pwc-archive/papers-with-abstracts huggingface dataset:
Improving Small Object Proposals for Company Logo Detection
Authors: Christian Eggert, Dan Zecha, Stephan Brehm, Rainer Lienhart
Date: 2017-04-28 00:00:00
Tasks: Object, object-detection, Object Detection

Many modern approaches for object detection are two-staged pipelines. The
first stage identifies regions of interest which are then classified in the
second stage. Faster R-CNN is such an approach for object detection which
combines both stages into a single pipeline. In this paper we apply Faster
R-CNN to the task of company logo detection. Motivated by its weak performance
on small object instances, we examine in detail both the proposal and the
classification stage with respect to a wide range of object sizes. We
investigate the influence of feature map resolution on the performance of those
stages.
  Based on theoretical considerations, we introduce an improved scheme for
generating anchor proposals and propose a modification to Faster R-CNN which
leverages higher-resolution feature maps for small objects. We evaluate our
approach on the FlickrLogos dataset improving the RPN performance from 0.52 to
0.71 (MABO) and the detection performance from 0.52 to 0.67 (mAP).
Object Detection by Spatio-Temporal Analysis and Tracking of the Detected Objects in a Video with Variable Background
Authors: Kumar S. Ray, Vijayan K. Asari, Soma Chakraborty
Date: 2017-04-28 00:00:00
Tasks: Object, object-detection, Object Detection

In this paper we propose a novel approach for detecting and tracking objects
in videos with variable background i.e. videos captured by moving cameras
without any additional sensor. In a video captured by a moving camera, both the
background and foreground are changing in each frame of the image sequence. So
for these videos, modeling a single background with traditional background
modeling methods is infeasible and thus the detection of actual moving object
in a variable background is a challenging task. To detect actual moving object
in this work, spatio-temporal blobs have been generated in each frame by
spatio-temporal analysis of the image sequence using a three-dimensional Gabor
filter. Then individual blobs, which are parts of one object are merged using
Minimum Spanning Tree to form the moving object in the variable background. The
height, width and four-bin gray-value histogram of the object are calculated as
its features and an object is tracked in each frame using these features to
generate the trajectories of the object through the video sequence. In this
work, problem of data association during tracking is solved by Linear
Assignment Problem and occlusion is handled by the application of kalman
filter. The major advantage of our method over most of the existing tracking
algorithms is that, the proposed method does not require initialization in the
first frame or training on sample data to perform. Performance of the algorithm
has been tested on benchmark videos and very satisfactory result has been
achieved. The performance of the algorithm is also comparable and superior with
respect to some benchmark algorithms.
Manifolds of Projective Shapes
Authors: Thomas Hotz, Florian Kelma, John T. Kent
Date: 2016-02-13 00:00:00

The projective shape of a configuration of k points or "landmarks" in RP(d)
consists of the information that is invariant under projective transformations
and hence is reconstructable from uncalibrated camera views. Mathematically,
the space of projective shapes for these k landmarks can be described as the
quotient space of k copies of RP(d) modulo the action of the projective linear
group PGL(d). Using homogeneous coordinates, such configurations can be
described as real k-times-(d+1)-dimensional matrices given up to
left-multiplication of non-singular diagonal matrices, while the group PGL(d)
acts as GL(d+1) from the right. The main purpose of this paper is to give a
detailed examination of the topology of projective shape space, and, using
matrix notation, it is shown how to derive subsets that are in a certain sense
maximal, differentiable Hausdorff manifolds which can be provided with a
Riemannian metric. A special subclass of the projective shapes consists of the
Tyler regular shapes, for which geometrically motivated pre-shapes can be
defined, thus allowing for the construction of a natural Riemannian metric.
Image reconstruction by domain transform manifold learning
Authors: Bo Zhu, Jeremiah Z. Liu, Bruce R. Rosen, Matthew S. Rosen
Date: 2017-04-28 00:00:00
Tasks: Astronomy, Image Reconstruction

