pwc-archive/papers-with-abstracts huggingface dataset:
Consistent Generative Query Networks
Authors: Ananya Kumar, S. M. Ali Eslami, Danilo J. Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan
Date: 2018-07-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: 3D Scene Reconstruction, Video Prediction

Stochastic video prediction models take in a sequence of image frames, and
generate a sequence of consecutive future image frames. These models typically
generate future frames in an autoregressive fashion, which is slow and requires
the input and output frames to be consecutive. We introduce a model that
overcomes these drawbacks by generating a latent representation from an
arbitrary set of frames that can then be used to simultaneously and efficiently
sample temporally consistent frames at arbitrary time-points. For example, our
model can "jump" and directly sample frames at the end of the video, without
sampling intermediate frames. Synthetic video evaluations confirm substantial
gains in speed and functionality without loss in fidelity. We also apply our
framework to a 3D scene reconstruction dataset. Here, our model is conditioned
on camera location and can sample consistent sets of images for what an
occluded region of a 3D scene might look like, even if there are multiple
possibilities for what that region might contain. Reconstructions and videos
are available at https://bit.ly/2O4Pc4R.
Reflection Analysis for Face Morphing Attack Detection
Authors: Clemens Seibold, Anna Hilsmann, Peter Eisert
Date: 2018-07-05 00:00:00
Tasks: Face Morphing Attack Detection, MORPH

A facial morph is a synthetically created image of a face that looks similar
to two different individuals and can even trick biometric facial recognition
systems into recognizing both individuals. This attack is known as face
morphing attack. The process of creating such a facial morph is well documented
and a lot of tutorials and software to create them are freely available.
Therefore, it is mandatory to be able to detect this kind of fraud to ensure
the integrity of the face as reliable biometric feature. In this work, we study
the effects of face morphing on the physically correctness of the illumination.
We estimate the direction to the light sources based on specular highlights in
the eyes and use them to generate a synthetic map for highlights on the skin.
This map is compared with the highlights in the image that is suspected to be a
fraud. Morphing faces with different geometries, a bad alignment of the source
images or using images with different illuminations, can lead to
inconsistencies in reflections that indicate the existence of a morphing
attack.
Detection and Analysis of Content Creator Collaborations in YouTube Videos using Face- and Speaker-Recognition
Authors: Moritz Lode, Michael Örtl, Christian Koch, Amr Rizk, Ralf Steinmetz
Date: 2018-07-05 00:00:00
Tasks: Active Speaker Detection, Face Recognition, Speaker Recognition

This work discusses and implements the application of speaker recognition for
the detection of collaborations in YouTube videos. CATANA, an existing
framework for detection and analysis of YouTube collaborations, is utilizing
face recognition for the detection of collaborators, which naturally performs
poor on video-content without appearing faces. This work proposes an extension
of CATANA using active speaker detection and speaker recognition to improve the
detection accuracy.
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Authors: Paul Bergmann, Sindy Löwe, Michael Fauser, David Sattlegger, Carsten Steger
Date: 2018-07-05 00:00:00
Tasks: Segmentation

Convolutional autoencoders have emerged as popular methods for unsupervised
defect segmentation on image data. Most commonly, this task is performed by
thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance.
This procedure, however, leads to large residuals whenever the reconstruction
encompasses slight localization inaccuracies around edges. It also fails to
reveal defective regions that have been visually altered when intensity values
stay roughly consistent. We show that these problems prevent these approaches
from being applied to complex real-world scenarios and that it cannot be easily
avoided by employing more elaborate architectures such as variational or
feature matching autoencoders. We propose to use a perceptual loss function
based on structural similarity which examines inter-dependencies between local
image regions, taking into account luminance, contrast and structural
information, instead of simply comparing single pixel values. It achieves
significant performance gains on a challenging real-world dataset of
nanofibrous materials and a novel dataset of two woven fabrics over the state
of the art approaches for unsupervised defect segmentation that use pixel-wise
reconstruction error metrics.
Calamari - A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition
Authors: Christoph Wick, Christian Reul, Frank Puppe
Date: 2018-07-05 00:00:00
Tasks: GPU, Optical Character Recognition, Optical Character Recognition (OCR)

