pwc-archive/papers-with-abstracts huggingface dataset:
Attention Based Glaucoma Detection: A Large-scale Database and CNN Model
Authors: Liu Li, Mai Xu, Xiaofei Wang, Lai Jiang, Hanruo Liu
Date: 2019-03-26 00:00:00
Proceeding: CVPR 2019 6

Recently, the attention mechanism has been successfully applied in
convolutional neural networks (CNNs), significantly boosting the performance of
many computer vision tasks. Unfortunately, few medical image recognition
approaches incorporate the attention mechanism in the CNNs. In particular,
there exists high redundancy in fundus images for glaucoma detection, such that
the attention mechanism has potential in improving the performance of CNN-based
glaucoma detection. This paper proposes an attention-based CNN for glaucoma
detection (AG-CNN). Specifically, we first establish a large-scale attention
based glaucoma (LAG) database, which includes 5,824 fundus images labeled with
either positive glaucoma (2,392) or negative glaucoma (3,432). The attention
maps of the ophthalmologists are also collected in LAG database through a
simulated eye-tracking experiment. Then, a new structure of AG-CNN is designed,
including an attention prediction subnet, a pathological area localization
subnet and a glaucoma classification subnet. Different from other
attention-based CNN methods, the features are also visualized as the localized
pathological area, which can advance the performance of glaucoma detection.
Finally, the experiment results show that the proposed AG-CNN approach
significantly advances state-of-the-art glaucoma detection.
Large-scale interactive object segmentation with human annotators
Authors: Rodrigo Benenson, Stefan Popov, Vittorio Ferrari
Date: 2019-03-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Instance Segmentation, Interactive Segmentation, Object, Segmentation, Semantic Segmentation

Manually annotating object segmentation masks is very time consuming.
Interactive object segmentation methods offer a more efficient alternative
where a human annotator and a machine segmentation model collaborate. In this
paper we make several contributions to interactive segmentation: (1) we
systematically explore in simulation the design space of deep interactive
segmentation models and report new insights and caveats; (2) we execute a
large-scale annotation campaign with real human annotators, producing masks for
2.5M instances on the OpenImages dataset. We plan to release this data
publicly, forming the largest existing dataset for instance segmentation.
Moreover, by re-annotating part of the COCO dataset, we show that we can
produce instance masks 3 times faster than traditional polygon drawing tools
while also providing better quality. (3) We present a technique for
automatically estimating the quality of the produced masks which exploits
indirect signals from the annotation process.
SRM : A Style-based Recalibration Module for Convolutional Neural Networks
Authors: HyunJae Lee, Hyo-Eun Kim, Hyeonseob Nam
Date: 2019-03-26 00:00:00
Tasks: Image Classification, Style Transfer

Following the advance of style transfer with Convolutional Neural Networks
(CNNs), the role of styles in CNNs has drawn growing attention from a broader
perspective. In this paper, we aim to fully leverage the potential of styles to
improve the performance of CNNs in general vision tasks. We propose a
Style-based Recalibration Module (SRM), a simple yet effective architectural
unit, which adaptively recalibrates intermediate feature maps by exploiting
their styles. SRM first extracts the style information from each channel of the
feature maps by style pooling, then estimates per-channel recalibration weight
via channel-independent style integration. By incorporating the relative
importance of individual styles into feature maps, SRM effectively enhances the
representational ability of a CNN. The proposed module is directly fed into
existing CNN architectures with negligible overhead. We conduct comprehensive
experiments on general image recognition as well as tasks related to styles,
which verify the benefit of SRM over recent approaches such as
Squeeze-and-Excitation (SE). To explain the inherent difference between SRM and
SE, we provide an in-depth comparison of their representational properties.
A geometry-inspired decision-based attack
Authors: Yujia Liu, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Date: 2019-03-26 00:00:00
Proceeding: ICCV 2019 10
Tasks: General Classification, image-classification, Image Classification

Deep neural networks have recently achieved tremendous success in image
classification. Recent studies have however shown that they are easily misled
into incorrect classification decisions by adversarial examples. Adversaries
can even craft attacks by querying the model in black-box settings, where no
information about the model is released except its final decision. Such
decision-based attacks usually require lots of queries, while real-world image
recognition systems might actually restrict the number of queries. In this
paper, we propose qFool, a novel decision-based attack algorithm that can
generate adversarial examples using a small number of queries. The qFool method
can drastically reduce the number of queries compared to previous
decision-based attacks while reaching the same quality of adversarial examples.
We also enhance our method by constraining adversarial perturbations in
low-frequency subspace, which can make qFool even more computationally
efficient. Altogether, we manage to fool commercial image recognition systems
with a small number of queries, which demonstrates the actual effectiveness of
our new algorithm in practice.
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems
Authors: Cheng Wang, Mathias Niepert, Hui Li
Date: 2019-03-26 00:00:00
Tasks: Domain Adaptation, Recommendation Systems, Representation Learning, Transfer Learning

