pwc-archive/papers-with-abstracts huggingface dataset:
Relational Constraints for Metric Learning on Relational Data
Authors: Jiajun Pan, Hoel Le Capitaine, Philippe Leray
Date: 2018-07-02 00:00:00
Tasks: Metric Learning

Most of metric learning approaches are dedicated to be applied on data
described by feature vectors, with some notable exceptions such as times
series, trees or graphs. The objective of this paper is to propose a metric
learning algorithm that specifically considers relational data. The proposed
approach can take benefit from both the topological structure of the data and
supervised labels. For selecting relative constraints representing the
relational information, we introduce a link-strength function that measures the
strength of relationship links between entities by the side-information of
their common parents. We show the performance of the proposed method with two
different classical metric learning algorithms, which are ITML (Information
Theoretic Metric Learning) and LSML (Least Squares Metric Learning), and test
on several real-world datasets. Experimental results show that using relational
information improves the quality of the learned metric.
Studio2Shop: from studio photo shoots to fashion articles
Authors: Julia Lasserre, Katharina Rasch, Roland Vollgraf
Date: 2018-07-02 00:00:00
Tasks: Articles, Retrieval

Fashion is an increasingly important topic in computer vision, in particular
the so-called street-to-shop task of matching street images with shop images
containing similar fashion items. Solving this problem promises new means of
making fashion searchable and helping shoppers find the articles they are
looking for. This paper focuses on finding pieces of clothing worn by a person
in full-body or half-body images with neutral backgrounds. Such images are
ubiquitous on the web and in fashion blogs, and are typically studio photos, we
refer to this setting as studio-to-shop. Recent advances in computational
fashion include the development of domain-specific numerical representations.
Our model Studio2Shop builds on top of such representations and uses a deep
convolutional network trained to match a query image to the numerical feature
vectors of all the articles annotated in this image. Top-$k$ retrieval
evaluation on test query images shows that the correct items are most often
found within a range that is sufficiently small for building realistic visual
search engines for the studio-to-shop setting.
A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics
Authors: Roel Dobbe, Sarah Dean, Thomas Gilbert, Nitin Kohli
Date: 2018-07-02 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Fairness, Position

Machine learning (ML) is increasingly deployed in real world contexts,
supplying actionable insights and forming the basis of automated
decision-making systems. While issues resulting from biases pre-existing in
training data have been at the center of the fairness debate, these systems are
also affected by technical and emergent biases, which often arise as
context-specific artifacts of implementation. This position paper interprets
technical bias as an epistemological problem and emergent bias as a dynamical
feedback phenomenon. In order to stimulate debate on how to change machine
learning practice to effectively address these issues, we explore this broader
view on bias, stress the need to reflect on epistemology, and point to
value-sensitive design methodologies to revisit the design and implementation
process of automated decision-making systems.
Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility
Authors: Yunlong Wang, Bjoern Sommer, Falk Schreiber, Harald Reiterer
Date: 2018-07-02 00:00:00
Tasks: Clustering

Extracting significant places or places of interest (POIs) using individuals'
spatio-temporal data is of fundamental importance for human mobility analysis.
Classical clustering methods have been used in prior work for detecting POIs,
but without considering temporal constraints. Usually, the involved parameters
for clustering are difficult to determine, e.g., the optimal cluster number in
hierarchical clustering. Currently, researchers either choose heuristic values
or use spatial distance-based optimization to determine an appropriate
parameter set. We argue that existing research does not optimally address
temporal information and thus leaves much room for improvement. Considering
temporal constraints in human mobility, we introduce an effective clustering
approach - namely POI clustering with temporal constraints (PC-TC) - to extract
POIs from spatio-temporal data of human mobility. Following human mobility
nature in modern society, our approach aims to extract both global POIs (e.g.,
workplace or university) and local POIs (e.g., library, lab, and canteen).
Based on two publicly available datasets including 193 individuals, our
evaluation results show that PC-TC has much potential for next place prediction
in terms of granularity (i.e., the number of extracted POIs) and
predictability.
Punctuation Prediction Model for Conversational Speech
Authors: Piotr Żelasko, Piotr Szymański, Jan Mizgajski, Adrian Szymczak, Yishay Carmiel, Najim Dehak
Date: 2018-07-02 00:00:00
Tasks: model, Prediction

