pwc-archive/papers-with-abstracts huggingface dataset:
Salient Object Detection in the Deep Learning Era: An In-Depth Survey
Authors: Wenguan Wang, Qiuxia Lai, Huazhu Fu, Jianbing Shen, Haibin Ling, Ruigang Yang
Date: 2019-04-19 00:00:00
Tasks: Attribute, Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Prediction, Salient Object Detection

As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years. Recent advances in SOD are predominantly led by deep learning-based solutions (named deep SOD). To enable in-depth understanding of deep SOD, in this paper, we provide a comprehensive survey covering various aspects, ranging from algorithm taxonomy to unsolved issues. In particular, we first review deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection. Following that, we summarize and analyze existing SOD datasets and evaluation metrics. Then, we benchmark a large group of representative SOD models, and provide detailed analyses of the comparison results. Moreover, we study the performance of SOD algorithms under different attribute settings, which has not been thoroughly explored previously, by constructing a novel SOD dataset with rich attribute annotations covering various salient object types, challenging factors, and scene categories. We further analyze, for the first time in the field, the robustness of SOD models to random input perturbations and adversarial attacks. We also look into the generalization and difficulty of existing SOD datasets. Finally, we discuss several open issues of SOD and outline future research directions.
Simple yet efficient real-time pose-based action recognition
Authors: Dennis Ludl, Thomas Gulde, Cristóbal Curio
Date: 2019-04-19 00:00:00
Tasks: Action Detection, Action Recognition, Autonomous Driving, Skeleton Based Action Recognition, Temporal Action Localization

Recognizing human actions is a core challenge for autonomous systems as they
directly share the same space with humans. Systems must be able to recognize
and assess human actions in real-time. In order to train corresponding
data-driven algorithms, a significant amount of annotated training data is
required. We demonstrated a pipeline to detect humans, estimate their pose,
track them over time and recognize their actions in real-time with standard
monocular camera sensors. For action recognition, we encode the human pose into
a new data format called Encoded Human Pose Image (EHPI) that can then be
classified using standard methods from the computer vision community. With this
simple procedure we achieve competitive state-of-the-art performance in
pose-based action detection and can ensure real-time performance. In addition,
we show a use case in the context of autonomous driving to demonstrate how such
a system can be trained to recognize human actions using simulation data.
Data Augmentation Using GANs
Authors: Fabio Henrique Kiyoiti dos Santos Tanaka, Claus Aranha
Date: 2019-04-19 00:00:00
Tasks: Data Augmentation

In this paper we propose the use of Generative Adversarial Networks (GAN) to
generate artificial training data for machine learning tasks. The generation of
artificial training data can be extremely useful in situations such as
imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also
useful when the data contains sensitive information, and it is desirable to
avoid using the original data set as much as possible (example: medical data).
We test our proposal on benchmark data sets using different network
architectures, and show that a Decision Tree (DT) classifier trained using the
training data generated by the GAN reached the same, (and surprisingly
sometimes better), accuracy and recall than a DT trained on the original data
set.
The Seventh Answer Set Programming Competition: Design and Results
Authors: Martin Gebser, Marco Maratea, Francesco Ricca
Date: 2019-04-19 00:00:00

Answer Set Programming (ASP) is a prominent knowledge representation language
with roots in logic programming and non-monotonic reasoning. Biennial ASP
competitions are organized in order to furnish challenging benchmark
collections and assess the advancement of the state of the art in ASP solving.
  In this paper, we report on the design and results of the Seventh ASP
Competition, jointly organized by the University of Calabria (Italy), the
University of Genova (Italy), and the University of Potsdam (Germany), in
affiliation with the 14th International Conference on Logic Programming and
Non-Monotonic Reasoning (LPNMR 2017). (Under consideration for acceptance in
TPLP).
OpenTapioca: Lightweight Entity Linking for Wikidata
Authors: Antonin Delpeuch
Date: 2019-04-19 00:00:00
Tasks: Entity Linking

We propose a simple Named Entity Linking system that can be trained from Wikidata only. This demonstrates the strengths and weaknesses of this data source for this task and provides an easily reproducible baseline to compare other systems against. Our model is lightweight to train, to run and to keep synchronous with Wikidata in real time.
Zero-Shot Cross-Lingual Opinion Target Extraction
Authors: Soufian Jebbara, Philipp Cimiano
Date: 2019-04-19 00:00:00
Proceeding: NAACL 2019 6
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Multilingual Word Embeddings, Sentiment Analysis, Word Embeddings