Data sparsity and data imbalance are practical and challenging issues in
cross-domain recommender systems. This paper addresses those problems by
leveraging the concepts which derive from representation learning, adversarial
learning and transfer learning (particularly, domain adaptation). Although
various transfer learning methods have shown promising performance in this
context, our proposed novel method RecSys-DAN focuses on alleviating the
cross-domain and within-domain data sparsity and data imbalance and learns
transferable latent representations for users, items and their interactions.
Different from existing approaches, the proposed method transfers the latent
representations from a source domain to a target domain in an adversarial way.
The mapping functions in the target domain are learned by playing a min-max
game with an adversarial loss, aiming to generate domain indistinguishable
representations for a discriminator. Four neural architectural instances of
ResSys-DAN are proposed and explored. Empirical results on real-world Amazon
data show that, even without using labeled data (i.e., ratings) in the target
domain, RecSys-DAN achieves competitive performance as compared to the
state-of-the-art supervised methods. More importantly, RecSys-DAN is highly
flexible to both unimodal and multimodal scenarios, and thus it is more robust
to the cold-start recommendation which is difficult for previous methods.
Micro-expression detection in long videos using optical flow and recurrent neural networks
Authors: Michiel Verburg, Vlado Menkovski
Date: 2019-03-26 00:00:00
Tasks: Micro-Expression Spotting, Optical Flow Estimation, Sentiment Analysis

Facial micro-expressions are subtle and involuntary expressions that can
reveal concealed emotions. Micro-expressions are an invaluable source of
information in application domains such as lie detection, mental health,
sentiment analysis and more. One of the biggest challenges in this field of
research is the small amount of available spontaneous micro-expression data.
However, spontaneous data collection is burdened by time-consuming and
expensive annotation. Hence, methods are needed which can reduce the amount of
data that annotators have to review. This paper presents a novel
micro-expression spotting method using a recurrent neural network (RNN) on
optical flow features. We extract Histogram of Oriented Optical Flow (HOOF)
features to encode the temporal changes in selected face regions. Finally, the
RNN spots short intervals which are likely to contain occurrences of relevant
facial micro-movements. The proposed method is evaluated on the SAMM database.
Any chance of subject bias is eliminated by training the RNN using
Leave-One-Subject-Out cross-validation. Comparing the spotted intervals with
the labeled data shows that the method produced 1569 false positives while
obtaining a recall of 0.4654. The initial results show that the proposed method
would reduce the video length by a factor of 3.5, while still retaining almost
half of the relevant micro-movements. Lastly, as the model gets more data, it
becomes better at detecting intervals, which makes the proposed method suitable
for supporting the annotation process.
Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach
Authors: Amir Atapour-Abarghouei, Toby P. Breckon
Date: 2019-03-26 00:00:00
Tasks: Autonomous Driving, Depth Completion, Depth Estimation, Depth Prediction, Monocular Depth Estimation, Multi-Task Learning, Scene Segmentation, Scene Understanding

Robust geometric and semantic scene understanding is ever more important in many real-world applications such as autonomous driving and robotic navigation. In this paper, we propose a multi-task learning-based approach capable of jointly performing geometric and semantic scene understanding, namely depth prediction (monocular depth estimation and depth completion) and semantic scene segmentation. Within a single temporally constrained recurrent network, our approach uniquely takes advantage of a complex series of skip connections, adversarial training and the temporal constraint of sequential frame recurrence to produce consistent depth and semantic class labels simultaneously. Extensive experimental evaluation demonstrates the efficacy of our approach compared to other contemporary state-of-the-art techniques.
Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring
Authors: Talha Qaiser, Nasir M. Rajpoot
Date: 2019-03-26 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Estimating over-amplification of human epidermal growth factor receptor 2
(HER2) on invasive breast cancer (BC) is regarded as a significant predictive
and prognostic marker. We propose a novel deep reinforcement learning (DRL)
based model that treats immunohistochemical (IHC) scoring of HER2 as a
sequential learning task. For a given image tile sampled from multi-resolution
giga-pixel whole slide image (WSI), the model learns to sequentially identify
some of the diagnostically relevant regions of interest (ROIs) by following a
parameterized policy. The selected ROIs are processed by recurrent and residual
convolution networks to learn the discriminative features for different HER2
scores and predict the next location, without requiring to process all the
sub-image patches of a given tile for predicting the HER2 score, mimicking the
histopathologist who would not usually analyze every part of the slide at the
highest magnification. The proposed model incorporates a task-specific
regularization term and inhibition of return mechanism to prevent the model
from revisiting the previously attended locations. We evaluated our model on
two IHC datasets: a publicly available dataset from the HER2 scoring challenge
contest and another dataset consisting of WSIs of gastroenteropancreatic
neuroendocrine tumor sections stained with Glo1 marker. We demonstrate that the
proposed model outperforms other methods based on state-of-the-art deep
convolutional networks. To the best of our knowledge, this is the first study
using DRL for IHC scoring and could potentially lead to wider use of DRL in the
domain of computational pathology reducing the computational burden of the
analysis of large multigigapixel histology images.
FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds
Authors: Jie Zhou, Xin Tan, Zhiwei Shao, Lizhuang Ma
Date: 2019-03-26 00:00:00
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Real-Time Object Detection

