pwc-archive/papers-with-abstracts huggingface dataset:
Training Competitive Binary Neural Networks from Scratch
Authors: Joseph Bethge, Marvin Bornstein, Adrian Loy, Haojin Yang, Christoph Meinel
Date: 2018-12-05 00:00:00

Convolutional neural networks have achieved astonishing results in different
application areas. Various methods that allow us to use these models on mobile
and embedded devices have been proposed. Especially binary neural networks are
a promising approach for devices with low computational power. However,
training accurate binary models from scratch remains a challenge. Previous work
often uses prior knowledge from full-precision models and complex training
strategies. In our work, we focus on increasing the performance of binary
neural networks without such prior knowledge and a much simpler training
strategy. In our experiments we show that we are able to achieve
state-of-the-art results on standard benchmark datasets. Further, to the best
of our knowledge, we are the first to successfully adopt a network architecture
with dense connections for binary networks, which lets us improve the
state-of-the-art even further.
Automatic Generation of Dense Non-rigid Optical Flow
Authors: Hoàng-Ân Lê, Tushar Nimbhorkar, Thomas Mensink, Anil S. Baslamisli, Sezer Karaoglu, Theo Gevers
Date: 2018-12-05 00:00:00
Tasks: Optical Flow Estimation

There hardly exists any large-scale datasets with dense optical flow of non-rigid motion from real-world imagery as of today. The reason lies mainly in the required setup to derive ground truth optical flows: a series of images with known camera poses along its trajectory, and an accurate 3D model from a textured scene. Human annotation is not only too tedious for large databases, it can simply hardly contribute to accurate optical flow. To circumvent the need for manual annotation, we propose a framework to automatically generate optical flow from real-world videos. The method extracts and matches objects from video frames to compute initial constraints, and applies a deformation over the objects of interest to obtain dense optical flow fields. We propose several ways to augment the optical flow variations. Extensive experimental results show that training on our automatically generated optical flow outperforms methods that are trained on rigid synthetic data using FlowNet-S, LiteFlowNet, PWC-Net, and RAFT. Datasets and implementation of our optical flow generation framework are released at https://github.com/lhoangan/arap_flow
Robust Ordinal Embedding from Contaminated Relative Comparisons
Authors: Ke Ma, Qianqian Xu, Xiaochun Cao
Date: 2018-12-05 00:00:00
Tasks: Outlier Detection

Existing ordinal embedding methods usually follow a two-stage routine:
outlier detection is first employed to pick out the inconsistent comparisons;
then an embedding is learned from the clean data. However, learning in a
multi-stage manner is well-known to suffer from sub-optimal solutions. In this
paper, we propose a unified framework to jointly identify the contaminated
comparisons and derive reliable embeddings. The merits of our method are
three-fold: (1) By virtue of the proposed unified framework, the sub-optimality
of traditional methods is largely alleviated; (2) The proposed method is aware
of global inconsistency by minimizing a corresponding cost, while traditional
methods only involve local inconsistency; (3) Instead of considering the
nuclear norm heuristics, we adopt an exact solution for rank equality
constraint. Our studies are supported by experiments with both simulated
examples and real-world data. The proposed framework provides us a promising
tool for robust ordinal embedding from the contaminated comparisons.
Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin
Authors: Ke Ma, Qianqian Xu, Zhiyong Yang, Xiaochun Cao
Date: 2018-12-05 00:00:00

