pwc-archive/papers-with-abstracts huggingface dataset:
Network Classification Based Structural Analysis of Real Networks and their Model-Generated Counterparts
Authors: Marcell Nagy, Roland Molontay
Date: 2018-10-19 00:00:00
Tasks: BIG-bench Machine Learning, Graph Classification

Data-driven analysis of complex networks has been in the focus of research for decades. An important area of research is to study how well real networks can be described with a small selection of metrics, furthermore how well network models can capture the relations between graph metrics observed in real networks. In this paper, we apply machine learning techniques to investigate the aforementioned problems. We study 500 real-world networks along with 2,000 synthetic networks generated by four frequently used network models with previously calibrated parameters to make the generated graphs as similar to the real networks as possible. This paper unifies several branches of data-driven complex network analysis, such as the study of graph metrics and their pair-wise relationships, network similarity estimation, model calibration, and graph classification. We find that the correlation profiles of the structural measures significantly differ across network domains and the domain can be efficiently determined using a small selection of graph metrics. The structural properties of the network models with fixed parameters are robust enough to perform parameter calibration. The goodness-of-fit of the network models highly depends on the network domain. By solving classification problems, we find that the models lack the capability of generating a graph with a high clustering coefficient and relatively large diameter simultaneously. On the other hand, models are able to capture exactly the degree-distribution-related metrics.
A Framework for Robot Programming in Cobotic Environments: First user experiments
Authors: Ying Siu Liang, Damien Pellier, Humbert Fiorino, Sylvie Pesty
Date: 2018-10-19 00:00:00

The increasing presence of robots in industries has not gone unnoticed. Large
industrial players have incorporated them into their production lines, but
smaller companies hesitate due to high initial costs and the lack of
programming expertise. In this work we introduce a framework that combines two
disciplines, Programming by Demonstration and Automated Planning, to allow
users without any programming knowledge to program a robot. The user teaches
the robot atomic actions together with their semantic meaning and represents
them in terms of preconditions and effects. Using these atomic actions the
robot can generate action sequences autonomously to reach any goal given by the
user. We evaluated the usability of our framework in terms of user experiments
with a Baxter Research Robot and showed that it is well-adapted to users
without any programming experience.
Data analysis from empirical moments and the Christoffel function
Authors: Edouard Pauwels, Mihai Putinar, Jean-Bernard Lasserre
Date: 2018-10-19 00:00:00

Spectral features of the empirical moment matrix constitute a resourceful tool for unveiling properties of a cloud of points, among which, density, support and latent structures. It is already well known that the empirical moment matrix encodes a great deal of subtle attributes of the underlying measure. Starting from this object as base of observations we combine ideas from statistics, real algebraic geometry, orthogonal polynomials and approximation theory for opening new insights relevant for Machine Learning (ML) problems with data supported on singular sets. Refined concepts and results from real algebraic geometry and approximation theory are empowering a simple tool (the empirical moment matrix) for the task of solving non-trivial questions in data analysis. We provide (1) theoretical support, (2) numerical experiments and, (3) connections to real world data as a validation of the stamina of the empirical moment matrix approach.
Wikistat 2.0: Educational Resources for Artificial Intelligence
Authors: Philippe Besse, Brendan Guillouet, Béatrice Laurent
Date: 2018-09-28 00:00:00

Big data, data science, deep learning, artificial intelligence are the key
words of intense hype related with a job market in full evolution, that impose
to adapt the contents of our university professional trainings. Which
artificial intelligence is mostly concerned by the job offers? Which
methodologies and technologies should be favored in the training programs?
Which objectives, tools and educational resources do we needed to put in place
to meet these pressing needs? We answer these questions in describing the
contents and operational resources in the Data Science orientation of the
specialty Applied Mathematics at INSA Toulouse. We focus on basic mathematics
training (Optimization, Probability, Statistics), associated with the practical
implementation of the most performing statistical learning algorithms, with the
most appropriate technologies and on real examples. Considering the huge
volatility of the technologies, it is imperative to train students in
seft-training, this will be their technological watch tool when they will be in
professional activity. This explains the structuring of the educational site
github.com/wikistat into a set of tutorials. Finally, to motivate the thorough
practice of these tutorials, a serious game is organized each year in the form
of a prediction contest between students of Master degrees in Applied
Mathematics for IA.
Improving Fast Segmentation With Teacher-student Learning
Authors: Jiafeng Xie, Bing Shuai, Jian-Fang Hu, Jingyang Lin, Wei-Shi Zheng
Date: 2018-10-19 00:00:00
Tasks: Segmentation

