pwc-archive/papers-with-abstracts huggingface dataset:
A Spatial and Temporal Features Mixture Model with Body Parts for Video-based Person Re-Identification
Authors: Jie Liu, Cheng Sun, Xiang Xu, Baomin Xu, Shuangyuan Yu
Date: 2018-07-03 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

The video-based person re-identification is to recognize a person under
different cameras, which is a crucial task applied in visual surveillance
system. Most previous methods mainly focused on the feature of full body in the
frame. In this paper we propose a novel Spatial and Temporal Features Mixture
Model (STFMM) based on convolutional neural network (CNN) and recurrent neural
network (RNN), in which the human body is split into $N$ parts in horizontal
direction so that we can obtain more specific features. The proposed method
skillfully integrates features of each part to achieve more expressive
representation of each person. We first split the video sequence into $N$ part
sequences which include the information of head, waist, legs and so on. Then
the features are extracted by STFMM whose $2N$ inputs are obtained from the
developed Siamese network, and these features are combined into a
discriminative representation for one person. Experiments are conducted on the
iLIDS-VID and PRID-2011 datasets. The results demonstrate that our approach
outperforms existing methods for video-based person re-identification. It
achieves a rank-1 CMC accuracy of 74\% on the iLIDS-VID dataset, exceeding the
the most recently developed method ASTPN by 12\%. For the cross-data testing,
our method achieves a rank-1 CMC accuracy of 48\% exceeding the ASTPN method by
18\%, which shows that our model has significant stability.
Scalable Structure Learning for Probabilistic Soft Logic
Authors: Varun Embar, Dhanya Sridhar, Golnoosh Farnadi, Lise Getoor
Date: 2018-07-03 00:00:00

Statistical relational frameworks such as Markov logic networks and
probabilistic soft logic (PSL) encode model structure with weighted first-order
logical clauses. Learning these clauses from data is referred to as structure
learning. Structure learning alleviates the manual cost of specifying models.
However, this benefit comes with high computational costs; structure learning
typically requires an expensive search over the space of clauses which involves
repeated optimization of clause weights. In this paper, we propose the first
two approaches to structure learning for PSL. We introduce a greedy
search-based algorithm and a novel optimization method that trade-off
scalability and approximations to the structure learning problem in varying
ways. The highly scalable optimization method combines data-driven generation
of clauses with a piecewise pseudolikelihood (PPLL) objective that learns model
structure by optimizing clause weights only once. We compare both methods
across five real-world tasks, showing that PPLL achieves an order of magnitude
runtime speedup and AUC gains up to 15% over greedy search.
Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors
Authors: Xuanyi Dong, Shoou-I Yu, Xinshuo Weng, Shih-En Wei, Yi Yang, Yaser Sheikh
Date: 2018-07-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Facial Landmark Detection, Optical Flow Estimation

In this paper, we present supervision-by-registration, an unsupervised
approach to improve the precision of facial landmark detectors on both images
and video. Our key observation is that the detections of the same landmark in
adjacent frames should be coherent with registration, i.e., optical flow.
Interestingly, the coherency of optical flow is a source of supervision that
does not require manual labeling, and can be leveraged during detector
training. For example, we can enforce in the training loss function that a
detected landmark at frame$_{t-1}$ followed by optical flow tracking from
frame$_{t-1}$ to frame$_t$ should coincide with the location of the detection
at frame$_t$. Essentially, supervision-by-registration augments the training
loss function with a registration loss, thus training the detector to have
output that is not only close to the annotations in labeled images, but also
consistent with registration on large amounts of unlabeled videos. End-to-end
training with the registration loss is made possible by a differentiable
Lucas-Kanade operation, which computes optical flow registration in the forward
pass, and back-propagates gradients that encourage temporal coherency in the
detector. The output of our method is a more precise image-based facial
landmark detector, which can be applied to single images or video. With
supervision-by-registration, we demonstrate (1) improvements in facial landmark
detection on both images (300W, ALFW) and video (300VW, Youtube-Celebrities),
and (2) significant reduction of jittering in video detections.
SymmNet: A Symmetric Convolutional Neural Network for Occlusion Detection
Authors: Ang Li, Zejian yuan
Date: 2018-07-03 00:00:00
Tasks: Optical Flow Estimation

