pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Authors: Lisa M. Koch, Martin Rajchl, Wenjia Bai, Christian F. Baumgartner, Tong Tong, Jonathan Passerat-Palmbach, Paul Aljabar, Daniel Rueckert
Date: 2016-04-29 00:00:00
Tasks: Cardiac Segmentation, Medical Image Analysis, Segmentation

Multi-atlas segmentation is a widely used tool in medical image analysis,
providing robust and accurate results by learning from annotated atlas
datasets. However, the availability of fully annotated atlas images for
training is limited due to the time required for the labelling task.
Segmentation methods requiring only a proportion of each atlas image to be
labelled could therefore reduce the workload on expert raters tasked with
annotating atlas images. To address this issue, we first re-examine the
labelling problem common in many existing approaches and formulate its solution
in terms of a Markov Random Field energy minimisation problem on a graph
connecting atlases and the target image. This provides a unifying framework for
multi-atlas segmentation. We then show how modifications in the graph
configuration of the proposed framework enable the use of partially annotated
atlas images and investigate different partial annotation strategies. The
proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets
for hippocampal and cardiac segmentation. Experiments were performed aimed at
(1) recreating existing segmentation techniques with the proposed framework and
(2) demonstrating the potential of employing sparsely annotated atlas data for
multi-atlas segmentation.
deepMiRGene: Deep Neural Network based Precursor microRNA Prediction
Authors: Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon
Date: 2016-04-29 00:00:00
Tasks: Feature Engineering, Prediction

Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene
regulation, miRNA identification is one of the most essential problems in
computational biology. miRNAs are usually short in length ranging between 20
and 23 base pairs. It is thus often difficult to distinguish miRNA-encoding
sequences from other non-coding RNAs and pseudo miRNAs that have a similar
length, and most previous studies have recommended using precursor miRNAs
instead of mature miRNAs for robust detection. A great number of conventional
machine-learning-based classification methods have been proposed, but they
often have the serious disadvantage of requiring manual feature engineering,
and their performance is limited as well. In this paper, we propose a novel
miRNA precursor prediction algorithm, deepMiRGene, based on recurrent neural
networks, specifically long short-term memory networks. deepMiRGene
automatically learns suitable features from the data themselves without manual
feature engineering and constructs a model that can successfully reflect
structural characteristics of precursor miRNAs. For the performance evaluation
of our approach, we have employed several widely used evaluation metrics on
three recent benchmark datasets and verified that deepMiRGene delivered
comparable performance among the current state-of-the-art tools.
Predicting the direction of stock market prices using random forest
Authors: Luckyson Khaidem, Snehanshu Saha, Sudeepa Roy Dey
Date: 2016-04-29 00:00:00
Tasks: BIG-bench Machine Learning, Ensemble Learning

Predicting trends in stock market prices has been an area of interest for
researchers for many years due to its complex and dynamic nature. Intrinsic
volatility in stock market across the globe makes the task of prediction
challenging. Forecasting and diffusion modeling, although effective can't be
the panacea to the diverse range of problems encountered in prediction,
short-term or otherwise. Market risk, strongly correlated with forecasting
errors, needs to be minimized to ensure minimal risk in investment. The authors
propose to minimize forecasting error by treating the forecasting problem as a
classification problem, a popular suite of algorithms in Machine learning. In
this paper, we propose a novel way to minimize the risk of investment in stock
market by predicting the returns of a stock using a class of powerful machine
learning algorithms known as ensemble learning. Some of the technical
indicators such as Relative Strength Index (RSI), stochastic oscillator etc are
used as inputs to train our model. The learning model used is an ensemble of
multiple decision trees. The algorithm is shown to outperform existing algo-
rithms found in the literature. Out of Bag (OOB) error estimates have been
found to be encouraging. Key Words: Random Forest Classifier, stock price
forecasting, Exponential smoothing, feature extraction, OOB error and
convergence.
Confidence driven TGV fusion
Authors: Valsamis Ntouskos, Fiora Pirri
Date: 2016-03-30 00:00:00