In the absence of prior knowledge, ordinal embedding methods obtain new
representation for items in a low-dimensional Euclidean space via a set of
quadruple-wise comparisons. These ordinal comparisons often come from human
annotators, and sufficient comparisons induce the success of classical
approaches. However, collecting a large number of labeled data is known as a
hard task, and most of the existing work pay little attention to the
generalization ability with insufficient samples. Meanwhile, recent progress in
large margin theory discloses that rather than just maximizing the minimum
margin, both the margin mean and variance, which characterize the margin
distribution, are more crucial to the overall generalization performance. To
address the issue of insufficient training samples, we propose a margin
distribution learning paradigm for ordinal embedding, entitled Distributional
Margin based Ordinal Embedding (\textit{DMOE}). Precisely, we first define the
margin for ordinal embedding problem. Secondly, we formulate a concise
objective function which avoids maximizing margin mean and minimizing margin
variance directly but exhibits the similar effect. Moreover, an Augmented
Lagrange Multiplier based algorithm is customized to seek the optimal solution
of \textit{DMOE} effectively. Experimental studies on both simulated and
real-world datasets are provided to show the effectiveness of the proposed
algorithm.
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
Authors: Jia Guo, Jiankang Deng, Niannan Xue, Stefanos Zafeiriou
Date: 2018-12-05 00:00:00
Tasks: Face Alignment, Face Recognition, Robust Face Alignment, Robust Face Recognition

Face Analysis Project on MXNet
An Empirical Study towards Understanding How Deep Convolutional Nets Recognize Falls
Authors: Yan Zhang, Heiko Neumann
Date: 2018-12-05 00:00:00
Tasks: Action Analysis

Detecting unintended falls is essential for ambient intelligence and
healthcare of elderly people living alone. In recent years, deep convolutional
nets are widely used in human action analysis, based on which a number of fall
detection methods have been proposed. Despite their highly effective
performances, the behaviors of how the convolutional nets recognize falls are
still not clear. In this paper, instead of proposing a novel approach, we
perform a systematical empirical study, attempting to investigate the
underlying fall recognition process. We propose four tasks to investigate,
which involve five types of input modalities, seven net instances and different
training samples. The obtained quantitative and qualitative results reveal the
patterns that the nets tend to learn, and several factors that can heavily
influence the performances on fall recognition. We expect that our conclusions
are favorable to proposing better deep learning solutions to fall detection
systems.
Local Temporal Bilinear Pooling for Fine-grained Action Parsing
Authors: Yan Zhang, Siyu Tang, Krikamol Muandet, Christian Jarvers, Heiko Neumann
Date: 2018-12-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Parsing, Decoder

Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to other work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced with neither information loss nor extra computation. We perform intensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art work on various datasets.
Computational Graph Approach for Detection of Composite Human Activities
Authors: Niko Reunanen, Ville Könönen, Hermanni Hälvä, Jani Mäntyjärvi, Arttu Lämsä, Jussi Liikka
Date: 2018-12-05 00:00:00
Tasks: Action Detection, Activity Detection

Existing work in human activity detection classifies physical activities
using a single fixed-length subset of a sensor signal. However, temporally
consecutive subsets of a sensor signal are not utilized. This is not optimal
for classifying physical activities (composite activities) that are composed of
a temporal series of simpler activities (atomic activities). A sport consists
of physical activities combined in a fashion unique to that sport. The
constituent physical activities and the sport are not fundamentally different.
We propose a computational graph architecture for human activity detection
based on the readings of a triaxial accelerometer. The resulting model learns
1) a representation of the atomic activities of a sport and 2) to classify
physical activities as compositions of the atomic activities. The proposed
model, alongside with a set of baseline models, was tested for a simultaneous
classification of eight physical activities (walking, nordic walking, running,
soccer, rowing, bicycling, exercise bicycling and lying down). The proposed
model obtained an overall mean accuracy of 77.91% (population) and 95.28%
(personalized). The corresponding accuracies of the best baseline model were
73.52% and 90.03%. However, without combining consecutive atomic activities,
the corresponding accuracies of the proposed model were 71.52% and 91.22%. The
results show that our proposed model is accurate, outperforms the baseline
models and learns to combine simple activities into complex activities.
Composite activities can be classified as combinations of atomic activities.
Our proposed architecture is a basis for accurate models in human activity
detection.
Learning to generate filters for convolutional neural networks
Authors: Wei Shen, Rujie Liu
Date: 2018-12-05 00:00:00
Proceeding: ICLR 2018 1