Detecting the occlusion from stereo images or video frames is important to
many computer vision applications. Previous efforts focus on bundling it with
the computation of disparity or optical flow, leading to a chicken-and-egg
problem. In this paper, we leverage convolutional neural network to liberate
the occlusion detection task from the interleaved, traditional calculation
framework. We propose a Symmetric Network (SymmNet) to directly exploit
information from an image pair, without estimating disparity or motion in
advance. The proposed network is structurally left-right symmetric to learn the
binocular occlusion simultaneously, aimed at jointly improving both results.
The comprehensive experiments show that our model achieves state-of-the-art
results on detecting the stereo and motion occlusion.
Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays
Authors: Jinzheng Cai, Le Lu, Adam P. Harrison, Xiaoshuang Shi, Pingjun Chen, Lin Yang
Date: 2018-07-03 00:00:00
Tasks: General Classification, image-classification, Image Classification

Given image labels as the only supervisory signal, we focus on harvesting, or
mining, thoracic disease localizations from chest X-ray images. Harvesting such
localizations from existing datasets allows for the creation of improved data
sources for computer-aided diagnosis and retrospective analyses. We train a
convolutional neural network (CNN) for image classification and propose an
attention mining (AM) strategy to improve the model's sensitivity or saliency
to disease patterns. The intuition of AM is that once the most salient disease
area is blocked or hidden from the CNN model, it will pay attention to
alternative image regions, while still attempting to make correct predictions.
However, the model requires to be properly constrained during AM, otherwise, it
may overfit to uncorrelated image parts and forget the valuable knowledge that
it has learned from the original image classification task. To alleviate such
side effects, we then design a knowledge preservation (KP) loss, which
minimizes the discrepancy between responses for X-ray images from the original
and the updated networks. Furthermore, we modify the CNN model to include
multi-scale aggregation (MSA), improving its localization ability on
small-scale disease findings, e.g., lung nodules. We experimentally validate
our method on the publicly-available ChestX-ray14 dataset, outperforming a
class activation map (CAM)-based approach, and demonstrating the value of our
novel framework for mining disease locations.
Ballistocardiogram Signal Processing: A Literature Review
Authors: Ibrahim Sadek
Date: 2018-07-03 00:00:00
Tasks: Heart Rate Variability

Time-domain algorithms are focused on detecting local maxima or local minima
using a moving window, and therefore finding the interval between the dominant
J-peaks of ballistocardiogram (BCG) signal. However, this approach has many
limitations due to the nonlinear and nonstationary behavior of the BCG signal.
This is because the BCG signal does not display consistent J-peaks, which can
usually be the case for overnight, in-home monitoring, particularly with frail
elderly. Additionally, its accuracy will be undoubtedly affected by motion
artifacts. Second, frequency-domain algorithms do not provide information about
interbeat intervals. Nevertheless, they can provide information about heart
rate variability. This is usually done by taking the fast Fourier transform or
the inverse Fourier transform of the logarithm of the estimated spectrum, i.e.,
cepstrum of the signal using a sliding window. Thereafter, the dominant
frequency is obtained in a particular frequency range. The limit of these
algorithms is that the peak in the spectrum may get wider and multiple peaks
may appear, which might cause a problem in measuring the vital signs. At last,
the objective of wavelet-domain algorithms is to decompose the signal into
different components, hence the component which shows an agreement with the
vital signs can be selected i.e., the selected component contains only
information about the heart cycles or respiratory cycles, respectively. An
empirical mode decomposition is an alternative approach to wavelet
decomposition, and it is also a very suitable approach to cope with nonlinear
and nonstationary signals such as cardiorespiratory signals. Apart from the
above-mentioned algorithms, machine learning approaches have been implemented
for measuring heartbeats. However, manual labeling of training data is a
restricting property.
Resembled Generative Adversarial Networks: Two Domains with Similar Attributes
Authors: Duhyeon Bang, Hyunjung Shim
Date: 2018-07-03 00:00:00
Tasks: Vocal Bursts Valence Prediction

