pwc-archive/papers-with-abstracts huggingface dataset:
Assessing Capsule Networks With Biased Data
Authors: Bruno Ferrarini, Shoaib Ehsan, Adrien Bartoli, Aleš Leonardis, Klaus D. McDonald-Maier
Date: 2019-04-09 00:00:00

Machine learning based methods achieves impressive results in object
classification and detection. Utilizing representative data of the visual world
during the training phase is crucial to achieve good performance with such data
driven approaches. However, it not always possible to access bias-free datasets
thus, robustness to biased data is a desirable property for a learning system.
Capsule Networks have been introduced recently and their tolerance to biased
data has received little attention. This paper aims to fill this gap and
proposes two experimental scenarios to assess the tolerance to imbalanced
training data and to determine the generalization performance of a model with
unfamiliar affine transformations of the images. This paper assesses dynamic
routing and EM routing based Capsule Networks and proposes a comparison with
Convolutional Neural Networks in the two tested scenarios. The presented
results provide new insights into the behaviour of capsule networks.
PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral images
Authors: Anirban Santara, Jayeeta Datta, Sourav Sarkar, Ankur Garg, Kirti Padia, Pabitra Mitra
Date: 2019-04-09 00:00:00
Proceeding: Submitted to ACMMM-2019 2019 4
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, Information Retrieval, Retrieval

Hyperspectral images of land-cover captured by airborne or satellite-mounted
sensors provide a rich source of information about the chemical composition of
the materials present in a given place. This makes hyperspectral imaging an
important tool for earth sciences, land-cover studies, and military and
strategic applications. However, the scarcity of labeled training examples and
spatial variability of spectral signature are two of the biggest challenges
faced by hyperspectral image classification. In order to address these issues,
we aim to develop a framework for material-agnostic information retrieval in
hyperspectral images based on Positive-Unlabelled (PU) classification. Given a
hyperspectral scene, the user labels some positive samples of a material he/she
is looking for and our goal is to retrieve all the remaining instances of the
query material in the scene. Additionally, we require the system to work
equally well for any material in any scene without the user having to disclose
the identity of the query material. This material-agnostic nature of the
framework provides it with superior generalization abilities. We explore two
alternative approaches to solve the hyperspectral image classification problem
within this framework. The first approach is an adaptation of non-negative risk
estimation based PU learning for hyperspectral data. The second approach is
based on one-versus-all positive-negative classification where the negative
class is approximately sampled using a novel spectral-spatial retrieval model.
We propose two annotator models - uniform and blob - that represent the
labelling patterns of a human annotator. We compare the performances of the
proposed algorithms for each annotator model on three benchmark hyperspectral
image datasets - Indian Pines, Pavia University and Salinas.
Crossmodal Voice Conversion
Authors: Hirokazu Kameoka, Kou Tanaka, Aaron Valero Puche, Yasunori Ohishi, Takuhiro Kaneko
Date: 2019-04-09 00:00:00
Tasks: Decoder, Voice Conversion

Humans are able to imagine a person's voice from the person's appearance and
imagine the person's appearance from his/her voice. In this paper, we make the
first attempt to develop a method that can convert speech into a voice that
matches an input face image and generate a face image that matches the voice of
the input speech by leveraging the correlation between faces and voices. We
propose a model, consisting of a speech converter, a face encoder/decoder and a
voice encoder. We use the latent code of an input face image encoded by the
face encoder as the auxiliary input into the speech converter and train the
speech converter so that the original latent code can be recovered from the
generated speech by the voice encoder. We also train the face decoder along
with the face encoder to ensure that the latent code will contain sufficient
information to reconstruct the input face image. We confirmed experimentally
that a speech converter trained in this way was able to convert input speech
into a voice that matched an input face image and that the voice encoder and
face decoder can be used to generate a face image that matches the voice of the
input speech.
Regression Concept Vectors for Bidirectional Explanations in Histopathology
Authors: Mara Graziani, Vincent Andrearczyk, Henning Müller
Date: 2019-04-09 00:00:00
Tasks: Breast Cancer Detection, Breast Cancer Histology Image Classification, Decision Making, Histopathological Image Classification, regression, Sensitivity

Explanations for deep neural network predictions in terms of domain-related
concepts can be valuable in medical applications, where justifications are
important for confidence in the decision-making. In this work, we propose a
methodology to exploit continuous concept measures as Regression Concept
Vectors (RCVs) in the activation space of a layer. The directional derivative
of the decision function along the RCVs represents the network sensitivity to
increasing values of a given concept measure. When applied to breast cancer
grading, nuclei texture emerges as a relevant concept in the detection of tumor
tissue in breast lymph node samples. We evaluate score robustness and
consistency by statistical analysis.
Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images
Authors: Matthias Rottmann, Marius Schubert
Date: 2019-04-09 00:00:00
Tasks: General Classification, regression, Segmentation, Semantic Segmentation