3D object detection from raw and sparse point clouds has been far less treated to date, compared with its 2D counterpart. In this paper, we propose a novel framework called FVNet for 3D front-view proposal generation and object detection from point clouds. It consists of two stages: generation of front-view proposals and estimation of 3D bounding box parameters. Instead of generating proposals from camera images or bird's-eye-view maps, we first project point clouds onto a cylindrical surface to generate front-view feature maps which retains rich information. We then introduce a proposal generation network to predict 3D region proposals from the generated maps and further extrude objects of interest from the whole point cloud. Finally, we present another network to extract the point-wise features from the extruded object points and regress the final 3D bounding box parameters in the canonical coordinates. Our framework achieves real-time performance with 12ms per point cloud sample. Extensive experiments on the 3D detection benchmark KITTI show that the proposed architecture outperforms state-of-the-art techniques which take either camera images or point clouds as input, in terms of accuracy and inference time.
Trait of Gait: A Survey on Gait Biometrics
Authors: Ebenezer R. H. P. Isaac, Susan Elias, Srinivasan Rajagopalan, K. S. Easwarakumar
Date: 2019-03-26 00:00:00
Tasks: Gait Recognition, Survey

Gait analysis is the study of the systematic methods that assess and quantify
animal locomotion. The research on gait analysis has considerably evolved
through time. It was an ancient art, and it still finds its application today
in modern science and medicine. This paper describes how one's gait can be used
as a biometric. It shall diversely cover salient research done within the field
and explain the nuances and advances in each type of gait analysis. The
prominent methods of gait recognition from the early era to the state of the
art are covered. This survey also reviews the various gait datasets. The
overall aim of this study is to provide a concise roadmap for anyone who wishes
to do research in the field of gait biometrics.
Generative Tensor Network Classification Model for Supervised Machine Learning
Authors: Zheng-Zhi Sun, Cheng Peng, Ding Liu, Shi-Ju Ran, Gang Su
Date: 2019-03-26 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Clustering, General Classification

Tensor network (TN) has recently triggered extensive interests in developing
machine-learning models in quantum many-body Hilbert space. Here we purpose a
generative TN classification (GTNC) approach for supervised learning. The
strategy is to train the generative TN for each class of the samples to
construct the classifiers. The classification is implemented by comparing the
distance in the many-body Hilbert space. The numerical experiments by GTNC show
impressive performance on the MNIST and Fashion-MNIST dataset. The testing
accuracy is competitive to the state-of-the-art convolutional neural network
while higher than the naive Bayes classifier (a generative classifier) and
support vector machine. Moreover, GTNC is more efficient than the existing TN
models that are in general discriminative. By investigating the distances in
the many-body Hilbert space, we find that (a) the samples are naturally
clustering in such a space; and (b) bounding the bond dimensions of the TN's to
finite values corresponds to removing redundant information in the image
recognition. These two characters make GTNC an adaptive and universal model of
excellent performance.
Discriminative Supervised Hashing for Cross-Modal similarity Search
Authors: Jun Yu, Xiao-Jun Wu, Josef Kittler
Date: 2018-12-06 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval

With the advantage of low storage cost and high retrieval efficiency, hashing
techniques have recently been an emerging topic in cross-modal similarity
search. As multiple modal data reflect similar semantic content, many
researches aim at learning unified binary codes. However, discriminative
hashing features learned by these methods are not adequate. This results in
lower accuracy and robustness. We propose a novel hashing learning framework
which jointly performs classifier learning, subspace learning and matrix
factorization to preserve class-specific semantic content, termed
Discriminative Supervised Hashing (DSH), to learn the discrimative unified
binary codes for multi-modal data. Besides, reducing the loss of information
and preserving the non-linear structure of data, DSH non-linearly projects
different modalities into the common space in which the similarity among
heterogeneous data points can be measured. Extensive experiments conducted on
the three publicly available datasets demonstrate that the framework proposed
in this paper outperforms several state-of -the-art methods.
Interoperability and machine-to-machine translation model with mappings to machine learning tasks
Authors: Jacob Nilsson, Fredrik Sandin, Jerker Delsing
Date: 2019-03-26 00:00:00
Tasks: BIG-bench Machine Learning, Machine Translation, Translation