An ASR system usually does not predict any punctuation or capitalization.
Lack of punctuation causes problems in result presentation and confuses both
the human reader andoff-the-shelf natural language processing algorithms. To
overcome these limitations, we train two variants of Deep Neural Network (DNN)
sequence labelling models - a Bidirectional Long Short-Term Memory (BLSTM) and
a Convolutional Neural Network (CNN), to predict the punctuation. The models
are trained on the Fisher corpus which includes punctuation annotation. In our
experiments, we combine time-aligned and punctuated Fisher corpus transcripts
using a sequence alignment algorithm. The neural networks are trained on Common
Web Crawl GloVe embedding of the words in Fisher transcripts aligned with
conversation side indicators and word time infomation. The CNNs yield a better
precision and BLSTMs tend to have better recall. While BLSTMs make fewer
mistakes overall, the punctuation predicted by the CNN is more accurate -
especially in the case of question marks. Our results constitute significant
evidence that the distribution of words in time, as well as pre-trained
embeddings, can be useful in the punctuation prediction task.
SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification
Authors: Xing Fan, Wei Jiang, Hao Luo, Mengjuan Fei
Date: 2018-07-02 00:00:00
Tasks: Person Re-Identification, Re-Ranking

Many current successful Person Re-Identification(ReID) methods train a model
with the softmax loss function to classify images of different persons and
obtain the feature vectors at the same time. However, the underlying feature
embedding space is ignored. In this paper, we use a modified softmax function,
termed Sphere Softmax, to solve the classification problem and learn a
hypersphere manifold embedding simultaneously. A balanced sampling strategy is
also introduced. Finally, we propose a convolutional neural network called
SphereReID adopting Sphere Softmax and training a single model end-to-end with
a new warming-up learning rate schedule on four challenging datasets including
Market-1501, DukeMTMC-reID, CHHK-03, and CUHK-SYSU. Experimental results
demonstrate that this single model outperforms the state-of-the-art methods on
all four datasets without fine-tuning or re-ranking. For example, it achieves
94.4% rank-1 accuracy on Market-1501 and 83.9% rank-1 accuracy on
DukeMTMC-reID. The code and trained weights of our model will be released.
Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract)
Authors: Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, Anna Rohrbach
Date: 2018-07-02 00:00:00
Tasks: Image Captioning

Most machine learning methods are known to capture and exploit biases of the
training data. While some biases are beneficial for learning, others are
harmful. Specifically, image captioning models tend to exaggerate biases
present in training data. This can lead to incorrect captions in domains where
unbiased captions are desired, or required, due to over reliance on the learned
prior and image context. We investigate generation of gender specific caption
words (e.g. man, woman) based on the person's appearance or the image context.
We introduce a new Equalizer model that ensures equal gender probability when
gender evidence is occluded in a scene and confident predictions when gender
evidence is present. The resulting model is forced to look at a person rather
than use contextual cues to make a gender specific prediction. The losses that
comprise our model, the Appearance Confusion Loss and the Confident Loss, are
general, and can be added to any description model in order to mitigate impacts
of unwanted bias in a description dataset. Our proposed model has lower error
than prior work when describing images with people and mentioning their gender
and more closely matches the ground truth ratio of sentences including women to
sentences including men.
Balanced Distribution Adaptation for Transfer Learning
Authors: Jindong Wang, Yiqiang Chen, Shuji Hao, Wenjie Feng, Zhiqi Shen
Date: 2018-07-02 00:00:00
Tasks: Transfer Learning

Transfer learning has achieved promising results by leveraging knowledge from
the source domain to annotate the target domain which has few or none labels.
Existing methods often seek to minimize the distribution divergence between
domains, such as the marginal distribution, the conditional distribution or
both. However, these two distances are often treated equally in existing
algorithms, which will result in poor performance in real applications.
Moreover, existing methods usually assume that the dataset is balanced, which
also limits their performances on imbalanced tasks that are quite common in
real problems. To tackle the distribution adaptation problem, in this paper, we
propose a novel transfer learning approach, named as Balanced Distribution
\underline{A}daptation~(BDA), which can adaptively leverage the importance of
the marginal and conditional distribution discrepancies, and several existing
methods can be treated as special cases of BDA. Based on BDA, we also propose a
novel Weighted Balanced Distribution Adaptation~(W-BDA) algorithm to tackle the
class imbalance issue in transfer learning. W-BDA not only considers the
distribution adaptation between domains but also adaptively changes the weight
of each class. To evaluate the proposed methods, we conduct extensive
experiments on several transfer learning tasks, which demonstrate the
effectiveness of our proposed algorithms over several state-of-the-art methods.
COSMO: Contextualized Scene Modeling with Boltzmann Machines
Authors: Ilker Bozcan, Sinan Kalkan
Date: 2018-07-02 00:00:00
Tasks: object-detection, Object Detection, Relation