Recently, segmentation neural networks have been significantly improved by
demonstrating very promising accuracies on public benchmarks. However, these
models are very heavy and generally suffer from low inference speed, which
limits their application scenarios in practice. Meanwhile, existing fast
segmentation models usually fail to obtain satisfactory segmentation accuracies
on public benchmarks. In this paper, we propose a teacher-student learning
framework that transfers the knowledge gained by a heavy and better performed
segmentation network (i.e. teacher) to guide the learning of fast segmentation
networks (i.e. student). Specifically, both zero-order and first-order
knowledge depicted in the fine annotated images and unlabeled auxiliary data
are transferred to regularize our student learning. The proposed method can
improve existing fast segmentation models without incurring extra computational
overhead, so it can still process images with the same fast speed. Extensive
experiments on the Pascal Context, Cityscape and VOC 2012 datasets demonstrate
that the proposed teacher-student learning framework is able to significantly
boost the performance of student network.
Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
Authors: Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
Date: 2018-10-19 00:00:00
Tasks: Change Detection, Earth Observation

The Copernicus Sentinel-2 program now provides multispectral images at a
global scale with a high revisit rate. In this paper we explore the usage of
convolutional neural networks for urban change detection using such
multispectral images. We first present the new change detection dataset that
was used for training the proposed networks, which will be openly available to
serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is
composed of pairs of multispectral aerial images, and the changes were manually
annotated at pixel level. We then propose two architectures to detect changes,
Siamese and Early Fusion, and compare the impact of using different numbers of
spectral channels as inputs. These architectures are trained from scratch using
the provided dataset.
Fully Convolutional Siamese Networks for Change Detection
Authors: Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch
Date: 2018-10-19 00:00:00
Tasks: Change Detection, Change detection for remote sensing images, Earth Observation, Video Object Tracking

This paper presents three fully convolutional neural network architectures
which perform change detection using a pair of coregistered images. Most
notably, we propose two Siamese extensions of fully convolutional networks
which use heuristics about the current problem to achieve the best results in
our tests on two open change detection datasets, using both RGB and
multispectral images. We show that our system is able to learn from scratch
using annotated change detection images. Our architectures achieve better
performance than previously proposed methods, while being at least 500 times
faster than related systems. This work is a step towards efficient processing
of data from large scale Earth observation systems such as Copernicus or
Landsat.
Planification par fusions incrémentales de graphes
Authors: Damien Pellier, lias. Belaidi
Date: 2018-10-19 00:00:00

In this paper, we introduce a generic and fresh model for distributed
planning called "Distributed Planning Through Graph Merging" ({\sf DPGM}). This
model unifies the different steps of the distributed planning process into a
single step. Our approach is based on a planning graph structure for the agent
reasoning and a CSP mechanism for the individual plan extraction and the
coordination. We assume that no agent can reach the global goal alone.
Therefore the agents must cooperate, {\it i.e.,} take in into account potential
positive interactions between their activities to reach their common shared
goal. The originality of our model consists in considering as soon as possible,
{\it i.e.,} in the individual planning process, the positive and the negative
interactions between agents activities in order to reduce the search cost of a
global coordinated solution plan.
Multitask Learning for Large-scale Semantic Change Detection
Authors: Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
Date: 2018-10-19 00:00:00
Tasks: Change Detection, Earth Observation

Change detection is one of the main problems in remote sensing, and is essential to the accurate processing and understanding of the large scale Earth observation data available through programs such as Sentinel and Landsat. Most of the recently proposed change detection methods bring deep learning to this context, but openly available change detection datasets are still very scarce, which limits the methods that can be proposed and tested. In this paper we present the first large scale high resolution semantic change detection (HRSCD) dataset, which enables the usage of deep learning methods for semantic change detection. The dataset contains coregistered RGB image pairs, pixel-wise change information and land cover information. We then propose several methods using fully convolutional neural networks to perform semantic change detection. Most notably, we present a network architecture that performs change detection and land cover mapping simultaneously, while using the predicted land cover information to help to predict changes. We also describe a sequential training scheme that allows this network to be trained without setting a hyperparameter that balances different loss functions and achieves the best overall results.
Coordinated exploration for labyrinthine environments with application to the Pursuit-Evasion problem
Authors: Damien Pellier, Humbert Fiorino
Date: 2018-10-19 00:00:00

