pwc-archive/papers-with-abstracts huggingface dataset:
Co-localization with Category-Consistent Features and Geodesic Distance Propagation
Authors: Hieu Le, Chen-Ping Yu, Gregory Zelinsky, Dimitris Samaras
Date: 2016-12-10 00:00:00
Tasks: image-classification, Image Classification, Object, Object Discovery, Object Localization

Co-localization is the problem of localizing objects of the same class using only the set of images that contain them. This is a challenging task because the object detector must be built without negative examples that can lead to more informative supervision signals. The main idea of our method is to cluster the feature space of a generically pre-trained CNN, to find a set of CNN features that are consistently and highly activated for an object category, which we call category-consistent CNN features. Then, we propagate their combined activation map using superpixel geodesic distances for co-localization. In our first set of experiments, we show that the proposed method achieves state-of-the-art performance on three related benchmarks: PASCAL 2007, PASCAL-2012, and the Object Discovery dataset. We also show that our method is able to detect and localize truly unseen categories, on six held-out ImageNet categories with accuracy that is significantly higher than previous state-of-the-art. Our intuitive approach achieves this success without any region proposals or object detectors and can be based on a CNN that was pre-trained purely on image classification tasks without further fine-tuning.
Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation
Authors: Zhiguang Wang, Jianbo Yang
Date: 2017-03-31 00:00:00
Tasks: Diabetic Retinopathy Detection

We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection. The visual-interpretable feature of the proposed method is achieved by adding the regression activation map (RAM) after the global averaging pooling layer of the convolutional networks (CNN). With RAM, the proposed model can localize the discriminative regions of an retina image to show the specific region of interest in terms of its severity level. We believe this advantage of the proposed deep learning model is highly desired for DR detection because in practice, users are not only interested with high prediction performance, but also keen to understand the insights of DR detection and why the adopted learning model works. In the experiments conducted on a large scale of retina image dataset, we show that the proposed CNN model can achieve high performance on DR detection compared with the state-of-the-art while achieving the merits of providing the RAM to highlight the salient regions of the input image.
A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination
Authors: H. Sebastian Seung, Jonathan Zung
Date: 2017-04-03 00:00:00

Much has been learned about plasticity of biological synapses from empirical
studies. Hebbian plasticity is driven by correlated activity of presynaptic and
postsynaptic neurons. Synapses that converge onto the same neuron often behave
as if they compete for a fixed resource; some survive the competition while
others are eliminated. To provide computational interpretations of these
aspects of synaptic plasticity, we formulate unsupervised learning as a
zero-sum game between Hebbian excitation and anti-Hebbian inhibition in a
neural network model. The game formalizes the intuition that Hebbian excitation
tries to maximize correlations of neurons with their inputs, while anti-Hebbian
inhibition tries to decorrelate neurons from each other. We further include a
model of synaptic competition, which enables a neuron to eliminate all
connections except those from its most strongly correlated inputs. Through
empirical studies, we show that this facilitates the learning of sensory
features that resemble parts of objects.
Local nearest neighbour classification with applications to semi-supervised learning
Authors: Timothy I. Cannings, Thomas B. Berrett, Richard J. Samworth
Date: 2017-04-03 00:00:00
Tasks: Classification, General Classification