In the semantic segmentation of street scenes the reliability of the
prediction and therefore uncertainty measures are of highest interest. We
present a method that generates for each input image a hierarchy of nested
crops around the image center and presents these, all re-scaled to the same
size, to a neural network for semantic segmentation. The resulting softmax
outputs are then post processed such that we can investigate mean and variance
over all image crops as well as mean and variance of uncertainty heat maps
obtained from pixel-wise uncertainty measures, like the entropy, applied to
each crop's softmax output. In our tests, we use the publicly available
DeepLabv3+ MobilenetV2 network (trained on the Cityscapes dataset) and
demonstrate that the incorporation of crops improves the quality of the
prediction and that we obtain more reliable uncertainty measures. These are
then aggregated over predicted segments for either classifying between IoU=0
and IoU>0 (meta classification) or predicting the IoU via linear regression
(meta regression). The latter yields reliable performance estimates for
segmentation networks, in particular useful in the absence of ground truth. For
the task of meta classification we obtain a classification accuracy of
$81.93\%$ and an AUROC of $89.89\%$. For meta regression we obtain an $R^2$
value of $84.77\%$. These results yield significant improvements compared to
other approaches.
High-Resolution Representations for Labeling Pixels and Regions
Authors: Ke Sun, Yang Zhao, Borui Jiang, Tianheng Cheng, Bin Xiao, Dong Liu, Yadong Mu, Xinggang Wang, Wenyu Liu, Jingdong Wang
Date: 2019-04-09 00:00:00
Tasks: Face Alignment, Facial Landmark Detection, Object Detection, Pose Estimation, Representation Learning, Semantic Segmentation, Vocal Bursts Intensity Prediction

High-resolution representation learning plays an essential role in many
vision problems, e.g., pose estimation and semantic segmentation. The
high-resolution network (HRNet)~\cite{SunXLW19}, recently developed for human
pose estimation, maintains high-resolution representations through the whole
process by connecting high-to-low resolution convolutions in \emph{parallel}
and produces strong high-resolution representations by repeatedly conducting
fusions across parallel convolutions.
  In this paper, we conduct a further study on high-resolution representations
by introducing a simple yet effective modification and apply it to a wide range
of vision tasks. We augment the high-resolution representation by aggregating
the (upsampled) representations from all the parallel convolutions rather than
only the representation from the high-resolution convolution as done
in~\cite{SunXLW19}. This simple modification leads to stronger representations,
evidenced by superior results. We show top results in semantic segmentation on
Cityscapes, LIP, and PASCAL Context, and facial landmark detection on AFLW,
COFW, $300$W, and WFLW. In addition, we build a multi-level representation from
the high-resolution representation and apply it to the Faster R-CNN object
detection framework and the extended frameworks. The proposed approach achieves
superior results to existing single-model networks on COCO object detection.
The code and models have been publicly available at
\url{https://github.com/HRNet}.
A Hierarchical Decoding Model For Spoken Language Understanding From Unaligned Data
Authors: Zijian Zhao, Su Zhu, Kai Yu
Date: 2019-04-09 00:00:00
Tasks: Spoken Language Understanding

Spoken language understanding (SLU) systems can be trained on two types of
labelled data: aligned or unaligned. Unaligned data do not require word by word
annotation and is easier to be obtained. In the paper, we focus on spoken
language understanding from unaligned data whose annotation is a set of
act-slot-value triples. Previous works usually focus on improve slot-value pair
prediction and estimate dialogue act types separately, which ignores the
hierarchical structure of the act-slot-value triples. Here, we propose a novel
hierarchical decoding model which dynamically parses act, slot and value in a
structured way and employs pointer network to handle out-of-vocabulary (OOV)
values. Experiments on DSTC2 dataset, a benchmark unaligned dataset, show that
the proposed model not only outperforms previous state-of-the-art model, but
also can be generalized effectively and efficiently to unseen act-slot type
pairs and OOV values.
Convolutional Temporal Attention Model for Video-based Person Re-identification
Authors: Tanzila Rahman, Mrigank Rochan, Yang Wang
Date: 2019-04-09 00:00:00
Tasks: Person Re-Identification, Semantic Segmentation, Video-Based Person Re-Identification