Conventionally, convolutional neural networks (CNNs) process different images
with the same set of filters. However, the variations in images pose a
challenge to this fashion. In this paper, we propose to generate
sample-specific filters for convolutional layers in the forward pass. Since the
filters are generated on-the-fly, the model becomes more flexible and can
better fit the training data compared to traditional CNNs. In order to obtain
sample-specific features, we extract the intermediate feature maps from an
autoencoder. As filters are usually high dimensional, we propose to learn a set
of coefficients instead of a set of filters. These coefficients are used to
linearly combine the base filters from a filter repository to generate the
final filters for a CNN. The proposed method is evaluated on MNIST, MTFL and
CIFAR10 datasets. Experiment results demonstrate that the classification
accuracy of the baseline model can be improved by using the proposed filter
generation method.
An Evolutionary Hierarchical Interval Type-2 Fuzzy Knowledge Representation System (EHIT2FKRS) for Travel Route Assignment
Authors: Mariam Zouari, Nesrine Baklouti, Javier Sanchez Medina, Mounir Ben Ayed, Adel M. ALIMI
Date: 2018-12-05 00:00:00
Tasks: Management

Urban Traffic Networks are characterized by high dynamics of traffic flow and
increased travel time, including waiting times. This leads to more complex road
traffic management. The present research paper suggests an innovative advanced
traffic management system based on Hierarchical Interval Type-2 Fuzzy Logic
model optimized by the Particle Swarm Optimization (PSO) method. The aim of
designing this system is to perform dynamic route assignment to relieve traffic
congestion and limit the unexpected fluctuation effects on traffic flow. The
suggested system is executed and simulated using SUMO, a well-known microscopic
traffic simulator. For the present study, we have tested four large and
heterogeneous metropolitan areas located in the cities of Sfax, Luxembourg,
Bologna and Cologne. The experimental results proved the effectiveness of
learning the Hierarchical Interval type-2 Fuzzy logic using real time particle
swarm optimization technique PSO to accomplish multiobjective optimality
regarding two criteria: number of vehicles that reach their destination and
average travel time. The obtained results are encouraging, confirming the
efficiency of the proposed system.
Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning
Authors: Ying Shen, Joël Colloc, Armelle Jacquet-Andrieu, Ziyi Guo, Yong liu
Date: 2018-12-05 00:00:00
Tasks: General Classification

Decision support is a probabilistic and quantitative method designed for
modeling problems in situations with ambiguity. Computer technology can be
employed to provide clinical decision support and treatment recommendations.
The problem of natural language applications is that they lack formality and
the interpretation is not consistent. Conversely, ontologies can capture the
intended meaning and specify modeling primitives. Disease Ontology (DO) that
pertains to cancer's clinical stages and their corresponding information
components is utilized to improve the reasoning ability of a decision support
system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider
disease manifestations and provides physicians with treatment solutions from
similar previous cases for reference. The proposed DSS supports natural
language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease
classification with the help of the ontology.
A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions
Authors: Kai Lei, Bing Zhang, Yong liu, Yang Deng, Dongyu Zhang, Ying Shen
Date: 2018-12-05 00:00:00
Tasks: Entity Linking, Learning-To-Rank, Question Answering, Retrieval, Semantic Similarity, Semantic Textual Similarity, text similarity

Named entity discovery and linking is the fundamental and core component of
question answering. In Question Entity Discovery and Linking (QEDL) problem,
traditional methods are challenged because multiple entities in one short
question are difficult to be discovered entirely and the incomplete information
in short text makes entity linking hard to implement. To overcome these
difficulties, we proposed a knowledge graph based solution for QEDL and
developed a system consists of Question Entity Discovery (QED) module and
Entity Linking (EL) module. The method of QED module is a tradeoff and ensemble
of two methods. One is the method based on knowledge graph retrieval, which
could extract more entities in questions and guarantee the recall rate, the
other is the method based on Conditional Random Field (CRF), which improves the
precision rate. The EL module is treated as a ranking problem and Learning to
Rank (LTR) method with features such as semantic similarity, text similarity
and entity popularity is utilized to extract and make full use of the
information in short texts. On the official dataset of a shared QEDL evaluation
task, our approach could obtain 64.44% F1 score of QED and 64.86% accuracy of
EL, which ranks the 2nd place and indicates its practical use for QEDL problem.
Interactive Full Image Segmentation by Considering All Regions Jointly
Authors: Eirikur Agustsson, Jasper R. R. Uijlings, Vittorio Ferrari
Date: 2018-12-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: All, Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