Image reconstruction plays a critical role in the implementation of all
contemporary imaging modalities across the physical and life sciences including
optical, MRI, CT, PET, and radio astronomy. During an image acquisition, the
sensor encodes an intermediate representation of an object in the sensor
domain, which is subsequently reconstructed into an image by an inversion of
the encoding function. Image reconstruction is challenging because analytic
knowledge of the inverse transform may not exist a priori, especially in the
presence of sensor non-idealities and noise. Thus, the standard reconstruction
approach involves approximating the inverse function with multiple ad hoc
stages in a signal processing chain whose composition depends on the details of
each acquisition strategy, and often requires expert parameter tuning to
optimize reconstruction performance. We present here a unified framework for
image reconstruction, AUtomated TransfOrm by Manifold APproximation (AUTOMAP),
which recasts image reconstruction as a data-driven, supervised learning task
that allows a mapping between sensor and image domain to emerge from an
appropriate corpus of training data. We implement AUTOMAP with a deep neural
network and exhibit its flexibility in learning reconstruction transforms for a
variety of MRI acquisition strategies, using the same network architecture and
hyperparameters. We further demonstrate its efficiency in sparsely representing
transforms along low-dimensional manifolds, resulting in superior immunity to
noise and reconstruction artifacts compared with conventional handcrafted
reconstruction methods. In addition to improving the reconstruction performance
of existing acquisition methodologies, we anticipate accelerating the discovery
of new acquisition strategies across modalities as the burden of reconstruction
becomes lifted by AUTOMAP and learned-reconstruction approaches.
Outline Colorization through Tandem Adversarial Networks
Authors: Kevin Frans
Date: 2017-04-28 00:00:00
Tasks: Colorization

When creating digital art, coloring and shading are often time consuming
tasks that follow the same general patterns. A solution to automatically
colorize raw line art would have many practical applications. We propose a
setup utilizing two networks in tandem: a color prediction network based only
on outlines, and a shading network conditioned on both outlines and a color
scheme. We present processing methods to limit information passed in the color
scheme, improving generalization. Finally, we demonstrate natural-looking
results when colorizing outlines from scratch, as well as from a messy,
user-defined color scheme.
Deep image mining for diabetic retinopathy screening
Authors: Gwenolé Quellec, Katia Charrière, Yassine Boudi, Béatrice Cochener, Mathieu Lamard
Date: 2016-10-22 00:00:00
Tasks: Medical Image Analysis

Deep learning is quickly becoming the leading methodology for medical image
analysis. Given a large medical archive, where each image is associated with a
diagnosis, efficient pathology detectors or classifiers can be trained with
virtually no expert knowledge about the target pathologies. However, deep
learning algorithms, including the popular ConvNets, are black boxes: little is
known about the local patterns analyzed by ConvNets to make a decision at the
image level. A solution is proposed in this paper to create heatmaps showing
which pixels in images play a role in the image-level predictions. In other
words, a ConvNet trained for image-level classification can be used to detect
lesions as well. A generalization of the backpropagation method is proposed in
order to train ConvNets that produce high-quality heatmaps. The proposed
solution is applied to diabetic retinopathy (DR) screening in a dataset of
almost 90,000 fundus photographs from the 2015 Kaggle Diabetic Retinopathy
competition and a private dataset of almost 110,000 photographs (e-ophtha). For
the task of detecting referable DR, very good detection performance was
achieved: $A_z = 0.954$ in Kaggle's dataset and $A_z = 0.949$ in e-ophtha.
Performance was also evaluated at the image level and at the lesion level in
the DiaretDB1 dataset, where four types of lesions are manually segmented:
microaneurysms, hemorrhages, exudates and cotton-wool spots. The proposed
detector outperforms recent algorithms trained to detect those lesions
specifically, as well as competing heatmap generation algorithms for ConvNets.
This detector is part of the Messidor system for mobile eye pathology
screening. Because it does not rely on expert knowledge or manual segmentation
for detecting relevant patterns, the proposed solution is a promising image
mining tool, which has the potential to discover new biomarkers in images.
On Inconsistency Indices and Inconsistency Axioms in Pairwise Comparisons
Authors: Jiri Mazurek
Date: 2017-03-15 00:00:00
Tasks: Decision Making