Optical Character Recognition (OCR) on contemporary and historical data is
still in the focus of many researchers. Especially historical prints require
book specific trained OCR models to achieve applicable results (Springmann and
L\"udeling, 2016, Reul et al., 2017a). To reduce the human effort for manually
annotating ground truth (GT) various techniques such as voting and pretraining
have shown to be very efficient (Reul et al., 2018a, Reul et al., 2018b).
Calamari is a new open source OCR line recognition software that both uses
state-of-the art Deep Neural Networks (DNNs) implemented in Tensorflow and
giving native support for techniques such as pretraining and voting. The
customizable network architectures constructed of Convolutional Neural Networks
(CNNS) and Long-ShortTerm-Memory (LSTM) layers are trained by the so-called
Connectionist Temporal Classification (CTC) algorithm of Graves et al. (2006).
Optional usage of a GPU drastically reduces the computation times for both
training and prediction. We use two different datasets to compare the
performance of Calamari to OCRopy, OCRopus3, and Tesseract 4. Calamari reaches
a Character Error Rate (CER) of 0.11% on the UW3 dataset written in modern
English and 0.18% on the DTA19 dataset written in German Fraktur, which
considerably outperforms the results of the existing softwares.
Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products
Authors: Patrick Follmann, Bertram Drost, Tobias Böttger
Date: 2018-07-05 00:00:00
Tasks: Instance Segmentation, Object, Semantic Segmentation, Weakly-supervised instance segmentation

Grocery stores have thousands of products that are usually identified using
barcodes with a human in the loop. For automated checkout systems, it is
necessary to count and classify the groceries efficiently and robustly. One
possibility is to use a deep learning algorithm for instance-aware semantic
segmentation. Such methods achieve high accuracies but require a large amount
of annotated training data.
  We propose a system to generate the training annotations in a weakly
supervised manner, drastically reducing the labeling effort. We assume that for
each training image, only the object class is known. The system automatically
segments the corresponding object from the background. The obtained training
data is augmented to simulate variations similar to those seen in real-world
setups.
Revisiting Perspective Information for Efficient Crowd Counting
Authors: Miaojing Shi, Zhaohui Yang, Chao Xu, Qijun Chen
Date: 2018-07-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Crowd Counting, regression

Crowd counting is the task of estimating people numbers in crowd images.
Modern crowd counting methods employ deep neural networks to estimate crowd
counts via crowd density regressions. A major challenge of this task lies in
the perspective distortion, which results in drastic person scale change in an
image. Density regression on the small person area is in general very hard. In
this work, we propose a perspective-aware convolutional neural network (PACNN)
for efficient crowd counting, which integrates the perspective information into
density regression to provide additional knowledge of the person scale change
in an image. Ground truth perspective maps are firstly generated for training;
PACNN is then specifically designed to predict multi-scale perspective maps,
and encode them as perspective-aware weighting layers in the network to
adaptively combine the outputs of multi-scale density maps. The weights are
learned at every pixel of the maps such that the final density combination is
robust to the perspective distortion. We conduct extensive experiments on the
ShanghaiTech, WorldExpo'10, UCF_CC_50, and UCSD datasets, and demonstrate the
effectiveness and efficiency of PACNN over the state-of-the-art.
Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network
Authors: Aram Ter-Sarkisov, Robert Ross, John Kelleher, Bernadette Earley, Michael Keane
Date: 2018-07-05 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