We derive a new asymptotic expansion for the global excess risk of a local-$k$-nearest neighbour classifier, where the choice of $k$ may depend upon the test point. This expansion elucidates conditions under which the dominant contribution to the excess risk comes from the decision boundary of the optimal Bayes classifier, but we also show that if these conditions are not satisfied, then the dominant contribution may arise from the tails of the marginal distribution of the features. Moreover, we prove that, provided the $d$-dimensional marginal distribution of the features has a finite $\rho$th moment for some $\rho > 4$ (as well as other regularity conditions), a local choice of $k$ can yield a rate of convergence of the excess risk of $O(n^{-4/(d+4)})$, where $n$ is the sample size, whereas for the standard $k$-nearest neighbour classifier, our theory would require $d \geq 5$ and $\rho > 4d/(d-4)$ finite moments to achieve this rate. These results motivate a new $k$-nearest neighbour classifier for semi-supervised learning problems, where the unlabelled data are used to obtain an estimate of the marginal feature density, and fewer neighbours are used for classification when this density estimate is small. Our worst-case rates are complemented by a minimax lower bound, which reveals that the local, semi-supervised $k$-nearest neighbour classifier attains the minimax optimal rate over our classes for the excess risk, up to a subpolynomial factor in $n$. These theoretical improvements over the standard $k$-nearest neighbour classifier are also illustrated through a simulation study.
Semi-Supervised Generation with Cluster-aware Generative Models
Authors: Lars Maaløe, Marco Fraccaro, Ole Winther
Date: 2017-04-03 00:00:00
Tasks: Clustering, General Classification

Deep generative models trained with large amounts of unlabelled data have
proven to be powerful within the domain of unsupervised learning. Many real
life data sets contain a small amount of labelled data points, that are
typically disregarded when training generative models. We propose the
Cluster-aware Generative Model, that uses unlabelled information to infer a
latent representation that models the natural clustering of the data, and
additional labelled data points to refine this clustering. The generative
performances of the model significantly improve when labelled information is
exploited, obtaining a log-likelihood of -79.38 nats on permutation invariant
MNIST, while also achieving competitive semi-supervised classification
accuracies. The model can also be trained fully unsupervised, and still improve
the log-likelihood performance with respect to related methods.
LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks
Authors: Luca Caltagirone, Mauro Bellone, Lennart Svensson, Mattias Wahde
Date: 2017-03-27 00:00:00
Tasks: Scene Parsing

In this work, a novel learning-based approach has been developed to generate
driving paths by integrating LIDAR point clouds, GPS-IMU information, and
Google driving directions. The system is based on a fully convolutional neural
network that jointly learns to carry out perception and path generation from
real-world driving sequences and that is trained using automatically generated
training examples. Several combinations of input data were tested in order to
assess the performance gain provided by specific information modalities. The
fully convolutional neural network trained using all the available sensors
together with driving directions achieved the best MaxF score of 88.13% when
considering a region of interest of 60x60 meters. By considering a smaller
region of interest, the agreement between predicted paths and ground-truth
increased to 92.60%. The positive results obtained in this work indicate that
the proposed system may help fill the gap between low-level scene parsing and
behavior-reflex approaches by generating outputs that are close to vehicle
control and at the same time human-interpretable.
L$^3$-SVMs: Landmarks-based Linear Local Support Vectors Machines
Authors: Valentina Zantedeschi, Rémi Emonet, Marc Sebban
Date: 2017-03-01 00:00:00
Tasks: Dimensionality Reduction

For their ability to capture non-linearities in the data and to scale to
large training sets, local Support Vector Machines (SVMs) have received a
special attention during the past decade. In this paper, we introduce a new
local SVM method, called L$^3$-SVMs, which clusters the input space, carries
out dimensionality reduction by projecting the data on landmarks, and jointly
learns a linear combination of local models. Simple and effective, our
algorithm is also theoretically well-founded. Using the framework of Uniform
Stability, we show that our SVM formulation comes with generalization
guarantees on the true risk. The experiments based on the simplest
configuration of our model (i.e. landmarks randomly selected, linear
projection, linear kernel) show that L$^3$-SVMs is very competitive w.r.t. the
state of the art and opens the door to new exciting lines of research.
Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages
Authors: Raj Dabre, Fabien Cromieres, Sadao Kurohashi
Date: 2017-02-20 00:00:00
Proceeding: MTSummit 2017 9
Tasks: Machine Translation, NMT, Sentence, Translation