This paper introduces a multirobot cooperation approach to solve the "pursuit
evasion" problem for mobile robots that have omnidirectional vision sensors.
The main characteristic of this approach is to implement a real cooperation
between robots based on knowledge sharing and makes them work as a team. A
complete algorithm for computing a motion strategy of robots is also presented.
This algorithm is based on searching critical points in the environment.
Finally, the deliberation protocol which distributes the exploration task among
the team and takes the best possible outcome from the robots resources is
presented.
Learning with privileged information via adversarial discriminative modality distillation
Authors: Nuno C. Garcia, Pietro Morerio, Vittorio Murino
Date: 2018-10-19 00:00:00
Tasks: Action Recognition, Hallucination

Heterogeneous data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while training data can be accurately collected to include a variety of sensory modalities, it is often the case that not all of them are available in real life (testing) scenarios, where a model has to be deployed. This raises the challenge of how to extract information from multimodal data in the training stage, in a form that can be exploited at test time, considering limitations such as noisy or missing modalities. This paper presents a new approach in this direction for RGB-D vision tasks, developed within the adversarial learning and privileged information frameworks. We consider the practical case of learning representations from depth and RGB videos, while relying only on RGB data at test time. We propose a new approach to train a hallucination network that learns to distill depth information via adversarial learning, resulting in a clean approach without several losses to balance or hyperparameters. We report state-of-the-art results on object classification on the NYUD dataset and video action recognition on the largest multimodal dataset available for this task, the NTU RGB+D, as well as on the Northwestern-UCLA.
Efficient Dependency-Guided Named Entity Recognition
Authors: Zhanming Jie, Aldrian Obaja Muis, Wei Lu
Date: 2018-10-19 00:00:00
Tasks: Event Extraction, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Relation Extraction

Named entity recognition (NER), which focuses on the extraction of
semantically meaningful named entities and their semantic classes from text,
serves as an indispensable component for several down-stream natural language
processing (NLP) tasks such as relation extraction and event extraction.
Dependency trees, on the other hand, also convey crucial semantic-level
information. It has been shown previously that such information can be used to
improve the performance of NER (Sasano and Kurohashi 2008, Ling and Weld 2012).
In this work, we investigate on how to better utilize the structured
information conveyed by dependency trees to improve the performance of NER.
Specifically, unlike existing approaches which only exploit dependency
information for designing local features, we show that certain global
structured information of the dependency trees can be exploited when building
NER models where such information can provide guided learning and inference.
Through extensive experiments, we show that our proposed novel
dependency-guided NER model performs competitively with models based on
conventional semi-Markov conditional random fields, while requiring
significantly less running time.
Assumption-Based Planning
Authors: Damien Pellier, Humbert Fiorino
Date: 2018-10-19 00:00:00

The purpose of the paper is to introduce a new approach of planning called
Assumption-Based Planning. This approach is a very interesting way to devise a
planner based on a multi-agent system in which the production of a global
shared plan is obtained by conjecture/refutation cycles. Contrary to classical
approaches, our contribution relies on the agents reasoning that leads to the
production of a plan from planning domains. To take into account complex
environments and the partial agents knowledge, we propose to consider the
planning problem as a defeasible reasoning where the agents exchange proposals
and counter-proposals and are able to reason about uncertainty. The
argumentation dialogue between agents must not be viewed as a negotiation
process but as an investigation process in order to build a plan. In this
paper, we focus on the mechanisms that allow an agent to produce `reasonable'
proposals according to its knowledge.
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images
Authors: Simon Ekström, Filip Malmberg, Håkan Ahlström, Joel Kullberg, Robin Strand
Date: 2018-10-19 00:00:00
Tasks: Combinatorial Optimization, Image Registration, Image Segmentation, Medical Image Analysis, Semantic Segmentation