The goal of video-based person re-identification is to match two input
videos, so that the distance of the two videos is small if two videos contain
the same person. A common approach for person re-identification is to first
extract image features for all frames in the video, then aggregate all the
features to form a video-level feature. The video-level features of two videos
can then be used to calculate the distance of the two videos. In this paper, we
propose a temporal attention approach for aggregating frame-level features into
a video-level feature vector for re-identification. Our method is motivated by
the fact that not all frames in a video are equally informative. We propose a
fully convolutional temporal attention model for generating the attention
scores. Fully convolutional network (FCN) has been widely used in semantic
segmentation for generating 2D output maps. In this paper, we formulate video
based person re-identification as a sequence labeling problem like semantic
segmentation. We establish a connection between them and modify FCN to generate
attention scores to represent the importance of each frame. Extensive
experiments on three different benchmark datasets (i.e. iLIDS-VID, PRID-2011
and SDU-VID) show that our proposed method outperforms other state-of-the-art
approaches.
On the Adaptivity of Stochastic Gradient-Based Optimization
Authors: Lihua Lei, Michael. I. Jordan
Date: 2019-04-09 00:00:00

Stochastic-gradient-based optimization has been a core enabling methodology in applications to large-scale problems in machine learning and related areas. Despite the progress, the gap between theory and practice remains significant, with theoreticians pursuing mathematical optimality at a cost of obtaining specialized procedures in different regimes (e.g., modulus of strong convexity, magnitude of target accuracy, signal-to-noise ratio), and with practitioners not readily able to know which regime is appropriate to their problem, and seeking broadly applicable algorithms that are reasonably close to optimality. To bridge these perspectives it is necessary to study algorithms that are adaptive to different regimes. We present the stochastically controlled stochastic gradient (SCSG) method for composite convex finite-sum optimization problems and show that SCSG is adaptive to both strong convexity and target accuracy. The adaptivity is achieved by batch variance reduction with adaptive batch sizes and a novel technique, which we referred to as geometrization, which sets the length of each epoch as a geometric random variable. The algorithm achieves strictly better theoretical complexity than other existing adaptive algorithms, while the tuning parameters of the algorithm only depend on the smoothness parameter of the objective.
Who Needs Words? Lexicon-Free Speech Recognition
Authors: Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert
Date: 2019-04-09 00:00:00
Tasks: speech-recognition, Speech Recognition

Lexicon-free speech recognition naturally deals with the problem of out-of-vocabulary (OOV) words. In this paper, we show that character-based language models (LM) can perform as well as word-based LMs for speech recognition, in word error rates (WER), even without restricting the decoding to a lexicon. We study character-based LMs and show that convolutional LMs can effectively leverage large (character) contexts, which is key for good speech recognition performance downstream. We specifically show that the lexicon-free decoding performance (WER) on utterances with OOV words using character-based LMs is better than lexicon-based decoding, both with character or word-based LMs.
Kernelized Complete Conditional Stein Discrepancy
Authors: Raghav Singhal, Xintian Han, Saad Lahlou, Rajesh Ranganath
Date: 2019-04-09 00:00:00

Much of machine learning relies on comparing distributions with discrepancy measures. Stein's method creates discrepancy measures between two distributions that require only the unnormalized density of one and samples from the other. Stein discrepancies can be combined with kernels to define kernelized Stein discrepancies (KSDs). While kernels make Stein discrepancies tractable, they pose several challenges in high dimensions. We introduce kernelized complete conditional Stein discrepancies (KCC-SDs). Complete conditionals turn a multivariate distribution into multiple univariate distributions. We show that KCC-SDs distinguish distributions. To show the efficacy of KCC-SDs in distinguishing distributions, we introduce a goodness-of-fit test using KCC-SDs. We empirically show that KCC-SDs have higher power over baselines and use KCC-SDs to assess sample quality in Markov chain Monte Carlo.
Private Hierarchical Clustering and Efficient Approximation
Authors: Xianrui Meng, Dimitrios Papadopoulos, Alina Oprea, Nikos Triandopoulos
Date: 2019-04-09 00:00:00
Tasks: Clustering, Privacy Preserving

In collaborative learning, multiple parties contribute their datasets to jointly deduce global machine learning models for numerous predictive tasks. Despite its efficacy, this learning paradigm fails to encompass critical application domains that involve highly sensitive data, such as healthcare and security analytics, where privacy risks limit entities to individually train models using only their own datasets. In this work, we target privacy-preserving collaborative hierarchical clustering. We introduce a formal security definition that aims to achieve the balance between utility and privacy and present a two-party protocol that provably satisfies it. We then extend our protocol with: (i) an optimized version for the single-linkage clustering, and (ii) scalable approximation variants. We implement all our schemes and experimentally evaluate their performance and accuracy on synthetic and real datasets, obtaining very encouraging results. For example, end-to-end execution of our secure approximate protocol for over 1M 10-dimensional data samples requires 35sec of computation and achieves 97.09% accuracy.
UG$^{2+}$ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
Authors: Ye Yuan, Wenhan Yang, Wenqi Ren, Jiaying Liu, Walter J. Scheirer, Zhangyang Wang
Date: 2019-04-09 00:00:00
Tasks: Face Detection

