pwc-archive/papers-with-abstracts huggingface dataset:
Recognition in Terra Incognita
Authors: Sara Beery, Grant van Horn, Pietro Perona
Date: 2018-07-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Classification, General Classification

It is desirable for detection and classification algorithms to generalize to
unfamiliar environments, but suitable benchmarks for quantitatively studying
this phenomenon are not yet available. We present a dataset designed to measure
recognition generalization to novel environments. The images in our dataset are
harvested from twenty camera traps deployed to monitor animal populations.
Camera traps are fixed at one location, hence the background changes little
across images; capture is triggered automatically, hence there is no human
bias. The challenge is learning recognition in a handful of locations, and
generalizing animal detection and classification to new locations where no
training data is available. In our experiments state-of-the-art algorithms show
excellent performance when tested at the same location where they were trained.
However, we find that generalization to new locations is poor, especially for
classification systems.
Single Bitmap Block Truncation Coding of Color Images Using Hill Climbing Algorithm
Authors: Lige Zhang, Xiaolin Qin, Qing Li, Haoyue Peng, Yu Hou
Date: 2018-07-13 00:00:00

Recently, the use of digital images in various fields is increasing rapidly.
To increase the number of images stored and get faster transmission of them, it
is necessary to reduce the size of these images. Single bitmap block truncation
coding (SBBTC) schemes are compression techniques, which are used to generate a
common bitmap to quantize the R, G and B planes in color image. As one of the
traditional SBBTC schemes, weighted plane (W-plane) method is famous for its
simplicity and low time consumption. However, the W-plane method also has poor
performance in visual quality. This paper proposes an improved SBBTC scheme
based on W-plane method using parallel computing and hill climbing algorithm.
Compared with various schemes, the simulation results of the proposed scheme
are better than that of the reference schemes in visual quality and time
consumption.
Deep Learning in the Wild
Authors: Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, Stefan Lörwald, Benjamin Bruno Meier, Katharina Rombach, Lukas Tuggener
Date: 2018-07-13 00:00:00
Tasks: Deep Learning

Deep learning with neural networks is applied by an increasing number of
people outside of classic research environments, due to the vast success of the
methodology on a wide range of machine perception tasks. While this interest is
fueled by beautiful success stories, practical work in deep learning on novel
tasks without existing baselines remains challenging. This paper explores the
specific challenges arising in the realm of real world tasks, based on case
studies from research \& development in conjunction with industry, and extracts
lessons learned from them. It thus fills a gap between the publication of
latest algorithmic and methodical developments, and the usually omitted
nitty-gritty of how to make them work. Specifically, we give insight into deep
learning projects on face matching, print media monitoring, industrial quality
control, music scanning, strategy game playing, and automated machine learning,
thereby providing best practices for deep learning in practice.
Non-Gaussian Component Analysis using Entropy Methods
Authors: Navin Goyal, Abhishek Shetty
Date: 2018-07-13 00:00:00
Tasks: Dimensionality Reduction

Non-Gaussian component analysis (NGCA) is a problem in multidimensional data
analysis which, since its formulation in 2006, has attracted considerable
attention in statistics and machine learning. In this problem, we have a random
variable $X$ in $n$-dimensional Euclidean space. There is an unknown subspace
$\Gamma$ of the $n$-dimensional Euclidean space such that the orthogonal
projection of $X$ onto $\Gamma$ is standard multidimensional Gaussian and the
orthogonal projection of $X$ onto $\Gamma^{\perp}$, the orthogonal complement
of $\Gamma$, is non-Gaussian, in the sense that all its one-dimensional
marginals are different from the Gaussian in a certain metric defined in terms
of moments. The NGCA problem is to approximate the non-Gaussian subspace
$\Gamma^{\perp}$ given samples of $X$.
  Vectors in $\Gamma^{\perp}$ correspond to `interesting' directions, whereas
vectors in $\Gamma$ correspond to the directions where data is very noisy. The
most interesting applications of the NGCA model is for the case when the
magnitude of the noise is comparable to that of the true signal, a setting in
which traditional noise reduction techniques such as PCA don't apply directly.
NGCA is also related to dimension reduction and to other data analysis problems
such as ICA. NGCA-like problems have been studied in statistics for a long time
using techniques such as projection pursuit.
  We give an algorithm that takes polynomial time in the dimension $n$ and has
an inverse polynomial dependence on the error parameter measuring the angle
distance between the non-Gaussian subspace and the subspace output by the
algorithm. Our algorithm is based on relative entropy as the contrast function
and fits under the projection pursuit framework. The techniques we develop for
analyzing our algorithm maybe of use for other related problems.
Sequential sampling of Gaussian process latent variable models
Authors: Martin Tegner, Benjamin Bloem-Reddy, Stephen Roberts
Date: 2018-07-13 00:00:00