In this paper, we explore a simple solution to "Multi-Source Neural Machine
Translation" (MSNMT) which only relies on preprocessing a N-way multilingual
corpus without modifying the Neural Machine Translation (NMT) architecture or
training procedure. We simply concatenate the source sentences to form a single
long multi-source input sentence while keeping the target side sentence as it
is and train an NMT system using this preprocessed corpus. We evaluate our
method in resource poor as well as resource rich settings and show its
effectiveness (up to 4 BLEU using 2 source languages and up to 6 BLEU using 5
source languages). We also compare against existing methods for MSNMT and show
that our solution gives competitive results despite its simplicity. We also
provide some insights on how the NMT system leverages multilingual information
in such a scenario by visualizing attention.
3D Object Reconstruction from Hand-Object Interactions
Authors: Dimitrios Tzionas, Juergen Gall
Date: 2017-04-03 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D Object Reconstruction, 3D Reconstruction, Articles, Object, Object Reconstruction

Recent advances have enabled 3d object reconstruction approaches using a
single off-the-shelf RGB-D camera. Although these approaches are successful for
a wide range of object classes, they rely on stable and distinctive geometric
or texture features. Many objects like mechanical parts, toys, household or
decorative articles, however, are textureless and characterized by minimalistic
shapes that are simple and symmetric. Existing in-hand scanning systems and 3d
reconstruction techniques fail for such symmetric objects in the absence of
highly distinctive features. In this work, we show that extracting 3d hand
motion for in-hand scanning effectively facilitates the reconstruction of even
featureless and highly symmetric objects and we present an approach that fuses
the rich additional information of hands into a 3d reconstruction pipeline,
significantly contributing to the state-of-the-art of in-hand scanning.
Predicting the Category and Attributes of Visual Search Targets Using Deep Gaze Pooling
Authors: Hosnieh Sattar, Andreas Bulling, Mario Fritz
Date: 2016-11-27 00:00:00

Predicting the target of visual search from eye fixation (gaze) data is a
challenging problem with many applications in human-computer interaction. In
contrast to previous work that has focused on individual instances as a search
target, we propose the first approach to predict categories and attributes of
search targets based on gaze data. However, state of the art models for
categorical recognition, in general, require large amounts of training data,
which is prohibitive for gaze data. To address this challenge, we propose a
novel Gaze Pooling Layer that integrates gaze information into CNN-based
architectures as an attention mechanism - incorporating both spatial and
temporal aspects of human gaze behavior. We show that our approach is effective
even when the gaze pooling layer is added to an already trained CNN, thus
eliminating the need for expensive joint data collection of visual and gaze
data. We propose an experimental setup and data set and demonstrate the
effectiveness of our method for search target prediction based on gaze
behavior. We further study how to integrate temporal and spatial gaze
information most effectively, and indicate directions for future research in
the gaze-based prediction of mental states.
Block-Matching Convolutional Neural Network for Image Denoising
Authors: Byeongyong Ahn, Nam Ik Cho
Date: 2017-04-03 00:00:00
Tasks: Denoising, Image Denoising

There are two main streams in up-to-date image denoising algorithms:
non-local self similarity (NSS) prior based methods and convolutional neural
network (CNN) based methods. The NSS based methods are favorable on images with
regular and repetitive patterns while the CNN based methods perform better on
irregular structures. In this paper, we propose a block-matching convolutional
neural network (BMCNN) method that combines NSS prior and CNN. Initially,
similar local patches in the input image are integrated into a 3D block. In
order to prevent the noise from messing up the block matching, we first apply
an existing denoising algorithm on the noisy image. The denoised image is
employed as a pilot signal for the block matching, and then denoising function
for the block is learned by a CNN structure. Experimental results show that the
proposed BMCNN algorithm achieves state-of-the-art performance. In detail,
BMCNN can restore both repetitive and irregular structures.
Detecting Violent and Abnormal Crowd activity using Temporal Analysis of Grey Level Co-occurrence Matrix (GLCM) Based Texture Measures
Authors: Kaelon Lloyd, David Marshall, Simon C. Moore, Paul L. Rosin
Date: 2016-05-17 00:00:00