Objective: Deformable image registration is a fundamental problem in medical
image analysis, with applications such as longitudinal studies, population
modeling, and atlas based image segmentation. Registration is often phrased as
an optimization problem, i.e., finding a deformation field that is optimal
according to a given objective function. Discrete, combinatorial, optimization
techniques have successfully been employed to solve the resulting optimization
problem. Specifically, optimization based on $\alpha$-expansion with minimal
graph cuts has been proposed as a powerful tool for image registration. The
high computational cost of the graph-cut based optimization approach, however,
limits the utility of this approach for registration of large volume images.
Methods: Here, we propose to accelerate graph-cut based deformable registration
by dividing the image into overlapping sub-regions and restricting the
$\alpha$-expansion moves to a single sub-region at a time. Results: We
demonstrate empirically that this approach can achieve a large reduction in
computation time -- from days to minutes -- with only a small penalty in terms
of solution quality. Conclusion: The reduction in computation time provided by
the proposed method makes graph cut based deformable registration viable for
large volume images. Significance: Graph cut based image registration has
previously been shown to produce excellent results, but the high computational
cost has hindered the adoption of the method for registration of large medical
volume images. Our proposed method lifts this restriction, requiring only a
small fraction of the computational cost to produce results of comparable
quality.
Block-Diagonal and LT Codes for Distributed Computing With Straggling Servers
Authors: Albin Severinson, Alexandre Graell i Amat, Eirik Rosnes
Date: 2017-12-21 00:00:00
Tasks: Distributed Computing

We propose two coded schemes for the distributed computing problem of
multiplying a matrix by a set of vectors. The first scheme is based on
partitioning the matrix into submatrices and applying maximum distance
separable (MDS) codes to each submatrix. For this scheme, we prove that up to a
given number of partitions the communication load and the computational delay
(not including the encoding and decoding delay) are identical to those of the
scheme recently proposed by Li et al., based on a single, long MDS code.
However, due to the use of shorter MDS codes, our scheme yields a significantly
lower overall computational delay when the delay incurred by encoding and
decoding is also considered. We further propose a second coded scheme based on
Luby Transform (LT) codes under inactivation decoding. Interestingly, LT codes
may reduce the delay over the partitioned scheme at the expense of an increased
communication load. We also consider distributed computing under a deadline and
show numerically that the proposed schemes outperform other schemes in the
literature, with the LT code-based scheme yielding the best performance for the
scenarios considered.
ScratchDet: Training Single-Shot Object Detectors from Scratch
Authors: Rui Zhu, Shifeng Zhang, Xiaobo Wang, Longyin Wen, Hailin Shi, Liefeng Bo, Tao Mei
Date: 2018-10-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: General Classification, Object, object-detection, Object Detection

Current state-of-the-art object objectors are fine-tuned from the off-the-shelf networks pretrained on large-scale classification dataset ImageNet, which incurs some additional problems: 1) The classification and detection have different degrees of sensitivity to translation, resulting in the learning objective bias; 2) The architecture is limited by the classification network, leading to the inconvenience of modification. To cope with these problems, training detectors from scratch is a feasible solution. However, the detectors trained from scratch generally perform worse than the pretrained ones, even suffer from the convergence issue in training. In this paper, we explore to train object detectors from scratch robustly. By analysing the previous work on optimization landscape, we find that one of the overlooked points in current trained-from-scratch detector is the BatchNorm. Resorting to the stable and predictable gradient brought by BatchNorm, detectors can be trained from scratch stably while keeping the favourable performance independent to the network architecture. Taking this advantage, we are able to explore various types of networks for object detection, without suffering from the poor convergence. By extensive experiments and analyses on downsampling factor, we propose the Root-ResNet backbone network, which makes full use of the information from original images. Our ScratchDet achieves the state-of-the-art accuracy on PASCAL VOC 2007, 2012 and MS COCO among all the train-from-scratch detectors and even performs better than several one-stage pretrained methods. Codes will be made publicly available at https://github.com/KimSoybean/ScratchDet.
CVABS: Moving Object Segmentation with Common Vector Approach for Videos
Authors: Şahin Işık, Kemal Özkan, Ömer Nezih Gerek
Date: 2018-10-19 00:00:00
Tasks: Self-Learning, Semantic Segmentation

Background modelling is a fundamental step for several real-time computer
vision applications that requires security systems and monitoring. An accurate
background model helps detecting activity of moving objects in the video. In
this work, we have developed a new subspace based background modelling
algorithm using the concept of Common Vector Approach with Gram-Schmidt
orthogonalization. Once the background model that involves the common
characteristic of different views corresponding to the same scene is acquired,
a smart foreground detection and background updating procedure is applied based
on dynamic control parameters. A variety of experiments is conducted on
different problem types related to dynamic backgrounds. Several types of
metrics are utilized as objective measures and the obtained visual results are
judged subjectively. It was observed that the proposed method stands
successfully for all problem types reported on CDNet2014 dataset by updating
the background frames with a self-learning feedback mechanism.