Modern large-scale automation systems integrate thousands to hundreds of
thousands of physical sensors and actuators. Demands for more flexible
reconfiguration of production systems and optimization across different
information models, standards and legacy systems challenge current system
interoperability concepts. Automatic semantic translation across information
models and standards is an increasingly important problem that needs to be
addressed to fulfill these demands in a cost-efficient manner under constraints
of human capacity and resources in relation to timing requirements and system
complexity. Here we define a translator-based operational interoperability
model for interacting cyber-physical systems in mathematical terms, which
includes system identification and ontology-based translation as special cases.
We present alternative mathematical definitions of the translator learning task
and mappings to similar machine learning tasks and solutions based on recent
developments in machine learning. Possibilities to learn translators between
artefacts without a common physical context, for example in simulations of
digital twins and across layers of the automation pyramid are briefly
discussed.
A Silver Standard Corpus of Human Phenotype-Gene Relations
Authors: Diana Sousa, Andre Lamurias, Francisco M. Couto
Date: 2019-03-26 00:00:00
Proceeding: NAACL 2019 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation Extraction

Human phenotype-gene relations are fundamental to fully understand the origin of some phenotypic abnormalities and their associated diseases. Biomedical literature is the most comprehensive source of these relations, however, we need Relation Extraction tools to automatically recognize them. Most of these tools require an annotated corpus and to the best of our knowledge, there is no corpus available annotated with human phenotype-gene relations. This paper presents the Phenotype-Gene Relations (PGR) corpus, a silver standard corpus of human phenotype and gene annotations and their relations. The corpus consists of 1712 abstracts, 5676 human phenotype annotations, 13835 gene annotations, and 4283 relations. We generated this corpus using Named-Entity Recognition tools, whose results were partially evaluated by eight curators, obtaining a precision of 87.01%. By using the corpus we were able to obtain promising results with two state-of-the-art deep learning tools, namely 78.05% of precision. The PGR corpus was made publicly available to the research community.
Improving image classifiers for small datasets by learning rate adaptations
Authors: Sourav Mishra, Toshihiko Yamasaki, Hideaki Imaizumi
Date: 2019-03-26 00:00:00
Tasks: Diagnostic

Our paper introduces an efficient combination of established techniques to
improve classifier performance, in terms of accuracy and training time. We
achieve two-fold to ten-fold speedup in nearing state of the art accuracy, over
different model architectures, by dynamically tuning the learning rate. We find
it especially beneficial in the case of a small dataset, where reliability of
machine reasoning is lower. We validate our approach by comparing our method
versus vanilla training on CIFAR-10. We also demonstrate its practical
viability by implementing on an unbalanced corpus of diagnostic images.
Domain Representation for Knowledge Graph Embedding
Authors: Cunxiang Wang, Feiliang Ren, Zhichao Lin, Chenxv Zhao, Tian Xie, Yue Zhang
Date: 2019-03-26 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Link Prediction, Representation Learning

Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However, most existing models ignore to represent hierarchical knowledge, such as the similarities and dissimilarities of entities in one domain. We proposed to learn a Domain Representations over existing knowledge graph embedding models, such that entities that have similar attributes are organized into the same domain. Such hierarchical knowledge of domains can give further evidence in link prediction. Experimental results show that domain embeddings give a significant improvement over the most recent state-of-art baseline knowledge graph embedding models.
Multiscale CNN based Deep Metric Learning for Bioacoustic Classification: Overcoming Training Data Scarcity Using Dynamic Triplet Loss
Authors: Anshul Thakur, Daksh Thapar, Padmanabhan Rajan, Aditya Nigam
Date: 2019-03-26 00:00:00
Tasks: Classification, General Classification, Metric Learning, Triplet

This paper proposes multiscale convolutional neural network (CNN)-based deep
metric learning for bioacoustic classification, under low training data
conditions. The proposed CNN is characterized by the utilization of four
different filter sizes at each level to analyze input feature maps. This
multiscale nature helps in describing different bioacoustic events effectively:
smaller filters help in learning the finer details of bioacoustic events,
whereas, larger filters help in analyzing a larger context leading to global
details. A dynamic triplet loss is employed in the proposed CNN architecture to
learn a transformation from the input space to the embedding space, where
classification is performed. The triplet loss helps in learning this
transformation by analyzing three examples, referred to as triplets, at a time
where intra-class distance is minimized while maximizing the inter-class
separation by a dynamically increasing margin. The number of possible triplets
increases cubically with the dataset size, making triplet loss more suitable
than the softmax cross-entropy loss in low training data conditions.
Experiments on three different publicly available datasets show that the
proposed framework performs better than existing bioacoustic classification
frameworks. Experimental results also confirm the superiority of the triplet
loss over the cross-entropy loss in low training data conditions