We present an instance segmentation algorithm trained and applied to a CCTV
recording of beef cattle during a winter finishing period. A fully
convolutional network was transformed into an instance segmentation network
that learns to label each instance of an animal separately. We introduce a
conceptually simple framework that the network uses to output a single
prediction for every animal. These results are a contribution towards behaviour
analysis in winter finishing beef cattle for early detection of animal
welfare-related problems.
Arcades: A deep model for adaptive decision making in voice controlled smart-home
Authors: Alexis Brenon, François Portet, Michel Vacher
Date: 2018-07-05 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In a voice-controlled smart-home, a controller must respond not only to
user's requests but also according to the interaction context. This paper
describes Arcades, a system which uses deep reinforcement learning to extract
context from a graphical representation of home automation system and to update
continuously its behavior to the user's one. This system is robust to changes
in the environment (sensor breakdown or addition) through its graphical
representation (scale well) and the reinforcement mechanism (adapt well). The
experiments on realistic data demonstrate that this method promises to reach
long life context-aware control of smart-home.
Variational Bayesian dropout: pitfalls and fixes
Authors: Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani
Date: 2018-07-05 00:00:00
Proceeding: ICML 2018 7

Dropout, a stochastic regularisation technique for training of neural
networks, has recently been reinterpreted as a specific type of approximate
inference algorithm for Bayesian neural networks. The main contribution of the
reinterpretation is in providing a theoretical framework useful for analysing
and extending the algorithm. We show that the proposed framework suffers from
several issues; from undefined or pathological behaviour of the true posterior
related to use of improper priors, to an ill-defined variational objective due
to singularity of the approximating distribution relative to the true
posterior. Our analysis of the improper log uniform prior used in variational
Gaussian dropout suggests the pathologies are generally irredeemable, and that
the algorithm still works only because the variational formulation annuls some
of the pathologies. To address the singularity issue, we proffer Quasi-KL (QKL)
divergence, a new approximate inference objective for approximation of
high-dimensional distributions. We show that motivations for variational
Bernoulli dropout based on discretisation and noise have QKL as a limit.
Properties of QKL are studied both theoretically and on a simple practical
example which shows that the QKL-optimal approximation of a full rank Gaussian
with a degenerate one naturally leads to the Principal Component Analysis
solution.
Open Logo Detection Challenge
Authors: Hang Su, Xiatian Zhu, Shaogang Gong
Date: 2018-07-05 00:00:00

Existing logo detection benchmarks consider artificial deployment scenarios
by assuming that large training data with fine-grained bounding box annotations
for each class are available for model training. Such assumptions are often
invalid in realistic logo detection scenarios where new logo classes come
progressively and require to be detected with little or none budget for
exhaustively labelling fine-grained training data for every new class. Existing
benchmarks are thus unable to evaluate the true performance of a logo detection
method in realistic and open deployments. In this work, we introduce a more
realistic and challenging logo detection setting, called Open Logo Detection.
Specifically, this new setting assumes fine-grained labelling only on a small
proportion of logo classes whilst the remaining classes have no labelled
training data to simulate the open deployment. We further create an open logo
detection benchmark, called OpenLogo,to promote the investigation of this new
challenge. OpenLogo contains 27,083 images from 352 logo classes, built by
aggregating/refining 7 existing datasets and establishing an open logo
detection evaluation protocol. To address this challenge, we propose a Context
Adversarial Learning (CAL) approach to synthesising training data with coherent
logo instance appearance against diverse background context for enabling more
effective optimisation of contemporary deep learning detection models.
Experiments show the performance advantage of CAL over existing
state-of-the-art alternative methods on the more realistic and challenging
OpenLogo benchmark.
Model-free Consensus Maximization for Non-Rigid Shapes
Authors: Thomas Probst, Ajad Chhatkuli, Danda Pani Paudel, Luc van Gool
Date: 2018-07-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: model