We propose a novel algorithm, namely Resembled Generative Adversarial
Networks (GAN), that generates two different domain data simultaneously where
they resemble each other. Although recent GAN algorithms achieve the great
success in learning the cross-domain relationship, their application is limited
to domain transfers, which requires the input image. The first attempt to
tackle the data generation of two domains was proposed by CoGAN. However, their
solution is inherently vulnerable for various levels of domain similarities.
Unlike CoGAN, our Resembled GAN implicitly induces two generators to match
feature covariance from both domains, thus leading to share semantic
attributes. Hence, we effectively handle a wide range of structural and
semantic similarities between various two domains. Based on experimental
analysis on various datasets, we verify that the proposed algorithm is
effective for generating two domains with similar attributes.
Structure Learning of Markov Random Fields through Grow-Shrink Maximum Pseudolikelihood Estimation
Authors: Yuya Takashina, Shuyo Nakatani, Masato Inoue
Date: 2018-07-03 00:00:00

Learning the structure of Markov random fields (MRFs) plays an important role
in multivariate analysis. The importance has been increasing with the recent
rise of statistical relational models since the MRF serves as a building block
of these models such as Markov logic networks. There are two fundamental ways
to learn structures of MRFs: methods based on parameter learning and those
based on independence test. The former methods more or less assume certain
forms of distribution, so they potentially perform poorly when the assumption
is not satisfied. The latter can learn an MRF structure without a strong
distributional assumption, but sometimes it is unclear what objective function
is maximized/minimized in these methods. In this paper, we follow the latter,
but we explicitly define the optimization problem of MRF structure learning as
maximum pseudolikelihood estimation (MPLE) with respect to the edge set. As a
result, the proposed solution successfully deals with the {\em symmetricity} in
MRFs, whereas such symmetricity is not taken into account in most existing
independence test techniques. The proposed method achieved higher accuracy than
previous methods when there were asymmetric dependencies in our experiments.
Stochastic Layer-Wise Precision in Deep Neural Networks
Authors: Griffin Lacey, Graham W. Taylor, Shawki Areibi
Date: 2018-07-03 00:00:00
Tasks: Computational Efficiency

Low precision weights, activations, and gradients have been proposed as a way
to improve the computational efficiency and memory footprint of deep neural
networks. Recently, low precision networks have even shown to be more robust to
adversarial attacks. However, typical implementations of low precision DNNs use
uniform precision across all layers of the network. In this work, we explore
whether a heterogeneous allocation of precision across a network leads to
improved performance, and introduce a learning scheme where a DNN
stochastically explores multiple precision configurations through learning.
This permits a network to learn an optimal precision configuration. We show on
convolutional neural networks trained on MNIST and ILSVRC12 that even though
these nets learn a uniform or near-uniform allocation strategy respectively,
stochastic precision leads to a favourable regularization effect improving
generalization.
Topic Discovery in Massive Text Corpora Based on Min-Hashing
Authors: Gibran Fuentes-Pineda, Ivan Vladimir Meza-Ruiz
Date: 2018-07-03 00:00:00
Tasks: Topic Models