Scene modeling is very crucial for robots that need to perceive, reason about
and manipulate the objects in their environments. In this paper, we adapt and
extend Boltzmann Machines (BMs) for contextualized scene modeling. Although
there are many models on the subject, ours is the first to bring together
objects, relations, and affordances in a highly-capable generative model. For
this end, we introduce a hybrid version of BMs where relations and affordances
are introduced with shared, tri-way connections into the model. Moreover, we
contribute a dataset for relation estimation and modeling studies. We evaluate
our method in comparison with several baselines on object estimation,
out-of-context object detection, relation estimation, and affordance estimation
tasks. Moreover, to illustrate the generative capability of the model, we show
several example scenes that the model is able to generate.
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition
Authors: Tianshui Chen, Liang Lin, Riquan Chen, Yang Wu, Xiaonan Luo
Date: 2018-07-02 00:00:00
Tasks: Fine-Grained Image Classification, Fine-Grained Image Recognition, Graph Neural Network, image-classification, Image Classification, Representation Learning

Humans can naturally understand an image in depth with the aid of rich
knowledge accumulated from daily lives or professions. For example, to achieve
fine-grained image recognition (e.g., categorizing hundreds of subordinate
categories of birds) usually requires a comprehensive visual concept
organization including category labels and part-level attributes. In this work,
we investigate how to unify rich professional knowledge with deep neural
network architectures and propose a Knowledge-Embedded Representation Learning
(KERL) framework for handling the problem of fine-grained image recognition.
Specifically, we organize the rich visual concepts in the form of knowledge
graph and employ a Gated Graph Neural Network to propagate node message through
the graph for generating the knowledge representation. By introducing a novel
gated mechanism, our KERL framework incorporates this knowledge representation
into the discriminative image feature learning, i.e., implicitly associating
the specific attributes with the feature maps. Compared with existing methods
of fine-grained image classification, our KERL framework has several appealing
properties: i) The embedded high-level knowledge enhances the feature
representation, thus facilitating distinguishing the subtle differences among
subordinate categories. ii) Our framework can learn feature maps with a
meaningful configuration that the highlighted regions finely accord with the
nodes (specific attributes) of the knowledge graph. Extensive experiments on
the widely used Caltech-UCSD bird dataset demonstrate the superiority of our
KERL framework over existing state-of-the-art methods.
Deep Reasoning with Knowledge Graph for Social Relationship Understanding
Authors: Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin
Date: 2018-07-02 00:00:00
Tasks: Visual Social Relationship Recognition

Social relationships (e.g., friends, couple etc.) form the basis of the
social network in our daily life. Automatically interpreting such relationships
bears a great potential for the intelligent systems to understand human
behavior in depth and to better interact with people at a social level. Human
beings interpret the social relationships within a group not only based on the
people alone, and the interplay between such social relationships and the
contextual information around the people also plays a significant role.
However, these additional cues are largely overlooked by the previous studies.
We found that the interplay between these two factors can be effectively
modeled by a novel structured knowledge graph with proper message propagation
and attention. And this structured knowledge can be efficiently integrated into
the deep neural network architecture to promote social relationship
understanding by an end-to-end trainable Graph Reasoning Model (GRM), in which
a propagation mechanism is learned to propagate node message through the graph
to explore the interaction between persons of interest and the contextual
objects. Meanwhile, a graph attentional mechanism is introduced to explicitly
reason about the discriminative objects to promote recognition. Extensive
experiments on the public benchmarks demonstrate the superiority of our method
over the existing leading competitors.
Leveraging Uncertainty Estimates for Predicting Segmentation Quality
Authors: Terrance DeVries, Graham W. Taylor
Date: 2018-07-02 00:00:00
Tasks: Deep Learning, Segmentation

The use of deep learning for medical imaging has seen tremendous growth in
the research community. One reason for the slow uptake of these systems in the
clinical setting is that they are complex, opaque and tend to fail silently.
Outside of the medical imaging domain, the machine learning community has
recently proposed several techniques for quantifying model uncertainty (i.e.~a
model knowing when it has failed). This is important in practical settings, as
we can refer such cases to manual inspection or correction by humans. In this
paper, we aim to bring these recent results on estimating uncertainty to bear
on two important outputs in deep learning-based segmentation. The first is
producing spatial uncertainty maps, from which a clinician can observe where
and why a system thinks it is failing. The second is quantifying an image-level
prediction of failure, which is useful for isolating specific cases and
removing them from automated pipelines. We also show that reasoning about
spatial uncertainty, the first output, is a useful intermediate representation
for generating segmentation quality predictions, the second output. We propose
a two-stage architecture for producing these measures of uncertainty, which can
accommodate any deep learning-based medical segmentation pipeline.
Estimating Phenotypic Traits From UAV Based RGB Imagery
Authors: Javier Ribera, Fangning He, Yuhao Chen, Ayman F. Habib, Edward J. Delp
Date: 2018-07-02 00:00:00
Tasks: Position