The UG$^{2+}$ challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. In its second track, we focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. While existing enhancement methods are empirically expected to help the high-level end task, that is observed to not always be the case in practice. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and low-light conditions, respectively, with annotate objects/faces annotated. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. We expect a large participation from the broad research community to address these challenges together.
MVF-Net: Multi-View 3D Face Morphable Model Regression
Authors: Fanzi Wu, Linchao Bao, Yajing Chen, Yonggen Ling, Yibing Song, Songnan Li, King Ngi Ngan, Wei Liu
Date: 2019-04-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry, Optical Flow Estimation, regression

We address the problem of recovering the 3D geometry of a human face from a
set of facial images in multiple views. While recent studies have shown
impressive progress in 3D Morphable Model (3DMM) based facial reconstruction,
the settings are mostly restricted to a single view. There is an inherent
drawback in the single-view setting: the lack of reliable 3D constraints can
cause unresolvable ambiguities. We in this paper explore 3DMM-based shape
recovery in a different setting, where a set of multi-view facial images are
given as input. A novel approach is proposed to regress 3DMM parameters from
multi-view inputs with an end-to-end trainable Convolutional Neural Network
(CNN). Multiview geometric constraints are incorporated into the network by
establishing dense correspondences between different views leveraging a novel
self-supervised view alignment loss. The main ingredient of the view alignment
loss is a differentiable dense optical flow estimator that can backpropagate
the alignment errors between an input view and a synthetic rendering from
another input view, which is projected to the target view through the 3D shape
to be inferred. Through minimizing the view alignment loss, better 3D shapes
can be recovered such that the synthetic projections from one view to another
can better align with the observed image. Extensive experiments demonstrate the
superiority of the proposed method over other 3DMM methods.
Intra-Ensemble in Neural Networks
Authors: Yuan Gao, Zixiang Cai, Lei Yu
Date: 2019-04-09 00:00:00
Tasks: Diversity

Improving model performance is always the key problem in machine learning including deep learning. However, stand-alone neural networks always suffer from marginal effect when stacking more layers. At the same time, ensemble is an useful technique to further enhance model performance. Nevertheless, training several independent deep neural networks for ensemble costs multiple resources. If so, is it possible to utilize ensemble in only one neural network? In this work, we propose Intra-Ensemble, an end-to-end ensemble strategy with stochastic channel recombination operations to train several sub-networks simultaneously within one neural network. Additional parameter size is marginal since the majority of parameters are mutually shared. Meanwhile, stochastic channel recombination significantly increases the diversity of sub-networks, which finally enhances ensemble performance. Extensive experiments and ablation studies prove the applicability of intra-ensemble on various kinds of datasets and network architectures.
Mixing syntagmatic and paradigmatic information for concept detection
Authors: Louis Chartrand, Mohamed Bouguessa
Date: 2019-04-09 00:00:00
Tasks: Topic Models, Word Embeddings

In the last decades, philosophers have begun using empirical data for conceptual analysis, but corpus-based conceptual analysis has so far failed to develop, in part because of the absence of reliable methods to automatically detect concepts in textual data. Previous attempts have shown that topic models can constitute efficient concept detection heuristics, but while they leverage the syntagmatic relations in a corpus, they fail to exploit paradigmatic relations, and thus probably fail to model concepts accurately. In this article, we show that using a topic model that models concepts on a space of word embeddings (Hu and Tsujii, 2016) can lead to significant increases in concept detection performance, as well as enable the target concept to be expressed in more flexible ways using word vectors.
Attention-based Multi-instance Neural Network for Medical Diagnosis from Incomplete and Low Quality Data
Authors: Zeyuan Wang, Josiah Poon, Shiding Sun, Simon Poon
Date: 2019-04-09 00:00:00
Tasks: General Classification, Medical Diagnosis

One way to extract patterns from clinical records is to consider each patient
record as a bag with various number of instances in the form of symptoms.
Medical diagnosis is to discover informative ones first and then map them to
one or more diseases. In many cases, patients are represented as vectors in
some feature space and a classifier is applied after to generate diagnosis
results. However, in many real-world cases, data is often of low-quality due to
a variety of reasons, such as data consistency, integrity, completeness,
accuracy, etc. In this paper, we propose a novel approach, attention based
multi-instance neural network (AMI-Net), to make the single disease
classification only based on the existing and valid information in the
real-world outpatient records. In the context of a patient, it takes a bag of
instances as input and output the bag label directly in end-to-end way.
Embedding layer is adopted at the beginning, mapping instances into an
embedding space which represents the individual patient condition. The
correlations among instances and their importance for the final classification
are captured by multi-head attention transformer, instance-level multi-instance
pooling and bag-level multi-instance pooling. The proposed approach was test on
two non-standardized and highly imbalanced datasets, one in the Traditional
Chinese Medicine (TCM) domain and the other in the Western Medicine (WM)
domain. Our preliminary results show that the proposed approach outperforms all
baselines results by a significant margin.