Aspect-based sentiment analysis involves the recognition of so called opinion
target expressions (OTEs). To automatically extract OTEs, supervised learning
algorithms are usually employed which are trained on manually annotated
corpora. The creation of these corpora is labor-intensive and sufficiently
large datasets are therefore usually only available for a very narrow selection
of languages and domains. In this work, we address the lack of available
annotated data for specific languages by proposing a zero-shot cross-lingual
approach for the extraction of opinion target expressions. We leverage
multilingual word embeddings that share a common vector space across various
languages and incorporate these into a convolutional neural network
architecture for OTE extraction. Our experiments with 5 languages give
promising results: We can successfully train a model on annotated data of a
source language and perform accurate prediction on a target language without
ever using any annotated samples in that target language. Depending on the
source and target language pairs, we reach performances in a zero-shot regime
of up to 77% of a model trained on target language data. Furthermore, we can
increase this performance up to 87% of a baseline model trained on target
language data by performing cross-lingual learning from multiple source
languages.
Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net
Authors: Yunze Man, Yangsibo Huang, Junyi Feng, Xi Li, Fei Wu
Date: 2019-04-19 00:00:00
Tasks: Medical Image Analysis, Pancreas Segmentation, Q-Learning, Segmentation

Segmentation of pancreas is important for medical image analysis, yet it
faces great challenges of class imbalance, background distractions and
non-rigid geometrical features. To address these difficulties, we introduce a
Deep Q Network(DQN) driven approach with deformable U-Net to accurately segment
the pancreas by explicitly interacting with contextual information and extract
anisotropic features from pancreas. The DQN based model learns a
context-adaptive localization policy to produce a visually tightened and
precise localization bounding box of the pancreas. Furthermore, deformable
U-Net captures geometry-aware information of pancreas by learning geometrically
deformable filters for feature extraction. Experiments on NIH dataset validate
the effectiveness of the proposed framework in pancreas segmentation.
SelFlow: Self-Supervised Learning of Optical Flow
Authors: Pengpeng Liu, Michael Lyu, Irwin King, Jia Xu
Date: 2019-04-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Optical Flow Estimation, Self-Supervised Learning

We present a self-supervised learning approach for optical flow. Our method
distills reliable flow estimations from non-occluded pixels, and uses these
predictions as ground truth to learn optical flow for hallucinated occlusions.
We further design a simple CNN to utilize temporal information from multiple
frames for better flow estimation. These two principles lead to an approach
that yields the best performance for unsupervised optical flow learning on the
challenging benchmarks including MPI Sintel, KITTI 2012 and 2015. More notably,
our self-supervised pre-trained model provides an excellent initialization for
supervised fine-tuning. Our fine-tuned models achieve state-of-the-art results
on all three datasets. At the time of writing, we achieve EPE=4.26 on the
Sintel benchmark, outperforming all submitted methods.
Listen to the Image
Authors: Di Hu, Dong Wang, Xuelong. Li, Feiping Nie, Qi. Wang
Date: 2019-04-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Translation

Visual-to-auditory sensory substitution devices can assist the blind in
sensing the visual environment by translating the visual information into a
sound pattern. To improve the translation quality, the task performances of the
blind are usually employed to evaluate different encoding schemes. In contrast
to the toilsome human-based assessment, we argue that machine model can be also
developed for evaluation, and more efficient. To this end, we firstly propose
two distinct cross-modal perception model w.r.t. the late-blind and
congenitally-blind cases, which aim to generate concrete visual contents based
on the translated sound. To validate the functionality of proposed models, two
novel optimization strategies w.r.t. the primary encoding scheme are presented.
Further, we conduct sets of human-based experiments to evaluate and compare
them with the conducted machine-based assessments in the cross-modal generation
task. Their highly consistent results w.r.t. different encoding schemes
indicate that using machine model to accelerate optimization evaluation and
reduce experimental cost is feasible to some extent, which could dramatically
promote the upgrading of encoding scheme then help the blind to improve their
visual perception ability.
Shallow Neural Network can Perfectly Classify an Object following Separable Probability Distribution
Authors: Youngjae Min, Hye Won Chung
Date: 2019-04-19 00:00:00
Tasks: Classification, General Classification

Guiding the design of neural networks is of great importance to save enormous
resources consumed on empirical decisions of architectural parameters. This
paper constructs shallow sigmoid-type neural networks that achieve 100%
accuracy in classification for datasets following a linear separability
condition. The separability condition in this work is more relaxed than the
widely used linear separability. Moreover, the constructed neural network
guarantees perfect classification for any datasets sampled from a separable
probability distribution. This generalization capability comes from the
saturation of sigmoid function that exploits small margins near the boundaries
of intervals formed by the separable probability distribution.
Recognizing the vocabulary of Brazilian popular newspapers with a free-access computational dictionary
Authors: Maria José Finatto, Oto Vale, Eric Laporte
Date: 2019-04-19 00:00:00