We introduce a novel model for spatially varying variational data fusion,
driven by point-wise confidence values. The proposed model allows for the joint
estimation of the data and the confidence values based on the spatial coherence
of the data. We discuss the main properties of the introduced model as well as
suitable algorithms for estimating the solution of the corresponding biconvex
minimization problem and their convergence. The performance of the proposed
model is evaluated considering the problem of depth image fusion by using both
synthetic and real data from publicly available datasets.
Faster R-CNN Features for Instance Search
Authors: Amaia Salvador, Xavier Giro-i-Nieto, Ferran Marques, Shin'ichi Satoh
Date: 2016-04-29 00:00:00
Tasks: Instance Search, object-detection, Object Detection, Region Proposal, Reranking, Retrieval

Image representations derived from pre-trained Convolutional Neural Networks
(CNNs) have become the new state of the art in computer vision tasks such as
instance retrieval. This work explores the suitability for instance retrieval
of image- and region-wise representations pooled from an object detection CNN
such as Faster R-CNN. We take advantage of the object proposals learned by a
Region Proposal Network (RPN) and their associated CNN features to build an
instance search pipeline composed of a first filtering stage followed by a
spatial reranking. We further investigate the suitability of Faster R-CNN
features when the network is fine-tuned for the same objects one wants to
retrieve. We assess the performance of our proposed system with the Oxford
Buildings 5k, Paris Buildings 6k and a subset of TRECVid Instance Search 2013,
achieving competitive results.
Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables
Authors: Nils Y. Hammerla, Shane Halloran, Thomas Ploetz
Date: 2016-04-29 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Human activity recognition (HAR) in ubiquitous computing is beginning to
adopt deep learning to substitute for well-established analysis techniques that
rely on hand-crafted feature extraction and classification techniques. From
these isolated applications of custom deep architectures it is, however,
difficult to gain an overview of their suitability for problems ranging from
the recognition of manipulative gestures to the segmentation and identification
of physical activities like running or ascending stairs. In this paper we
rigorously explore deep, convolutional, and recurrent approaches across three
representative datasets that contain movement data captured with wearable
sensors. We describe how to train recurrent approaches in this setting,
introduce a novel regularisation approach, and illustrate how they outperform
the state-of-the-art on a large benchmark dataset. Across thousands of
recognition experiments with randomly sampled model configurations we
investigate the suitability of each model for different tasks in HAR, explore
the impact of hyperparameters using the fANOVA framework, and provide
guidelines for the practitioner who wants to apply deep learning in their
problem setting.
Joint Sound Source Separation and Speaker Recognition
Authors: Jeroen Zegers, Hugo Van hamme
Date: 2016-04-29 00:00:00
Tasks: blind source separation, Speaker Recognition

Non-negative Matrix Factorization (NMF) has already been applied to learn
speaker characterizations from single or non-simultaneous speech for speaker
recognition applications. It is also known for its good performance in (blind)
source separation for simultaneous speech. This paper explains how NMF can be
used to jointly solve the two problems in a multichannel speaker recognizer for
simultaneous speech. It is shown how state-of-the-art multichannel NMF for
blind source separation can be easily extended to incorporate speaker
recognition. Experiments on the CHiME corpus show that this method outperforms
the sequential approach of first applying source separation, followed by
speaker recognition that uses state-of-the-art i-vector techniques.
Improved Dense Trajectory with Cross Streams
Authors: Katsunori Ohnishi, Masatoshi Hidaka, Tatsuya Harada
Date: 2016-04-29 00:00:00
Tasks: Action Classification, Action Recognition, Temporal Action Localization