Pairwise comparisons are an important tool of modern (multiple criteria)
decision making. Since human judgments are often inconsistent, many studies
focused on the ways how to express and measure this inconsistency, and several
inconsistency indices were proposed as an alternative to Saaty inconsistency
index and inconsistency ratio for reciprocal pairwise comparisons matrices.
This paper aims to: firstly, introduce a new measure of inconsistency of
pairwise comparisons and to prove its basic properties; secondly, to postulate
an additional axiom, an upper boundary axiom, to an existing set of axioms; and
the last, but not least, the paper provides proofs of satisfaction of this
additional axiom by selected inconsistency indices as well as it provides their
numerical comparison.
Active Collaborative Ensemble Tracking
Authors: Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba, Shin Ishii
Date: 2017-04-28 00:00:00
Tasks: Diversity, General Classification

A discriminative ensemble tracker employs multiple classifiers, each of which
casts a vote on all of the obtained samples. The votes are then aggregated in
an attempt to localize the target object. Such method relies on collective
competence and the diversity of the ensemble to approach the target/non-target
classification task from different views. However, by updating all of the
ensemble using a shared set of samples and their final labels, such diversity
is lost or reduced to the diversity provided by the underlying features or
internal classifiers' dynamics. Additionally, the classifiers do not exchange
information with each other while striving to serve the collective goal, i.e.,
better classification. In this study, we propose an active collaborative
information exchange scheme for ensemble tracking. This, not only orchestrates
different classifier towards a common goal but also provides an intelligent
update mechanism to keep the diversity of classifiers and to mitigate the
shortcomings of one with the others. The data exchange is optimized with regard
to an ensemble uncertainty utility function, and the ensemble is updated via
co-training. The evaluations demonstrate promising results realized by the
proposed algorithm for the real-world online tracking.
Low-effort place recognition with WiFi fingerprints using deep learning
Authors: Michał Nowicki, Jan Wietrzykowski
Date: 2016-11-07 00:00:00
Tasks: Deep Learning, General Classification, Indoor Localization

Using WiFi signals for indoor localization is the main localization modality
of the existing personal indoor localization systems operating on mobile
devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals
are usually available indoors and can provide rough initial position estimate
or can be used together with other positioning systems. Currently, the best
solutions rely on filtering, manual data analysis, and time-consuming parameter
tuning to achieve reliable and accurate localization. In this work, we propose
to use deep neural networks to significantly lower the work-force burden of the
localization system design, while still achieving satisfactory results.
Assuming the state-of-the-art hierarchical approach, we employ the DNN system
for building/floor classification. We show that stacked autoencoders allow to
efficiently reduce the feature space in order to achieve robust and precise
classification. The proposed architecture is verified on the publicly available
UJIIndoorLoc dataset and the results are compared with other solutions.
A Tribe Competition-Based Genetic Algorithm for Feature Selection in Pattern Classification
Authors: Benteng Ma, Yong Xia
Date: 2017-04-28 00:00:00
Tasks: Evolutionary Algorithms, feature selection, General Classification

Feature selection has always been a critical step in pattern recognition, in
which evolutionary algorithms, such as the genetic algorithm (GA), are most
commonly used. However, the individual encoding scheme used in various GAs
would either pose a bias on the solution or require a pre-specified number of
features, and hence may lead to less accurate results. In this paper, a tribe
competition-based genetic algorithm (TCbGA) is proposed for feature selection
in pattern classification. The population of individuals is divided into
multiple tribes, and the initialization and evolutionary operations are
modified to ensure that the number of selected features in each tribe follows a
Gaussian distribution. Thus each tribe focuses on exploring a specific part of
the solution space. Meanwhile, tribe competition is introduced to the evolution
process, which allows the winning tribes, which produce better individuals, to
enlarge their sizes, i.e. having more individuals to search their parts of the
solution space. This algorithm, therefore, avoids the bias on solutions and
requirement of a pre-specified number of features. We have evaluated our
algorithm against several state-of-the-art feature selection approaches on 20
benchmark datasets. Our results suggest that the proposed TCbGA algorithm can
identify the optimal feature subset more effectively and produce more accurate
pattern classification.
From First-Order Logic to Assertional Logic
Authors: Yi Zhou
Date: 2017-01-12 00:00:00