The task of discovering topics in text corpora has been dominated by Latent Dirichlet Allocation and other Topic Models for over a decade. In order to apply these approaches to massive text corpora, the vocabulary needs to be reduced considerably and large computer clusters and/or GPUs are typically required. Moreover, the number of topics must be provided beforehand but this depends on the corpus characteristics and it is often difficult to estimate, especially for massive text corpora. Unfortunately, both topic quality and time complexity are sensitive to this choice. This paper describes an alternative approach to discover topics based on Min-Hashing, which can handle massive text corpora and large vocabularies using modest computer hardware and does not require to fix the number of topics in advance. The basic idea is to generate multiple random partitions of the corpus vocabulary to find sets of highly co-occurring words, which are then clustered to produce the final topics. In contrast to probabilistic topic models where topics are distributions over the complete vocabulary, the topics discovered by the proposed approach are sets of highly co-occurring words. Interestingly, these topics underlie various thematics with different levels of granularity. An extensive qualitative and quantitative evaluation using the 20 Newsgroups (18K), Reuters (800K), Spanish Wikipedia (1M), and English Wikipedia (5M) corpora shows that the proposed approach is able to consistently discover meaningful and coherent topics. Remarkably, the time complexity of the proposed approach is linear with respect to corpus and vocabulary size; a non-parallel implementation was able to discover topics from the entire English edition of Wikipedia with over 5 million documents and 1 million words in less than 7 hours.
Neural Random Projections for Language Modelling
Authors: Davide Nunes, Luis Antunes
Date: 2018-07-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Language Modelling

Neural network-based language models deal with data sparsity problems by
mapping the large discrete space of words into a smaller continuous space of
real-valued vectors. By learning distributed vector representations for words,
each training sample informs the neural network model about a combinatorial
number of other patterns. In this paper, we exploit the sparsity in natural
language even further by encoding each unique input word using a fixed sparse
random representation. These sparse codes are then projected onto a smaller
embedding space which allows for the encoding of word occurrences from a
possibly unknown vocabulary, along with the creation of more compact language
models using a reduced number of parameters. We investigate the properties of
our encoding mechanism empirically, by evaluating its performance on the widely
used Penn Treebank corpus. We show that guaranteeing approximately equidistant
(nearly orthogonal) vector representations for unique discrete inputs is enough
to provide the neural network model with enough information to learn --and make
use-- of distributed representations for these inputs.
Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data
Authors: Li Sun, Zhi Yan, Anestis Zaganidis, Cheng Zhao, Tom Duckett
Date: 2018-07-02 00:00:00

This paper presents a novel semantic mapping approach, Recurrent-OctoMap,
learned from long-term 3D Lidar data. Most existing semantic mapping approaches
focus on improving semantic understanding of single frames, rather than 3D
refinement of semantic maps (i.e. fusing semantic observations). The most
widely-used approach for 3D semantic map refinement is a Bayesian update, which
fuses the consecutive predictive probabilities following a Markov-Chain model.
Instead, we propose a learning approach to fuse the semantic features, rather
than simply fusing predictions from a classifier. In our approach, we represent
and maintain our 3D map as an OctoMap, and model each cell as a recurrent
neural network (RNN), to obtain a Recurrent-OctoMap. In this case, the semantic
mapping process can be formulated as a sequence-to-sequence encoding-decoding
problem. Moreover, in order to extend the duration of observations in our
Recurrent-OctoMap, we developed a robust 3D localization and mapping system for
successively mapping a dynamic environment using more than two weeks of data,
and the system can be trained and deployed with arbitrary memory length. We
validate our approach on the ETH long-term 3D Lidar dataset [1]. The
experimental results show that our proposed approach outperforms the
conventional "Bayesian update" approach.
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Authors: Edoardo Maria Ponti, Helen O'Horan, Yevgeni Berzak, Ivan Vulić, Roi Reichart, Thierry Poibeau, Ekaterina Shutova, Anna Korhonen
Date: 2018-07-02 00:00:00
Proceeding: CL 2019 9
Tasks: Cross-Lingual Transfer, Survey