Improved dense trajectories (iDT) have shown great performance in action
recognition, and their combination with the two-stream approach has achieved
state-of-the-art performance. It is, however, difficult for iDT to completely
remove background trajectories from video with camera shaking. Trajectories in
less discriminative regions should be given modest weights in order to create
more discriminative local descriptors for action recognition. In addition, the
two-stream approach, which learns appearance and motion information separately,
cannot focus on motion in important regions when extracting features from
spatial convolutional layers of the appearance network, and vice versa. In
order to address the above mentioned problems, we propose a new local
descriptor that pools a new convolutional layer obtained from crossing two
networks along iDT. This new descriptor is calculated by applying
discriminative weights learned from one network to a convolutional layer of the
other network. Our method has achieved state-of-the-art performance on ordinal
action recognition datasets, 92.3% on UCF101, and 66.2% on HMDB51.
Effective Backscatter Approximation for Photometry in Murky Water
Authors: Chourmouzios Tsiotsios, Maria E. Angelopoulou, Andrew J. Davison, Tae-Kyun Kim
Date: 2016-04-29 00:00:00
Tasks: Image Restoration

Shading-based approaches like Photometric Stereo assume that the image
formation model can be effectively optimized for the scene normals. However, in
murky water this is a very challenging problem. The light from artificial
sources is not only reflected by the scene but it is also scattered by the
medium particles, yielding the backscatter component. Backscatter corresponds
to a complex term with several unknown variables, and makes the problem of
normal estimation hard. In this work, we show that instead of trying to
optimize the complex backscatter model or use previous unrealistic
simplifications, we can approximate the per-pixel backscatter signal directly
from the captured images. Our method is based on the observation that
backscatter is saturated beyond a certain distance, i.e. it becomes scene-depth
independent, and finally corresponds to a smoothly varying signal which depends
strongly on the light position with respect to each pixel. Our backscatter
approximation method facilitates imaging and scene reconstruction in murky
water when the illumination is artificial as in Photometric Stereo.
Specifically, we show that it allows accurate scene normal estimation and
offers potentials like single image restoration. We evaluate our approach using
numerical simulations and real experiments within both the controlled
environment of a big water-tank and real murky port-waters.
Towards Conceptual Compression
Authors: Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra
Date: 2016-04-29 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Image Generation

We introduce a simple recurrent variational auto-encoder architecture that
significantly improves image modeling. The system represents the
state-of-the-art in latent variable models for both the ImageNet and Omniglot
datasets. We show that it naturally separates global conceptual information
from lower level details, thus addressing one of the fundamentally desired
properties of unsupervised learning. Furthermore, the possibility of
restricting ourselves to storing only global information about an image allows
us to achieve high quality 'conceptual compression'.
Supervisory Control for Behavior Composition
Authors: Paolo Felli, Nitin Yadav, Sebastian Sardina
Date: 2016-04-29 00:00:00

We relate behavior composition, a synthesis task studied in AI, to
supervisory control theory from the discrete event systems field. In
particular, we show that realizing (i.e., implementing) a target behavior
module (e.g., a house surveillance system) by suitably coordinating a
collection of available behaviors (e.g., automatic blinds, doors, lights,
cameras, etc.) amounts to imposing a supervisor onto a special discrete event
system. Such a link allows us to leverage on the solid foundations and
extensive work on discrete event systems, including borrowing tools and ideas
from that field. As evidence of that we show how simple it is to introduce
preferences in the mapped framework.
Summary Transfer: Exemplar-based Subset Selection for Video Summarization
Authors: Ke Zhang, Wei-Lun Chao, Fei Sha, Kristen Grauman
Date: 2016-03-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: Video Summarization

Video summarization has unprecedented importance to help us digest, browse,
and search today's ever-growing video collections. We propose a novel subset
selection technique that leverages supervision in the form of human-created
summaries to perform automatic keyframe-based video summarization. The main
idea is to nonparametrically transfer summary structures from annotated videos
to unseen test videos. We show how to extend our method to exploit semantic
side information about the video's category/genre to guide the transfer process
by those training videos semantically consistent with the test input. We also
show how to generalize our method to subshot-based summarization, which not
only reduces computational costs but also provides more flexible ways of
defining visual similarity across subshots spanning several frames. We conduct
extensive evaluation on several benchmarks and demonstrate promising results,
outperforming existing methods in several settings.
Music transcription modelling and composition using deep learning
Authors: Bob L. Sturm, João Felipe Santos, Oded Ben-Tal, Iryna Korshunova
Date: 2016-04-29 00:00:00
Tasks: Deep Learning, Descriptive, Music Transcription