First-Order Logic (FOL) is widely regarded as one of the most important
foundations for knowledge representation. Nevertheless, in this paper, we argue
that FOL has several critical issues for this purpose. Instead, we propose an
alternative called assertional logic, in which all syntactic objects are
categorized as set theoretic constructs including individuals, concepts and
operators, and all kinds of knowledge are formalized by equality assertions. We
first present a primitive form of assertional logic that uses minimal assumed
knowledge and constructs. Then, we show how to extend it by definitions, which
are special kinds of knowledge, i.e., assertions. We argue that assertional
logic, although simpler, is more expressive and extensible than FOL. As a case
study, we show how assertional logic can be used to unify logic and
probability, and more building blocks in AI.
Automatic Real-time Background Cut for Portrait Videos
Authors: Xiaoyong Shen, RuiXing Wang, Hengshuang Zhao, Jiaya Jia
Date: 2017-04-28 00:00:00
Tasks: Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

We in this paper solve the problem of high-quality automatic real-time
background cut for 720p portrait videos. We first handle the background
ambiguity issue in semantic segmentation by proposing a global background
attenuation model. A spatial-temporal refinement network is developed to
further refine the segmentation errors in each frame and ensure temporal
coherence in the segmentation map. We form an end-to-end network for training
and testing. Each module is designed considering efficiency and accuracy. We
build a portrait dataset, which includes 8,000 images with high-quality labeled
map for training and testing. To further improve the performance, we build a
portrait video dataset with 50 sequences to fine-tune video segmentation. Our
framework benefits many video processing applications.
Axiomatizing Category Theory in Free Logic
Authors: Christoph Benzmüller, Dana S. Scott
Date: 2016-09-06 00:00:00

Starting from a generalization of the standard axioms for a monoid we present
a stepwise development of various, mutually equivalent foundational axiom
systems for category theory. Our axiom sets have been formalized in the
Isabelle/HOL interactive proof assistant, and this formalization utilizes a
semantically correct embedding of free logic in classical higher-order logic.
The modeling and formal analysis of our axiom sets has been significantly
supported by series of experiments with automated reasoning tools integrated
with Isabelle/HOL. We also address the relation of our axiom systems to
alternative proposals from the literature, including an axiom set proposed by
Freyd and Scedrov for which we reveal a technical issue (when encoded in free
logic where free variables range over defined and undefined objects): either
all operations, e.g. morphism composition, are total or their axiom system is
inconsistent. The repair for this problem is quite straightforward, however.
Near-Data Processing for Differentiable Machine Learning Models
Authors: Hyeokjun Choe, Seil Lee, Hyunha Nam, Seongsik Park, Seijoon Kim, Eui-Young Chung, Sungroh Yoon
Date: 2016-10-06 00:00:00
Tasks: BIG-bench Machine Learning

Near-data processing (NDP) refers to augmenting memory or storage with
processing power. Despite its potential for acceleration computing and reducing
power requirements, only limited progress has been made in popularizing NDP for
various reasons. Recently, two major changes have occurred that have ignited
renewed interest and caused a resurgence of NDP. The first is the success of
machine learning (ML), which often demands a great deal of computation for
training, requiring frequent transfers of big data. The second is the
popularity of NAND flash-based solid-state drives (SSDs) containing multicore
processors that can accommodate extra computation for data processing. In this
paper, we evaluate the potential of NDP for ML using a new SSD platform that
allows us to simulate instorage processing (ISP) of ML workloads. Our platform
(named ISP-ML) is a full-fledged simulator of a realistic multi-channel SSD
that can execute various ML algorithms using data stored in the SSD. To conduct
a thorough performance analysis and an in-depth comparison with alternative
techniques, we focus on a specific algorithm: stochastic gradient descent
(SGD), which is the de facto standard for training differentiable models such
as logistic regression and neural networks. We implement and compare three SGD
variants (synchronous, Downpour, and elastic averaging) using ISP-ML,
exploiting the multiple NAND channels to parallelize SGD. In addition, we
compare the performance of ISP and that of conventional in-host processing,
revealing the advantages of ISP. Based on the advantages and limitations
identified through our experiments, we further discuss directions for future
research on ISP for accelerating ML.
Reading Wikipedia to Answer Open-Domain Questions
Authors: Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes
Date: 2017-03-31 00:00:00
Proceeding: ACL 2017 7
Tasks: Articles, Open-Domain Question Answering, Question Answering, Reading Comprehension, Retrieval

