigmergic learning model. In this model, the state change of a
stigmergy agent will expand its influence to affect the states of others. The
strength of the interaction is determined by the level of neural activity as
well as the distance between stigmergy agents. Inspired by the morphological
and functional changes in astrocytes during environmental enrichment, it is
likely that the regulation of distance between stigmergy agents plays a
critical role in the stigmergy learning process. Simulation results have
verified its importance and indicated that the well-regulated distance between
stigmergy agents can help to obtain stigmergy learning gain.
CGNet: A Light-weight Context Guided Network for Semantic Segmentation
Authors: Tianyi Wu, Sheng Tang, Rui Zhang, Yongdong Zhang
Date: 2018-11-20 00:00:00
Tasks: Segmentation, Semantic Segmentation

The demand of applying semantic segmentation model on mobile devices has been
increasing rapidly. Current state-of-the-art networks have enormous amount of
parameters hence unsuitable for mobile devices, while other small memory
footprint models follow the spirit of classification network and ignore the
inherent characteristic of semantic segmentation. To tackle this problem, we
propose a novel Context Guided Network (CGNet), which is a light-weight and
efficient network for semantic segmentation. We first propose the Context
Guided (CG) block, which learns the joint feature of both local feature and
surrounding context, and further improves the joint feature with the global
context. Based on the CG block, we develop CGNet which captures contextual
information in all stages of the network and is specially tailored for
increasing segmentation accuracy. CGNet is also elaborately designed to reduce
the number of parameters and save memory footprint. Under an equivalent number
of parameters, the proposed CGNet significantly outperforms existing
segmentation networks. Extensive experiments on Cityscapes and CamVid datasets
verify the effectiveness of the proposed approach. Specifically, without any
post-processing and multi-scale testing, the proposed CGNet achieves 64.8% mean
IoU on Cityscapes with less than 0.5 M parameters. The source code for the
complete system can be found at https://github.com/wutianyiRosun/CGNet.
SpherePHD: Applying CNNs on a Spherical PolyHeDron Representation of 360 degree Images
Authors: Yeonkun Lee, Jaeseok Jeong, Jongseob Yun, Wonjune Cho, Kuk-Jin Yoon
Date: 2018-11-20 00:00:00
Tasks: Semantic Segmentation

Omni-directional cameras have many advantages overconventional cameras in
that they have a much wider field-of-view (FOV). Accordingly, several
approaches have beenproposed recently to apply convolutional neural
networks(CNNs) to omni-directional images for various visual tasks.However,
most of them use image representations defined inthe Euclidean space after
transforming the omni-directionalviews originally formed in the non-Euclidean
space. Thistransformation leads to shape distortion due to nonuniformspatial
resolving power and the loss of continuity. Theseeffects make existing
convolution kernels experience diffi-culties in extracting meaningful
information.This paper presents a novel method to resolve such prob-lems of
applying CNNs to omni-directional images. Theproposed method utilizes a
spherical polyhedron to rep-resent omni-directional views. This method
minimizes thevariance of the spatial resolving power on the sphere sur-face,
and includes new convolution and pooling methodsfor the proposed
representation. The proposed method canalso be adopted by any existing
CNN-based methods. Thefeasibility of the proposed method is demonstrated
throughclassification, detection, and semantic segmentation taskswith synthetic
and real datasets.
Orthographic Feature Transform for Monocular 3D Object Detection
Authors: Thomas Roddick, Alex Kendall, Roberto Cipolla
Date: 2018-11-20 00:00:00
Tasks: 3D Object Detection, 3D Object Detection From Monocular Images, Monocular 3D Object Detection, Object, object-detection, Object Detection

3D object detection from monocular images has proven to be an enormously
challenging task, with the performance of leading systems not yet achieving
even 10\% of that of LiDAR-based counterparts. One explanation for this
performance gap is that existing systems are entirely at the mercy of the
perspective image-based representation, in which the appearance and scale of
objects varies drastically with depth and meaningful distances are difficult to
infer. In this work we argue that the ability to reason about the world in 3D
is an essential element of the 3D object detection task. To this end, we
introduce the orthographic feature transform, which enables us to escape the
image domain by mapping image-based features into an orthographic 3D space.
This allows us to reason holistically about the spatial configuration of the
scene in a domain where scale is consistent and distances between objects are
meaningful. We apply this transformation as part of an end-to-end deep learning
architecture and achieve state-of-the-art performance on the KITTI 3D object
benchmark.\footnote{We will release full source code and pretrained models upon
acceptance of this manuscript for publication.
Analysing Results from AI Benchmarks: Key Indicators and How to Obtain Them
Authors: Fernando Martínez-Plumed, José Hernández-Orallo
Date: 2018-11-20 00:00:00
Tasks: AI Agent, Atari Games

Item response theory (IRT) can be applied to the analysis of the evaluation
of results from AI benchmarks. The two-parameter IRT model provides two
indicators (difficulty and discrimination) on the side of the item (or AI
problem) while only one indicator (ability) on the side of the respondent (or
AI agent). In this paper we analyse how to make this set of indicators dual, by
adding a fourth indicator, generality, on the side of the respondent.
Generality is meant to be dual to discrimination, and it is based on
difficulty. Namely, generality is defined as a new metric that evaluates
whether an agent is consistently good at easy problems and bad at difficult
ones. With the addition of generality, we see that this set of four key
indicators can give us more insight on the results of AI benchmarks. In
particular, we explore two popular benchmarks in AI, the Arcade Learning
Environment (Atari 2600 games) and the General Video Game AI competition. We
provide some guidelines to estimate and interpret these indicators for other AI
benchmarks and competitions.