The severity of sustained injury resulting from assault-related violence can
be minimised by reducing detection time. However, it has been shown that human
operators perform poorly at detecting events found in video footage when
presented with simultaneous feeds. We utilise computer vision techniques to
develop an automated method of abnormal crowd detection that can aid a human
operator in the detection of violent behaviour. We observed that behaviour in
city centre environments often occur in crowded areas, resulting in individual
actions being occluded by other crowd members. We propose a real-time
descriptor that models crowd dynamics by encoding changes in crowd texture
using temporal summaries of Grey Level Co-Occurrence Matrix (GLCM) features. We
introduce a measure of inter-frame uniformity (IFU) and demonstrate that the
appearance of violent behaviour changes in a less uniform manner when compared
to other types of crowd behaviour. Our proposed method is computationally cheap
and offers real-time description. Evaluating our method using a privately held
CCTV dataset and the publicly available Violent Flows, UCF Web Abnormality, and
UMN Abnormal Crowd datasets, we report a receiver operating characteristic
score of 0.9782, 0.9403, 0.8218 and 0.9956 respectively.
Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points
Authors: Dimitrios Tzionas, Abhilash Srikantha, Pablo Aponte, Juergen Gall
Date: 2017-04-03 00:00:00
Tasks: Pose Tracking

Hand motion capture has been an active research topic in recent years,
following the success of full-body pose tracking. Despite similarities, hand
tracking proves to be more challenging, characterized by a higher
dimensionality, severe occlusions and self-similarity between fingers. For this
reason, most approaches rely on strong assumptions, like hands in isolation or
expensive multi-camera systems, that limit the practical use. In this work, we
propose a framework for hand tracking that can capture the motion of two
interacting hands using only a single, inexpensive RGB-D camera. Our approach
combines a generative model with collision detection and discriminatively
learned salient points. We quantitatively evaluate our approach on 14 new
sequences with challenging interactions.
Truncating Wide Networks using Binary Tree Architectures
Authors: Yan Zhang, Mete Ozay, Shuo-Hao Li, Takayuki Okatani
Date: 2017-04-03 00:00:00
Proceeding: ICCV 2017 10
Tasks: General Classification, image-classification, Image Classification

Recent study shows that a wide deep network can obtain accuracy comparable to
a deeper but narrower network. Compared to narrower and deeper networks, wide
networks employ relatively less number of layers and have various important
benefits, such that they have less running time on parallel computing devices,
and they are less affected by gradient vanishing problems. However, the
parameter size of a wide network can be very large due to use of large width of
each layer in the network. In order to keep the benefits of wide networks
meanwhile improve the parameter size and accuracy trade-off of wide networks,
we propose a binary tree architecture to truncate architecture of wide networks
by reducing the width of the networks. More precisely, in the proposed
architecture, the width is continuously reduced from lower layers to higher
layers in order to increase the expressive capacity of network with a less
increase on parameter size. Also, to ease the gradient vanishing problem,
features obtained at different layers are concatenated to form the output of
our architecture. By employing the proposed architecture on a baseline wide
network, we can construct and train a new network with same depth but
considerably less number of parameters. In our experimental analyses, we
observe that the proposed architecture enables us to obtain better parameter
size and accuracy trade-off compared to baseline networks using various
benchmark image classification datasets. The results show that our model can
decrease the classification error of baseline from 20.43% to 19.22% on
Cifar-100 using only 28% of parameters that baseline has. Code is available at
https://github.com/ZhangVision/bitnet.
Convolutional neural networks for segmentation and object detection of human semen
Authors: Malte Stær Nissen, Oswin Krause, Kristian Almstrup, Søren Kjærulff, Torben Trindkær Nielsen, Mads Nielsen
Date: 2017-04-03 00:00:00
Tasks: object-detection, Object Detection