Many computer vision methods use consensus maximization to relate
measurements containing outliers with the correct transformation model. In the
context of rigid shapes, this is typically done using Random Sampling and
Consensus (RANSAC) by estimating an analytical model that agrees with the
largest number of measurements (inliers). However, small parameter models may
not be always available. In this paper, we formulate the model-free consensus
maximization as an Integer Program in a graph using `rules' on measurements. We
then provide a method to solve it optimally using the Branch and Bound (BnB)
paradigm. We focus its application on non-rigid shapes, where we apply the
method to remove outlier 3D correspondences and achieve performance superior to
the state of the art. Our method works with outlier ratio as high as 80\%. We
further derive a similar formulation for 3D template to image matching,
achieving similar or better performance compared to the state of the art.
A Boo(n) for Evaluating Architecture Performance
Authors: Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst
Date: 2018-07-05 00:00:00
Proceeding: ICML 2018 7

We point out important problems with the common practice of using the best
single model performance for comparing deep learning architectures, and we
propose a method that corrects these flaws. Each time a model is trained, one
gets a different result due to random factors in the training process, which
include random parameter initialization and random data shuffling. Reporting
the best single model performance does not appropriately address this
stochasticity. We propose a normalized expected best-out-of-$n$ performance
($\text{Boo}_n$) as a way to correct these problems.
Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks
Authors: Georgios Papoudakis, Kyriakos C. Chatzidimitriou, Pericles A. Mitkas
Date: 2018-07-05 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Game of Doom, Open-Ended Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent developments in deep reinforcement learning have enabled the creation
of agents for solving a large variety of games given a visual input. These
methods have been proven successful for 2D games, like the Atari games, or for
simple tasks, like navigating in mazes. It is still an open question, how to
address more complex environments, in which the reward is sparse and the state
space is huge. In this paper we propose a divide and conquer deep reinforcement
learning solution and we test our agent in the first person shooter (FPS) game
of Doom. Our work is based on previous works in deep reinforcement learning and
in Doom agents. We also present how our agent is able to perform better in
unknown environments compared to a state of the art reinforcement learning
algorithm.
Deeply-Sparse Signal rePresentations ($\text{D}\text{S}^2\text{P}$)
Authors: Demba Ba
Date: 2018-07-05 00:00:00
Tasks: Dictionary Learning

A recent line of work shows that a deep neural network with ReLU nonlinearities arises from a finite sequence of cascaded sparse coding models, the outputs of which, except for the last element in the cascade, are sparse and unobservable. That is, intermediate outputs deep in the cascade are sparse, hence the title of this manuscript. We show here, using techniques from the dictionary learning literature that, if the measurement matrices in the cascaded sparse coding model (a) satisfy RIP and (b) all have sparse columns except for the last, they can be recovered with high probability. We propose two algorithms for this purpose: one that recovers the matrices in a forward sequence, and another that recovers them in a backward sequence. The method of choice in deep learning to solve this problem is by training an auto-encoder. Our algorithms provide a sound alternative, with theoretical guarantees, as well upper bounds on sample complexity. The theory shows that the learning complexity of the forward algorithm depends on the number of hidden units at the deepest layer and the number of active neurons at that layer (sparsity). In addition, the theory relates the number of hidden units in successive layers, thus giving a practical prescription for designing deep ReLU neural networks. Because it puts fewer restrictions on the architecture, the backward algorithm requires more data. We demonstrate the deep dictionary learning algorithm via simulations. Finally, we use a coupon-collection argument to conjecture a lower bound on sample complexity that gives some insight as to why deep networks require more data to train than shallow ones.
Neural Language Codes for Multilingual Acoustic Models
Authors: Markus Müller, Sebastian Stüker, Alex Waibel
Date: 2018-07-05 00:00:00
Tasks: speech-recognition, Speech Recognition