We report an experiment to check the identification of a set of words in
popular written Portuguese with two versions of a computational dictionary of
Brazilian Portuguese, DELAF PB 2004 and DELAF PB 2015. This dictionary is
freely available for use in linguistic analyses of Brazilian Portuguese and
other researches, which justifies critical study. The vocabulary comes from the
PorPopular corpus, made of popular newspapers Di{\'a}rio Ga{\'u}cho (DG) and
Massa! (MA). From DG, we retained a set of texts with 984.465 words (tokens),
published in 2008, with the spelling used before the Portuguese Language
Orthographic Agreement adopted in 2009. From MA, we examined papers of 2012,
2014 e 2015, with 215.776 words (tokens), all with the new spelling. The
checking involved: a) generating lists of words (types) occurring in DG and MA;
b) comparing them with the entry lists of both versions of DELAF PB; c)
assessing the coverage of this vocabulary; d) proposing ways of incorporating
the items not covered. The results of the work show that an average of 19% of
the types in DG were not found in DELAF PB 2004 or 2015. In MA, this average is
13%. Switching versions of the dictionary affected slightly the performance in
recognizing the words.
Code-Switching for Enhancing NMT with Pre-Specified Translation
Authors: Kai Song, Yue Zhang, Heng Yu, Weihua Luo, Kun Wang, Min Zhang
Date: 2019-04-19 00:00:00
Proceeding: NAACL 2019 6
Tasks: Data Augmentation, NMT, Translation

Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags for lexicon words and the use of hard constraints during decoding. Both methods can hurt translation fidelity for various reasons. We investigate a data augmentation method, making code-switched training data by replacing source phrases with their target translations. Our method does not change the MNT model or decoding algorithm, allowing the model to learn lexicon translations by copying source-side target words. Extensive experiments show that our method achieves consistent improvements over existing approaches, improving translation of constrained words without hurting unconstrained words.
Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks
Authors: Wenjia Wang, Junxuan Chen, Jie Zhao, Ying Chi, Xuansong Xie, Li Zhang, Xian-Sheng Hua
Date: 2019-04-19 00:00:00
Tasks: Segmentation

The identification of pulmonary lobes is of great importance in disease
diagnosis and treatment. A few lung diseases have regional disorders at lobar
level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this
work, we propose an automated segmentation of pulmonary lobes using
coordination-guided deep neural networks from chest CT images. We first employ
an automated lung segmentation to extract the lung area from CT image, then
exploit volumetric convolutional neural network (V-net) for segmenting the
pulmonary lobes. To reduce the misclassification of different lobes, we
therefore adopt coordination-guided convolutional layers (CoordConvs) that
generate additional feature maps of the positional information of pulmonary
lobes. The proposed model is trained and evaluated on a few publicly available
datasets and has achieved the state-of-the-art accuracy with a mean Dice
coefficient index of 0.947 $\pm$ 0.044.
Deep Likelihood Network for Image Restoration with Multiple Degradation Levels
Authors: Yiwen Guo, Ming Lu, WangMeng Zuo, Chang-Shui Zhang, Yurong Chen
Date: 2019-04-19 00:00:00
Tasks: Image Inpainting, Image Restoration, Super-Resolution

Convolutional neural networks have been proven effective in a variety of image restoration tasks. Most state-of-the-art solutions, however, are trained using images with a single particular degradation level, and their performance deteriorates drastically when applied to other degradation settings. In this paper, we propose deep likelihood network (DL-Net), aiming at generalizing off-the-shelf image restoration networks to succeed over a spectrum of degradation levels. We slightly modify an off-the-shelf network by appending a simple recursive module, which is derived from a fidelity term, for disentangling the computation for multiple degradation levels. Extensive experimental results on image inpainting, interpolation, and super-resolution show the effectiveness of our DL-Net.
Epistasis-based Basis Estimation Method for Simplifying the Problem Space of an Evolutionary Search in Binary Representation
Authors: Junghwan Lee, Yong-Hyuk Kim
Date: 2019-04-19 00:00:00

An evolutionary search space can be smoothly transformed via a suitable
change of basis; however, it can be difficult to determine an appropriate
basis. In this paper, a method is proposed to select an optimum basis can be
used to simplify an evolutionary search space in a binary encoding scheme. The
basis search method is based on a genetic algorithm and the fitness evaluation
is based on the epistasis, which is an indicator of the complexity of a genetic
algorithm. Two tests were conducted to validate the proposed method when
applied to two different evolutionary search problems. The first searched for
an appropriate basis to apply, while the second searched for a solution to the
test problem. The results obtained after the identified basis had been applied
were compared to those with the original basis, and it was found that the
proposed method provided superior results.
AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks
Authors: Xianzhi Du, Mostafa El-Khamy, Jungwon Lee
Date: 2019-04-19 00:00:00
Tasks: Disparity Estimation, Stereo Disparity Estimation, Stereo Matching, Stereo Matching Hand