We consider the problem of inferring a latent function in a probabilistic
model of data. When dependencies of the latent function are specified by a
Gaussian process and the data likelihood is complex, efficient computation
often involve Markov chain Monte Carlo sampling with limited applicability to
large data sets. We extend some of these techniques to scale efficiently when
the problem exhibits a sequential structure. We propose an approximation that
enables sequential sampling of both latent variables and associated parameters.
We demonstrate strong performance in growing-data settings that would otherwise
be unfeasible with naive, non-sequential sampling.
On the Complexity of Value Iteration
Authors: Nikhil Balaji, Stefan Kiefer, Petr Novotný, Guillermo A. Pérez, Mahsa Shirmohammadi
Date: 2018-07-13 00:00:00

Value iteration is a fundamental algorithm for solving Markov Decision
Processes (MDPs). It computes the maximal $n$-step payoff by iterating $n$
times a recurrence equation which is naturally associated to the MDP. At the
same time, value iteration provides a policy for the MDP that is optimal on a
given finite horizon $n$. In this paper, we settle the computational complexity
of value iteration. We show that, given a horizon $n$ in binary and an MDP,
computing an optimal policy is EXP-complete, thus resolving an open problem
that goes back to the seminal 1987 paper on the complexity of MDPs by
Papadimitriou and Tsitsiklis. As a stepping stone, we show that it is
EXP-complete to compute the $n$-fold iteration (with $n$ in binary) of a
function given by a straight-line program over the integers with $\max$ and $+$
as operators.
TequilaGAN: How to easily identify GAN samples
Authors: Rafael Valle, Wilson Cai, Anish Doshi
Date: 2018-07-13 00:00:00
Proceeding: ICLR 2019 5
Tasks: Generative Adversarial Network

In this paper we show strategies to easily identify fake samples generated
with the Generative Adversarial Network framework. One strategy is based on the
statistical analysis and comparison of raw pixel values and features extracted
from them. The other strategy learns formal specifications from the real data
and shows that fake samples violate the specifications of the real data. We
show that fake samples produced with GANs have a universal signature that can
be used to identify fake samples. We provide results on MNIST, CIFAR10, music
and speech data.
Performance of Image Registration Tools on High-Resolution 3D Brain Images
Authors: Abdullah Nazib, James Galloway, Clinton Fookes, Dimitri Perrin
Date: 2018-07-13 00:00:00
Tasks: Computational Efficiency, Image Registration, Vocal Bursts Intensity Prediction

Recent progress in tissue clearing has allowed for the imaging of entire
organs at single-cell resolution. These methods produce very large 3D images
(several gigabytes for a whole mouse brain). A necessary step in analysing
these images is registration across samples. Existing methods of registration
were developed for lower resolution image modalities (e.g. MRI) and it is
unclear whether their performance and accuracy is satisfactory at this larger
scale. In this study, we used data from different mouse brains cleared with the
CUBIC protocol to evaluate five freely available image registration tools. We
used several performance metrics to assess accuracy, and completion time as a
measure of efficiency. The results of this evaluation suggest that the ANTS
registration tool provides the best registration accuracy while Elastix has the
highest computational efficiency among the methods with an acceptable accuracy.
The results also highlight the need to develop new registration methods
optimised for these high-resolution 3D images.
Perceptrons from Memristors
Authors: Francisco Silva, Mikel Sanz, João Seixas, Enrique Solano, Yasser Omar
Date: 2018-07-13 00:00:00

Memristors, resistors with memory whose outputs depend on the history of
their inputs, have been used with success in neuromorphic architectures,
particularly as synapses and non-volatile memories. However, to the best of our
knowledge, no model for a network in which both the synapses and the neurons
are implemented using memristors has been proposed so far. In the present work
we introduce models for single and multilayer perceptrons based exclusively on
memristors. We adapt the delta rule to the memristor-based single-layer
perceptron and the backpropagation algorithm to the memristor-based multilayer
perceptron. Our results show that both perform as expected for perceptrons,
including satisfying Minsky-Papert's theorem. As a consequence of the Universal
Approximation Theorem, they also show that memristors are universal function
approximators. By using memristors for both the neurons and the synapses, our
models pave the way for novel memristor-based neural network architectures and
algorithms. A neural network based on memristors could show advantages in terms
of energy conservation and open up possibilities for other learning systems to
be adapted to a memristor-based paradigm, both in the classical and quantum
learning realms.
Ultra-Fine Entity Typing
Authors: Eunsol Choi, Omer Levy, Yejin Choi, Luke Zettlemoyer
Date: 2018-07-13 00:00:00
Proceeding: ACL 2018 7
Tasks: Entity Linking, Entity Typing, Sentence