This paper proposes to tackle open- domain question answering using Wikipedia
as the unique knowledge source: the answer to any factoid question is a text
span in a Wikipedia article. This task of machine reading at scale combines the
challenges of document retrieval (finding the relevant articles) with that of
machine comprehension of text (identifying the answer spans from those
articles). Our approach combines a search component based on bigram hashing and
TF-IDF matching with a multi-layer recurrent neural network model trained to
detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA
datasets indicate that (1) both modules are highly competitive with respect to
existing counterparts and (2) multitask learning using distant supervision on
their combination is an effective complete system on this challenging task.
Word Affect Intensities
Authors: Saif M. Mohammad
Date: 2017-04-28 00:00:00
Tasks: Emotion Recognition, Text Generation

Words often convey affect -- emotions, feelings, and attitudes. Further, different words can convey affect to various degrees (intensities). However, existing manually created lexicons for basic emotions (such as anger and fear) indicate only coarse categories of affect association (for example, associated with anger or not associated with anger). Automatic lexicons of affect provide fine degrees of association, but they tend not to be accurate as human-created lexicons. Here, for the first time, we present a manually created affect intensity lexicon with real-valued scores of intensity for four basic emotions: anger, fear, joy, and sadness. (We will subsequently add entries for more emotions such as disgust, anticipation, trust, and surprise.) We refer to this dataset as the NRC Affect Intensity Lexicon, or AIL for short. AIL has entries for close to 6,000 English words. We used a technique called best-worst scaling (BWS) to create the lexicon. BWS improves annotation consistency and obtains reliable fine-grained scores (split-half reliability > 0.91). We also compare the entries in AIL with the entries in the NRC VAD Lexicon, which has valence, arousal, and dominance (VAD) scores for 20K English words. We find that anger, fear, and sadness words, on average, have very similar VAD scores. However, sadness words tend to have slightly lower dominance scores than fear and anger words. The Affect Intensity Lexicon has applications in automatic emotion analysis in a number of domains such as commerce, education, intelligence, and public health. AIL is also useful in the building of natural language generation systems.
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
Authors: Sarfaraz Hussein, Kunlin Cao, Qi Song, Ulas Bagci
Date: 2017-04-28 00:00:00
Tasks: Lung Cancer Diagnosis, Multi-Task Learning, Prognosis, Transfer Learning

Risk stratification of lung nodules is a task of primary importance in lung
cancer diagnosis. Any improvement in robust and accurate nodule
characterization can assist in identifying cancer stage, prognosis, and
improving treatment planning. In this study, we propose a 3D Convolutional
Neural Network (CNN) based nodule characterization strategy. With a completely
3D approach, we utilize the volumetric information from a CT scan which would
be otherwise lost in the conventional 2D CNN based approaches. In order to
address the need for a large amount for training data for CNN, we resort to
transfer learning to obtain highly discriminative features. Moreover, we also
acquire the task dependent feature representation for six high-level nodule
attributes and fuse this complementary information via a Multi-task learning
(MTL) framework. Finally, we propose to incorporate potential disagreement
among radiologists while scoring different nodule attributes in a graph
regularized sparse multi-task learning. We evaluated our proposed approach on
one of the largest publicly available lung nodule datasets comprising 1018
scans and obtained state-of-the-art results in regressing the malignancy
scores.