Linguistic typology aims to capture structural and semantic variation across the world's languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-employment of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such approach could be facilitated by recent developments in data-driven induction of typological knowledge.
Semantic Segmentation with Scarce Data
Authors: Isay Katsman, Rohun Tripathi, Andreas Veit, Serge Belongie
Date: 2018-07-02 00:00:00
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation is a challenging vision problem that usually
necessitates the collection of large amounts of finely annotated data, which is
often quite expensive to obtain. Coarsely annotated data provides an
interesting alternative as it is usually substantially more cheap. In this
work, we present a method to leverage coarsely annotated data along with fine
supervision to produce better segmentation results than would be obtained when
training using only the fine data. We validate our approach by simulating a
scarce data setting with less than 200 low resolution images from the
Cityscapes dataset and show that our method substantially outperforms solely
training on the fine annotation data by an average of 15.52% mIoU and
outperforms the coarse mask by an average of 5.28% mIoU.
Uncertainty in the Variational Information Bottleneck
Authors: Alexander A. Alemi, Ian Fischer, Joshua V. Dillon
Date: 2018-07-02 00:00:00
Tasks: General Classification

We present a simple case study, demonstrating that Variational Information
Bottleneck (VIB) can improve a network's classification calibration as well as
its ability to detect out-of-distribution data. Without explicitly being
designed to do so, VIB gives two natural metrics for handling and quantifying
uncertainty.
Learning under selective labels in the presence of expert consistency
Authors: Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova
Date: 2018-07-02 00:00:00
Tasks: Data Augmentation, Decision Making, Selection bias

We explore the problem of learning under selective labels in the context of
algorithm-assisted decision making. Selective labels is a pervasive selection
bias problem that arises when historical decision making blinds us to the true
outcome for certain instances. Examples of this are common in many
applications, ranging from predicting recidivism using pre-trial release data
to diagnosing patients. In this paper we discuss why selective labels often
cannot be effectively tackled by standard methods for adjusting for sample
selection bias, even if there are no unobservables. We propose a data
augmentation approach that can be used to either leverage expert consistency to
mitigate the partial blindness that results from selective labels, or to
empirically validate whether learning under such framework may lead to
unreliable models prone to systemic discrimination.
Analysis and Optimization of Deep Counterfactual Value Networks
Authors: Patryk Hopner, Eneldo Loza Mencía
Date: 2018-07-02 00:00:00
Tasks: counterfactual

Recently a strong poker-playing algorithm called DeepStack was published,
which is able to find an approximate Nash equilibrium during gameplay by using
heuristic values of future states predicted by deep neural networks. This paper
analyzes new ways of encoding the inputs and outputs of DeepStack's deep
counterfactual value networks based on traditional abstraction techniques, as
well as an unabstracted encoding, which was able to increase the network's
accuracy.
Optimality and Sub-optimality of PCA I: Spiked Random Matrix Models
Authors: Amelia Perry, Alexander S. Wein, Afonso S. Bandeira, Ankur Moitra
Date: 2018-07-02 00:00:00

A central problem of random matrix theory is to understand the eigenvalues of
spiked random matrix models, introduced by Johnstone, in which a prominent
eigenvector (or "spike") is planted into a random matrix. These distributions
form natural statistical models for principal component analysis (PCA) problems
throughout the sciences. Baik, Ben Arous and Peche showed that the spiked
Wishart ensemble exhibits a sharp phase transition asymptotically: when the
spike strength is above a critical threshold, it is possible to detect the
presence of a spike based on the top eigenvalue, and below the threshold the
top eigenvalue provides no information. Such results form the basis of our
understanding of when PCA can detect a low-rank signal in the presence of
noise. However, under structural assumptions on the spike, not all information
is necessarily contained in the spectrum. We study the statistical limits of
tests for the presence of a spike, including non-spectral tests. Our results
leverage Le Cam's notion of contiguity, and include:
  i) For the Gaussian Wigner ensemble, we show that PCA achieves the optimal
detection threshold for certain natural priors for the spike.
  ii) For any non-Gaussian Wigner ensemble, PCA is sub-optimal for detection.
However, an efficient variant of PCA achieves the optimal threshold (for
natural priors) by pre-transforming the matrix entries.
  iii) For the Gaussian Wishart ensemble, the PCA threshold is optimal for
positive spikes (for natural priors) but this is not always the case for
negative spikes.