We introduce a new entity typing task: given a sentence with an entity
mention, the goal is to predict a set of free-form phrases (e.g. skyscraper,
songwriter, or criminal) that describe appropriate types for the target entity.
This formulation allows us to use a new type of distant supervision at large
scale: head words, which indicate the type of the noun phrases they appear in.
We show that these ultra-fine types can be crowd-sourced, and introduce new
evaluation sets that are much more diverse and fine-grained than existing
benchmarks. We present a model that can predict open types, and is trained
using a multitask objective that pools our new head-word supervision with prior
supervision from entity linking. Experimental results demonstrate that our
model is effective in predicting entity types at varying granularity; it
achieves state of the art performance on an existing fine-grained entity typing
benchmark, and sets baselines for our newly-introduced datasets. Our data and
model can be downloaded from: http://nlp.cs.washington.edu/entity_type
Analysis Dictionary Learning based Classification: Structure for Robustness
Authors: Wen Tang, Ashkan Panahi, Hamid Krim, Liyi Dai
Date: 2018-07-13 00:00:00
Tasks: Classification, Dictionary Learning, General Classification

A discriminative structured analysis dictionary is proposed for the classification task. A structure of the union of subspaces (UoS) is integrated into the conventional analysis dictionary learning to enhance the capability of discrimination. A simple classifier is also simultaneously included into the formulated functional to ensure a more complete consistent classification. The solution of the algorithm is efficiently obtained by the linearized alternating direction method of multipliers. Moreover, a distributed structured analysis dictionary learning is also presented to address large scale datasets. It can group-(class-) independently train the structured analysis dictionaries by different machines/cores/threads, and therefore avoid a high computational cost. A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification. Experiments demonstrate that our method achieves a comparable or better performance than the state-of-the-art algorithms in a variety of visual classification tasks. In addition, the training and testing computational complexity are also greatly reduced.
TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection
Authors: Yunchao Wei, Zhiqiang Shen, Bowen Cheng, Honghui Shi, JinJun Xiong, Jiashi Feng, Thomas Huang
Date: 2018-07-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Multiple Instance Learning, Object, object-detection, Object Detection, Segmentation, Weakly Supervised Object Detection, Weakly supervised segmentation

This work provides a simple approach to discover tight object bounding boxes
with only image-level supervision, called Tight box mining with Surrounding
Segmentation Context (TS2C). We observe that object candidates mined through
current multiple instance learning methods are usually trapped to
discriminative object parts, rather than the entire object. TS2C leverages
surrounding segmentation context derived from weakly-supervised segmentation to
suppress such low-quality distracting candidates and boost the high-quality
ones. Specifically, TS2C is developed based on two key properties of desirable
bounding boxes: 1) high purity, meaning most pixels in the box are with high
object response, and 2) high completeness, meaning the box covers high object
response pixels comprehensively. With such novel and computable criteria, more
tight candidates can be discovered for learning a better object detector. With
TS2C, we obtain 48.0% and 44.4% mAP scores on VOC 2007 and 2012 benchmarks,
which are the new state-of-the-arts.
Automatic segmentation of skin lesions using deep learning
Authors: Joshua Peter Ebenezer, Jagath C. Rajapakse
Date: 2018-07-13 00:00:00
Tasks: Deep Learning

This paper summarizes the method used in our submission to Task 1 of the
International Skin Imaging Collaboration's (ISIC) Skin Lesion Analysis Towards
Melanoma Detection challenge held in 2018. We used a fully automated method to
accurately segment lesion boundaries from dermoscopic images. A U-net deep
learning network is trained on publicly available data from ISIC. We introduce
the use of intensity, color, and texture enhancement operations as
pre-processing steps and morphological operations and contour identification as
post-processing steps.
Computer Analysis of Architecture Using Automatic Image Understanding
Authors: Fan Wei, Yuan Li, Lior Shamir
Date: 2018-07-13 00:00:00

In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available.
Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes
Authors: Junjie Huang, Wei Zou, Jiagang Zhu, Zheng Zhu
Date: 2018-07-13 00:00:00
Tasks: Moving Object Detection, object-detection, Object Detection, Optical Flow Estimation

Real-time moving object detection in unconstrained scenes is a difficult task
due to dynamic background, changing foreground appearance and limited
computational resource. In this paper, an optical flow based moving object
detection framework is proposed to address this problem. We utilize homography
matrixes to online construct a background model in the form of optical flow.
When judging out moving foregrounds from scenes, a dual-mode judge mechanism is
designed to heighten the system's adaptation to challenging situations. In
experiment part, two evaluation metrics are redefined for more properly
reflecting the performance of methods. We quantitatively and qualitatively
validate the effectiveness and feasibility of our method with videos in various
scene conditions. The experimental results show that our method adapts itself
to different situations and outperforms the state-of-the-art methods,
indicating the advantages of optical flow based methods.
Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition
Authors: Varun N. Shenoy, Oliver O. Aalami
Date: 2018-07-13 00:00:00
Tasks: BIG-bench Machine Learning, Management