In many agricultural applications one wants to characterize physical
properties of plants and use the measurements to predict, for example biomass
and environmental influence. This process is known as phenotyping. Traditional
collection of phenotypic information is labor-intensive and time-consuming. Use
of imagery is becoming popular for phenotyping. In this paper, we present
methods to estimate traits of sorghum plants from RBG cameras on board of an
unmanned aerial vehicle (UAV). The position and orientation of the imagery
together with the coordinates of sparse points along the area of interest are
derived through a new triangulation method. A rectified orthophoto mosaic is
then generated from the imagery. The number of leaves is estimated and a
model-based method to analyze the leaf morphology for leaf segmentation is
proposed. We present a statistical model to find the location of each
individual sorghum plant.
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Authors: Phuc Nguyen, Deva Ramanan, Charless Fowlkes
Date: 2018-07-02 00:00:00
Proceeding: ICML 2018 7
Tasks: Instance Segmentation, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Semantic Segmentation

Much recent work on visual recognition aims to scale up learning to massive,
noisily-annotated datasets. We address the problem of scaling- up the
evaluation of such models to large-scale datasets with noisy labels. Current
protocols for doing so require a human user to either vet (re-annotate) a small
fraction of the test set and ignore the rest, or else correct errors in
annotation as they are found through manual inspection of results. In this
work, we re-formulate the problem as one of active testing, and examine
strategies for efficiently querying a user so as to obtain an accu- rate
performance estimate with minimal vetting. We demonstrate the effectiveness of
our proposed active testing framework on estimating two performance metrics,
Precision@K and mean Average Precision, for two popular computer vision tasks,
multi-label classification and instance segmentation. We further show that our
approach is able to save significant human annotation effort and is more robust
than alternative evaluation protocols.
A Simple but Effective Classification Model for Grammatical Error Correction
Authors: Zhu Kaili, Chuan Wang, Ruobing Li, Yang Liu, Tianlei Hu, Hui Lin
Date: 2018-07-02 00:00:00
Tasks: Classification, General Classification, Grammatical Error Correction

We treat grammatical error correction (GEC) as a classification problem in
this study, where for different types of errors, a target word is identified,
and the classifier predicts the correct word form from a set of possible
choices. We propose a novel neural network based feature representation and
classification model, trained using large text corpora without human
annotations. Specifically we use RNNs with attention to represent both the left
and right context of a target word. All feature embeddings are learned jointly
in an end-to-end fashion. Experimental results show that our novel approach
outperforms other classifier methods on the CoNLL-2014 test set (F0.5 45.05%).
Our model is simple but effective, and is suitable for industrial production.
An initial study on estimating area of a leaf using image processing
Authors: G. D. Illeperuma
Date: 2018-07-02 00:00:00

Calculating leaf area is very important. Computer aided image processing can
make this faster and more accurate. This include scanning the leaf , converting
it to binary image and calculation of number of pixels covered. Later this is
converted to mm2.
Tap-based User Authentication for Smartwatches
Authors: Toan Nguyen, Nasir Memon
Date: 2018-07-02 00:00:00

This paper presents TapMeIn, an eyes-free, two-factor authentication method
for smartwatches. It allows users to tap a memorable melody (tap-password) of
their choice anywhere on the touchscreen to unlock their watch. A user is
verified based on the tap-password as well as her physiological and behavioral
characteristics when tapping. Results from preliminary experiments with 41
participants show that TapMeIn could achieve an accuracy of 98.7% with a False
Positive Rate of only 0.98%. In addition, TapMeIn retains its performance in
different conditions such as sitting and walking. In terms of speed, TapMeIn
has an average authentication time of 2 seconds. A user study with the System
Usability Scale (SUS) tool suggests that TapMeIn has a high usability score.
FATE: Fast and Accurate Timing Error Prediction Framework for Low Power DNN Accelerator Design
Authors: Jeff Zhang, Siddharth Garg
Date: 2018-07-02 00:00:00
Tasks: General Classification

Deep neural networks (DNN) are increasingly being accelerated on
application-specific hardware such as the Google TPU designed especially for
deep learning. Timing speculation is a promising approach to further increase
the energy efficiency of DNN accelerators. Architectural exploration for timing
speculation requires detailed gate-level timing simulations that can be
time-consuming for large DNNs that execute millions of multiply-and-accumulate
(MAC) operations. In this paper we propose FATE, a new methodology for fast and
accurate timing simulations of DNN accelerators like the Google TPU. FATE
proposes two novel ideas: (i) DelayNet, a DNN based timing model for MAC units;
and (ii) a statistical sampling methodology that reduces the number of MAC
operations for which timing simulations are performed. We show that FATE
results in between 8 times-58 times speed-up in timing simulations, while
introducing less than 2% error in classification accuracy estimates. We
demonstrate the use of FATE by comparing to conventional DNN accelerator that
uses 2's complement (2C) arithmetic with an alternative implementation that
uses signed magnitude representations (SMR). We show that that the SMR
implementation provides 18% more energy savings for the same classification
accuracy than 2C, a result that might be of independent interest.