We compare a set of convolutional neural network (CNN) architectures for the
task of segmenting and detecting human sperm cells in an image taken from a
semen sample. In contrast to previous work, samples are not stained or washed
to allow for full sperm quality analysis, making analysis harder due to
clutter. Our results indicate that training on full images is superior to
training on patches when class-skew is properly handled. Full image training
including up-sampling during training proves to be beneficial in deep CNNs for
pixel wise accuracy and detection performance. Predicted sperm cells are found
by using connected components on the CNN predictions. We investigate
optimization of a threshold parameter on the size of detected components. Our
best network achieves 93.87% precision and 91.89% recall on our test dataset
after thresholding outperforming a classical mage analysis approach.
A Comparison of Directional Distances for Hand Pose Estimation
Authors: Dimitrios Tzionas, Juergen Gall
Date: 2017-04-03 00:00:00
Tasks: Benchmarking, Hand Pose Estimation, Pose Estimation

Benchmarking methods for 3d hand tracking is still an open problem due to the
difficulty of acquiring ground truth data. We introduce a new dataset and
benchmarking protocol that is insensitive to the accumulative error of other
protocols. To this end, we create testing frame pairs of increasing difficulty
and measure the pose estimation error separately for each of them. This
approach gives new insights and allows to accurately study the performance of
each feature or method without employing a full tracking pipeline. Following
this protocol, we evaluate various directional distances in the context of
silhouette-based 3d hand tracking, expressed as special cases of a generalized
Chamfer distance form. An appropriate parameter setup is proposed for each of
them, and a comparative study reveals the best performing method in this
context.
Multilabel Classification with R Package mlr
Authors: Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl
Date: 2017-03-27 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

We implemented several multilabel classification algorithms in the machine
learning package mlr. The implemented methods are binary relevance, classifier
chains, nested stacking, dependent binary relevance and stacking, which can be
used with any base learner that is accessible in mlr. Moreover, there is access
to the multilabel classification versions of randomForestSRC and rFerns. All
these methods can be easily compared by different implemented multilabel
performance measures and resampling methods in the standardized mlr framework.
In a benchmark experiment with several multilabel datasets, the performance of
the different methods is evaluated.
Propagators and Solvers for the Algebra of Modular Systems
Authors: Bart Bogaerts, Eugenia Ternovska, David Mitchell
Date: 2016-06-27 00:00:00

To appear in the proceedings of LPAR 21.
  Solving complex problems can involve non-trivial combinations of distinct
knowledge bases and problem solvers. The Algebra of Modular Systems is a
knowledge representation framework that provides a method for formally
specifying such systems in purely semantic terms. Formally, an expression of
the algebra defines a class of structures. Many expressive formalism used in
practice solve the model expansion task, where a structure is given on the
input and an expansion of this structure in the defined class of structures is
searched (this practice overcomes the common undecidability problem for
expressive logics). In this paper, we construct a solver for the model
expansion task for a complex modular systems from an expression in the algebra
and black-box propagators or solvers for the primitive modules. To this end, we
define a general notion of propagators equipped with an explanation mechanism,
an extension of the alge- bra to propagators, and a lazy conflict-driven
learning algorithm. The result is a framework for seamlessly combining solving
technology from different domains to produce a solver for a combined system.
Scalable Bayesian Rule Lists
Authors: Hongyu Yang, Cynthia Rudin, Margo Seltzer
Date: 2016-02-27 00:00:00
Proceeding: ICML 2017 8
Tasks: Computational Efficiency

We present an algorithm for building probabilistic rule lists that is two
orders of magnitude faster than previous work. Rule list algorithms are
competitors for decision tree algorithms. They are associative classifiers, in
that they are built from pre-mined association rules. They have a logical
structure that is a sequence of IF-THEN rules, identical to a decision list or
one-sided decision tree. Instead of using greedy splitting and pruning like
decision tree algorithms, we fully optimize over rule lists, striking a
practical balance between accuracy, interpretability, and computational speed.
The algorithm presented here uses a mixture of theoretical bounds (tight enough
to have practical implications as a screening or bounding procedure),
computational reuse, and highly tuned language libraries to achieve
computational efficiency. Currently, for many practical problems, this method
achieves better accuracy and sparsity than decision trees; further, in many
cases, the computational time is practical and often less than that of decision
trees. The result is a probabilistic classifier (which estimates P(y = 1|x) for
each x) that optimizes the posterior of a Bayesian hierarchical model over rule
lists.