We address interactive full image annotation, where the goal is to accurately
segment all object and stuff regions in an image. We propose an interactive,
scribble-based annotation framework which operates on the whole image to
produce segmentations for all regions. This enables sharing scribble
corrections across regions, and allows the annotator to focus on the largest
errors made by the machine across the whole image. To realize this, we adapt
Mask-RCNN into a fast interactive segmentation framework and introduce an
instance-aware loss measured at the pixel-level in the full image canvas, which
lets predictions for nearby regions properly compete for space. Finally, we
compare to interactive single object segmentation on the COCO panoptic dataset.
We demonstrate that our interactive full image segmentation approach leads to a
5% IoU gain, reaching 90% IoU at a budget of four extreme clicks and four
corrective scribbles per region.
Approach for Semi-automatic Construction of Anti-infective Drug Ontology Based on Entity Linking
Authors: Ying Shen, Yang Deng, Kaiqi Yuan, Li Liu, Yong liu
Date: 2018-12-05 00:00:00
Tasks: Entity Linking

Ontology can be used for the interpretation of natural language. To construct
an anti-infective drug ontology, one needs to design and deploy a
methodological step to carry out the entity discovery and linking. Medical
synonym resources have been an important part of medical natural language
processing (NLP). However, there are problems such as low precision and low
recall rate. In this study, an NLP approach is adopted to generate candidate
entities. Open ontology is analyzed to extract semantic relations. Six-word
vector features and word-level features are selected to perform the entity
linking. The extraction results of synonyms with a single feature and different
combinations of features are studied. Experiments show that our selected
features have achieved a precision rate of 86.77%, a recall rate of 89.03% and
an F1 score of 87.89%. This paper finally presents the structure of the
proposed ontology and its relevant statistical data.
Improving Medical Short Text Classification with Semantic Expansion Using Word-Cluster Embedding
Authors: Ying Shen, Qiang Zhang, Jin Zhang, Jiyue Huang, Yuming Lu, Kai Lei
Date: 2018-12-05 00:00:00
Tasks: Classification, Clustering, General Classification, Sentence, Sentence Classification, text-classification, Text Classification

Automatic text classification (TC) research can be used for real-world
problems such as the classification of in-patient discharge summaries and
medical text reports, which is beneficial to make medical documents more
understandable to doctors. However, in electronic medical records (EMR), the
texts containing sentences are shorter than that in general domain, which leads
to the lack of semantic features and the ambiguity of semantic. To tackle this
challenge, we propose to add word-cluster embedding to deep neural network for
improving short text classification. Concretely, we first use hierarchical
agglomerative clustering to cluster the word vectors in the semantic space.
Then we calculate the cluster center vector which represents the implicit topic
information of words in the cluster. Finally, we expand word vector with
cluster center vector, and implement classifiers using CNN and LSTM
respectively. To evaluate the performance of our proposed method, we conduct
experiments on public data sets TREC and the medical short sentences data sets
which is constructed and released by us. The experimental results demonstrate
that our proposed method outperforms state-of-the-art baselines in short
sentence classification on both medical domain and general domain.
MedSim: A Novel Semantic Similarity Measure in Bio-medical Knowledge Graphs
Authors: Kai Lei, Kaiqi Yuan, Qiang Zhang, Ying Shen
Date: 2018-12-05 00:00:00
Tasks: Knowledge Graphs, Semantic Similarity, Semantic Textual Similarity