Biometric measurements captured from medical devices, such as blood pressure
gauges, glucose monitors, and weighing scales, are essential to tracking a
patient's health. Trends in these measurements can accurately track diabetes,
cardiovascular issues, and assist medication management for patients.
Currently, patients record their results and data of measurement in a physical
notebook. It may be weeks before a doctor sees a patient's records and can
assess the health of the patient. With a predicted 6.8 billion smartphones in
the world by 2022, health monitoring platforms, such as Apple's HealthKit, can
be leveraged to provide the right care at the right time. This research
presents a mobile application that enables users to capture medical monitor
data and send it to their doctor swiftly. A key contribution of this paper is a
robust engine that can recognize digits from medical monitors with an accuracy
of 98.2%.
Algorithms for metric learning via contrastive embeddings
Authors: Diego Ihara Centurion, Neshat Mohammadi, Anastasios Sidiropoulos
Date: 2018-07-13 00:00:00
Tasks: graph partitioning, Metric Learning

We study the problem of supervised learning a metric space under
discriminative constraints. Given a universe $X$ and sets ${\cal S}, {\cal
D}\subset {X \choose 2}$ of similar and dissimilar pairs, we seek to find a
mapping $f:X\to Y$, into some target metric space $M=(Y,\rho)$, such that
similar objects are mapped to points at distance at most $u$, and dissimilar
objects are mapped to points at distance at least $\ell$. More generally, the
goal is to find a mapping of maximum accuracy (that is, fraction of correctly
classified pairs). We propose approximation algorithms for various versions of
this problem, for the cases of Euclidean and tree metric spaces. For both of
these target spaces, we obtain fully polynomial-time approximation schemes
(FPTAS) for the case of perfect information. In the presence of imperfect
information we present approximation algorithms that run in quasipolynomial
time (QPTAS). Our algorithms use a combination of tools from metric embeddings
and graph partitioning, that could be of independent interest.
Effective Occlusion Handling for Fast Correlation Filter-based Trackers
Authors: Zheng Zhang, T. T. Wong
Date: 2018-07-13 00:00:00
Tasks: Occlusion Handling

Correlation filter-based trackers heavily suffer from the problem of multiple peaks in their response maps incurred by occlusions. Moreover, the whole tracking pipeline may break down due to the uncertainties brought by shifting among peaks, which will further lead to the degraded correlation filter model. To alleviate the drift problem caused by occlusions, we propose a novel scheme to choose the specific filter model according to different scenarios. Specifically, an effective measurement function is designed to evaluate the quality of filter response. A sophisticated strategy is employed to judge whether occlusions occur, and then decide how to update the filter models. In addition, we take advantage of both log-polar method and pyramid-like approach to estimate the best scale of the target. We evaluate our proposed approach on VOT2018 challenge and OTB100 dataset, whose experimental result shows that the proposed tracker achieves the promising performance compared against the state-of-the-art trackers.
Extracting Contact and Motion from Manipulation Videos
Authors: Konstantinos Zampogiannis, Kanishka Ganguly, Cornelia Fermuller, Yiannis Aloimonos
Date: 2018-07-13 00:00:00
Tasks: Clustering, Imitation Learning, Object, Point Cloud Segmentation

When we physically interact with our environment using our hands, we touch
objects and force them to move: contact and motion are defining properties of
manipulation. In this paper, we present an active, bottom-up method for the
detection of actor-object contacts and the extraction of moved objects and
their motions in RGBD videos of manipulation actions. At the core of our
approach lies non-rigid registration: we continuously warp a point cloud model
of the observed scene to the current video frame, generating a set of dense 3D
point trajectories. Under loose assumptions, we employ simple point cloud
segmentation techniques to extract the actor and subsequently detect
actor-environment contacts based on the estimated trajectories. For each such
interaction, using the detected contact as an attention mechanism, we obtain an
initial motion segment for the manipulated object by clustering trajectories in
the contact area vicinity and then we jointly refine the object segment and
estimate its 6DOF pose in all observed frames. Because of its generality and
the fundamental, yet highly informative, nature of its outputs, our approach is
applicable to a wide range of perception and planning tasks. We qualitatively
evaluate our method on a number of input sequences and present a comprehensive
robot imitation learning example, in which we demonstrate the crucial role of
our outputs in developing action representations/plans from observation.