We apply deep learning methods, specifically long short-term memory (LSTM)
networks, to music transcription modelling and composition. We build and train
LSTM networks using approximately 23,000 music transcriptions expressed with a
high-level vocabulary (ABC notation), and use them to generate new
transcriptions. Our practical aim is to create music transcription models
useful in particular contexts of music composition. We present results from
three perspectives: 1) at the population level, comparing descriptive
statistics of the set of training transcriptions and generated transcriptions;
2) at the individual level, examining how a generated transcription reflects
the conventions of a music practice in the training transcriptions (Celtic
folk); 3) at the application level, using the system for idea generation in
music composition. We make our datasets, software and sound examples open and
available: \url{https://github.com/IraKorshunova/folk-rnn}.
Learning Compact Structural Representations for Audio Events Using Regressor Banks
Authors: Huy Phan, Marco Maass, Lars Hertel, Radoslaw Mazur, Ian McLoughlin, Alfred Mertins
Date: 2016-04-29 00:00:00
Tasks: General Classification

We introduce a new learned descriptor for audio signals which is efficient
for event representation. The entries of the descriptor are produced by
evaluating a set of regressors on the input signal. The regressors are
class-specific and trained using the random regression forests framework. Given
an input signal, each regressor estimates the onset and offset positions of the
target event. The estimation confidence scores output by a regressor are then
used to quantify how the target event aligns with the temporal structure of the
corresponding category. Our proposed descriptor has two advantages. First, it
is compact, i.e. the dimensionality of the descriptor is equal to the number of
event classes. Second, we show that even simple linear classification models,
trained on our descriptor, yield better accuracies on audio event
classification task than not only the nonlinear baselines but also the
state-of-the-art results.
Volumetric and Multi-View CNNs for Object Classification on 3D Data
Authors: Charles R. Qi, Hao Su, Matthias Niessner, Angela Dai, Mengyuan Yan, Leonidas J. Guibas
Date: 2016-04-12 00:00:00
Proceeding: CVPR 2016 6
Tasks: 3D Object Recognition, 3D Point Cloud Classification, General Classification

3D shape models are becoming widely available and easier to capture, making
available 3D information crucial for progress in object classification. Current
state-of-the-art methods rely on CNNs to address this problem. Recently, we
witness two types of CNNs being developed: CNNs based upon volumetric
representations versus CNNs based upon multi-view representations. Empirical
results from these two types of CNNs exhibit a large gap, indicating that
existing volumetric CNN architectures and approaches are unable to fully
exploit the power of 3D representations. In this paper, we aim to improve both
volumetric CNNs and multi-view CNNs according to extensive analysis of existing
approaches. To this end, we introduce two distinct network architectures of
volumetric CNNs. In addition, we examine multi-view CNNs, where we introduce
multi-resolution filtering in 3D. Overall, we are able to outperform current
state-of-the-art methods for both volumetric CNNs and multi-view CNNs. We
provide extensive experiments designed to evaluate underlying design choices,
thus providing a better understanding of the space of methods available for
object classification on 3D data.
Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow
Authors: Kean Ming Tan, Zhaoran Wang, Han Liu, Tong Zhang
Date: 2016-04-29 00:00:00
Tasks: Dimensionality Reduction