We present MedSim, a novel semantic SIMilarity method based on public
well-established bio-MEDical knowledge graphs (KGs) and large-scale corpus, to
study the therapeutic substitution of antibiotics. Besides hierarchy and corpus
of KGs, MedSim further interprets medicine characteristics by constructing
multi-dimensional medicine-specific feature vectors. Dataset of 528 antibiotic
pairs scored by doctors is applied for evaluation and MedSim has produced
statistically significant improvement over other semantic similarity methods.
Furthermore, some promising applications of MedSim in drug substitution and
drug abuse prevention are presented in case study.
Learning to Compose Dynamic Tree Structures for Visual Contexts
Authors: Kaihua Tang, Hanwang Zhang, Baoyuan Wu, Wenhan Luo, Wei Liu
Date: 2018-12-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Generation, Panoptic Scene Graph Generation, Reinforcement Learning, Scene Graph Generation, Visual Question Answering (VQA), Visual Reasoning

We propose to compose dynamic tree structures that place the objects in an
image into a visual context, helping visual reasoning tasks such as scene graph
generation and visual Q&A. Our visual context tree model, dubbed VCTree, has
two key advantages over existing structured object representations including
chains and fully-connected graphs: 1) The efficient and expressive binary tree
encodes the inherent parallel/hierarchical relationships among objects, e.g.,
"clothes" and "pants" are usually co-occur and belong to "person"; 2) the
dynamic structure varies from image to image and task to task, allowing more
content-/task-specific message passing among objects. To construct a VCTree, we
design a score function that calculates the task-dependent validity between
each object pair, and the tree is the binary version of the maximum spanning
tree from the score matrix. Then, visual contexts are encoded by bidirectional
TreeLSTM and decoded by task-specific models. We develop a hybrid learning
procedure which integrates end-task supervised learning and the tree structure
reinforcement learning, where the former's evaluation result serves as a
self-critic for the latter's structure exploration. Experimental results on two
benchmarks, which require reasoning over contexts: Visual Genome for scene
graph generation and VQA2.0 for visual Q&A, show that VCTree outperforms
state-of-the-art results while discovering interpretable visual context
structures.
An enhanced computational feature selection method for medical synonym identification via bilingualism and multi-corpus training
Authors: K. Lei, S. Si, D. Wen, Y. Shen
Date: 2018-12-05 00:00:00
Tasks: feature selection

Medical synonym identification has been an important part of medical natural
language processing (NLP). However, in the field of Chinese medical synonym
identification, there are problems like low precision and low recall rate. To
solve the problem, in this paper, we propose a method for identifying Chinese
medical synonyms. We first selected 13 features including Chinese and English
features. Then we studied the synonym identification results of each feature
alone and different combinations of the features. Through the comparison among
identification results, we present an optimal combination of features for
Chinese medical synonym identification. Experiments show that our selected
features have achieved 97.37% precision rate, 96.00% recall rate and 97.33% F1
score.
Learning to Take Directions One Step at a Time
Authors: Qiyang Hu, Adrian Wälchli, Tiziano Portenier, Matthias Zwicker, Paolo Favaro
Date: 2018-12-05 00:00:00
Tasks: Video Prediction

We present a method to generate a video sequence given a single image. Because items in an image can be animated in arbitrarily many different ways, we introduce as control signal a sequence of motion strokes. Such control signal can be automatically transferred from other videos, e.g., via bounding box tracking. Each motion stroke provides the direction to the moving object in the input image and we aim to train a network to generate an animation following a sequence of such directions. To address this task we design a novel recurrent architecture, which can be trained easily and effectively thanks to an explicit separation of past, future and current states. As we demonstrate in the experiments, our proposed architecture is capable of generating an arbitrary number of frames from a single image and a sequence of motion strokes. Key components of our architecture are an autoencoding constraint to ensure consistency with the past and a generative adversarial scheme to ensure that images look realistic and are temporally smooth. We demonstrate the effectiveness of our approach on the MNIST, KTH, Human3.6M, Push and Weizmann datasets.