Multilingual Speech Recognition is one of the most costly AI problems,
because each language (7,000+) and even different accents require their own
acoustic models to obtain best recognition performance. Even though they all
use the same phoneme symbols, each language and accent imposes its own coloring
or "twang". Many adaptive approaches have been proposed, but they require
further training, additional data and generally are inferior to monolingually
trained models. In this paper, we propose a different approach that uses a
large multilingual model that is \emph{modulated} by the codes generated by an
ancillary network that learns to code useful differences between the "twangs"
or human language.
  We use Meta-Pi networks to have one network (the language code net) gate the
activity of neurons in another (the acoustic model nets). Our results show that
during recognition multilingual Meta-Pi networks quickly adapt to the proper
language coloring without retraining or new data, and perform better than
monolingually trained networks. The model was evaluated by training acoustic
modeling nets and modulating language code nets jointly and optimize them for
best recognition performance.
Lattice based Conceptual Spaces to Explore Cognitive Functionalities for Prosthetic Arm
Authors: M. S. Ishwarya, Aswani Kumar Cherukuri
Date: 2018-07-05 00:00:00

Upper limb Prosthetic can be viewed as an independent cognitive system in
order to develop a conceptual space. In this paper, we provide a detailed
analogical reasoning of prosthetic arm to build the conceptual spaces with the
help of the theory called geometric framework of conceptual spaces proposed by
Gardenfors. Terminologies of conceptual spaces such as concepts, similarities,
properties, quality dimensions and prototype are applied for a specific
prosthetic system and conceptual space is built for prosthetic arm. Concept
lattice traversals are used on the lattice represented conceptual spaces.
Cognitive functionalities such as generalization (Similarities) and
specialization (Differences) are achieved in the lattice represented conceptual
space. This might well prove to design intelligent prosthetics to assist
challenged humans. Geometric framework of conceptual spaces holds similar
concepts closer in geometric structures in a way similar to concept lattices.
Hence, we also propose to use concept lattice to represent concepts of
geometric framework of conceptual spaces. Also, we extend our discussion with
our insights on conceptual spaces of bidirectional hand prosthetics.
Subpixel-Precise Tracking of Rigid Objects in Real-time
Authors: Tobias Böttger, Markus Ulrich, Carsten Steger
Date: 2018-07-05 00:00:00
Tasks: Object, Object Tracking, Position

We present a novel object tracking scheme that can track rigid objects in
real time. The approach uses subpixel-precise image edges to track objects with
high accuracy. It can determine the object position, scale, and rotation with
subpixel-precision at around 80fps. The tracker returns a reliable score for
each frame and is capable of self diagnosing a tracking failure. Furthermore,
the choice of the similarity measure makes the approach inherently robust
against occlusion, clutter, and nonlinear illumination changes. We evaluate the
method on sequences from rigid objects from the OTB-2015 and VOT2016 dataset
and discuss its performance. The evaluation shows that the tracker is more
accurate than state-of-the-art real-time trackers while being equally robust.
Volumetric performance capture from minimal camera viewpoints
Authors: Andrew Gilbert, Marco Volino, John Collomosse, Adrian Hilton
Date: 2018-07-05 00:00:00
Proceeding: ECCV 2018 9

We present a convolutional autoencoder that enables high fidelity volumetric
reconstructions of human performance to be captured from multi-view video
comprising only a small set of camera views. Our method yields similar
end-to-end reconstruction error to that of a probabilistic visual hull computed
using significantly more (double or more) viewpoints. We use a deep prior
implicitly learned by the autoencoder trained over a dataset of view-ablated
multi-view video footage of a wide range of subjects and actions. This opens up
the possibility of high-end volumetric performance capture in on-set and
prosumer scenarios where time or cost prohibit a high witness camera count.
Multi-robot Path Planning in Well-formed Infrastructures: Prioritized Planning vs. Prioritized Wait Adjustment (Preliminary Results)
Authors: Anton Andreychuk, Konstantin Yakovlev
Date: 2018-07-05 00:00:00

We study the problem of planning collision-free paths for a group of
homogeneous robots. We propose a novel approach for turning the paths that were
planned egocentrically by the robots, e.g. without taking other robots' moves
into account, into collision-free trajectories and evaluate it empirically.
Suggested algorithm is much faster (up to one order of magnitude) than
state-of-the-art but this comes at the price of notable drop-down of the
solution cost.