Sparse generalized eigenvalue problem (GEP) plays a pivotal role in a large
family of high-dimensional statistical models, including sparse Fisher's
discriminant analysis, canonical correlation analysis, and sufficient dimension
reduction. Sparse GEP involves solving a non-convex optimization problem. Most
existing methods and theory in the context of specific statistical models that
are special cases of the sparse GEP require restrictive structural assumptions
on the input matrices. In this paper, we propose a two-stage computational
framework to solve the sparse GEP. At the first stage, we solve a convex
relaxation of the sparse GEP. Taking the solution as an initial value, we then
exploit a nonconvex optimization perspective and propose the truncated Rayleigh
flow method (Rifle) to estimate the leading generalized eigenvector. We show
that Rifle converges linearly to a solution with the optimal statistical rate
of convergence for many statistical models. Theoretically, our method
significantly improves upon the existing literature by eliminating structural
assumptions on the input matrices for both stages. To achieve this, our
analysis involves two key ingredients: (i) a new analysis of the gradient based
method on nonconvex objective functions, and (ii) a fine-grained
characterization of the evolution of sparsity patterns along the solution path.
Thorough numerical studies are provided to validate the theoretical results.
Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps
Authors: Biyun Sheng, Chunhua Shen, Guosheng Lin, Jun Li, Wankou Yang, Changyin Sun
Date: 2016-04-29 00:00:00
Tasks: Attribute, Crowd Counting

Crowd counting is an important task in computer vision, which has many
applications in video surveillance. Although the regression-based framework has
achieved great improvements for crowd counting, how to improve the
discriminative power of image representation is still an open problem.
Conventional holistic features used in crowd counting often fail to capture
semantic attributes and spatial cues of the image. In this paper, we propose
integrating semantic information into learning locality-aware feature sets for
accurate crowd counting. First, with the help of convolutional neural network
(CNN), the original pixel space is mapped onto a dense attribute feature map,
where each dimension of the pixel-wise feature indicates the probabilistic
strength of a certain semantic class. Then, locality-aware features (LAF) built
on the idea of spatial pyramids on neighboring patches are proposed to explore
more spatial context and local information. Finally, the traditional VLAD
encoding method is extended to a more generalized form in which diverse
coefficient weights are taken into consideration. Experimental results validate
the effectiveness of our presented method.
On the representation and embedding of knowledge bases beyond binary relations
Authors: Jianfeng Wen, Jian-Xin Li, Yongyi Mao, Shini Chen, Richong Zhang
Date: 2016-04-28 00:00:00

The models developed to date for knowledge base embedding are all based on
the assumption that the relations contained in knowledge bases are binary. For
the training and testing of these embedding models, multi-fold (or n-ary)
relational data are converted to triples (e.g., in FB15K dataset) and
interpreted as instances of binary relations. This paper presents a canonical
representation of knowledge bases containing multi-fold relations. We show that
the existing embedding models on the popular FB15K datasets correspond to a
sub-optimal modelling framework, resulting in a loss of structural information.
We advocate a novel modelling framework, which models multi-fold relations
directly using this canonical representation. Using this framework, the
existing TransH model is generalized to a new model, m-TransH. We demonstrate
experimentally that m-TransH outperforms TransH by a large margin, thereby
establishing a new state of the art.
DCTNet and PCANet for acoustic signal feature extraction
Authors: Yin Xian, Andrew Thompson, Xiaobai Sun, Douglas Nowacek, Loren Nolte
Date: 2016-04-28 00:00:00
Tasks: Classification, General Classification

We introduce the use of DCTNet, an efficient approximation and alternative to
PCANet, for acoustic signal classification. In PCANet, the eigenfunctions of
the local sample covariance matrix (PCA) are used as filterbanks for
convolution and feature extraction. When the eigenfunctions are well
approximated by the Discrete Cosine Transform (DCT) functions, each layer of of
PCANet and DCTNet is essentially a time-frequency representation. We relate
DCTNet to spectral feature representation methods, such as the the short time
Fourier transform (STFT), spectrogram and linear frequency spectral
coefficients (LFSC). Experimental results on whale vocalization data show that
DCTNet improves classification rate, demonstrating DCTNet's applicability to
signal processing problems such as underwater acoustics.