In this paper, a new deep learning architecture for stereo disparity
estimation is proposed. The proposed atrous multiscale network (AMNet) adopts
an efficient feature extractor with depthwise-separable convolutions and an
extended cost volume that deploys novel stereo matching costs on the deep
features. A stacked atrous multiscale network is proposed to aggregate rich
multiscale contextual information from the cost volume which allows for
estimating the disparity with high accuracy at multiple scales. AMNet can be
further modified to be a foreground-background aware network, FBA-AMNet, which
is capable of discriminating between the foreground and the background objects
in the scene at multiple scales. An iterative multitask learning method is
proposed to train FBA-AMNet end-to-end. The proposed disparity estimation
networks, AMNet and FBA-AMNet, show accurate disparity estimates and advance
the state of the art on the challenging Middlebury, KITTI 2012, KITTI 2015, and
Sceneflow stereo disparity estimation benchmarks.
Optimal initialization of K-means using Particle Swarm Optimization
Authors: Ashutosh Mahesh Pednekar
Date: 2019-04-19 00:00:00

This paper proposes the use of an optimization algorithm, namely PSO to
decide the initial centroids in K-means, to eventually get better accuracy. The
vectorized notation of the optimal centroids can be thought of as entities in
an optimization space, where the accuracy of K-means over a random subset of
the data could act as a fitness measure. The resultant optimal vector can be
used as the initial centroids for K-means.
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA
Authors: Ricardo Pio Monti, Kun Zhang, Aapo Hyvarinen
Date: 2019-04-19 00:00:00
Tasks: Causal Discovery

We consider the problem of inferring causal relationships between two or more
passively observed variables. While the problem of such causal discovery has
been extensively studied especially in the bivariate setting, the majority of
current methods assume a linear causal relationship, and the few methods which
consider non-linear dependencies usually make the assumption of additive noise.
Here, we propose a framework through which we can perform causal discovery in
the presence of general non-linear relationships. The proposed method is based
on recent progress in non-linear independent component analysis and exploits
the non-stationarity of observations in order to recover the underlying sources
or latent disturbances. We show rigorously that in the case of bivariate causal
discovery, such non-linear ICA can be used to infer the causal direction via a
series of independence tests. We further propose an alternative measure of
causal direction based on asymptotic approximations to the likelihood ratio, as
well as an extension to multivariate causal discovery. We demonstrate the
capabilities of the proposed method via a series of simulation studies and
conclude with an application to neuroimaging data.
Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes
Authors: Qi. Wang, Junyu. Gao, Xuelong. Li
Date: 2019-04-19 00:00:00
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation

Semantic segmentation, a pixel-level vision task, is developed rapidly by
using convolutional neural networks (CNNs). Training CNNs requires a large
amount of labeled data, but manually annotating data is difficult. For
emancipating manpower, in recent years, some synthetic datasets are released.
However, they are still different from real scenes, which causes that training
a model on the synthetic data (source domain) cannot achieve a good performance
on real urban scenes (target domain). In this paper, we propose a weakly
supervised adversarial domain adaptation to improve the segmentation
performance from synthetic data to real scenes, which consists of three deep
neural networks. To be specific, a detection and segmentation ("DS" for short)
model focuses on detecting objects and predicting segmentation map; a
pixel-level domain classifier ("PDC" for short) tries to distinguish the image
features from which domains; an object-level domain classifier ("ODC" for
short) discriminates the objects from which domains and predicts the objects
classes. PDC and ODC are treated as the discriminators, and DS is considered as
the generator. By adversarial learning, DS is supposed to learn
domain-invariant features. In experiments, our proposed method yields the new
record of mIoU metric in the same problem.
SCANN: Synthesis of Compact and Accurate Neural Networks
Authors: Shayan Hassantabar, Zeyu Wang, Niraj K. Jha
Date: 2019-04-19 00:00:00
Tasks: Dimensionality Reduction, Neural Network Compression, Small Data Image Classification

Deep neural networks (DNNs) have become the driving force behind recent artificial intelligence (AI) research. An important problem with implementing a neural network is the design of its architecture. Typically, such an architecture is obtained manually by exploring its hyperparameter space and kept fixed during training. This approach is time-consuming and inefficient. Another issue is that modern neural networks often contain millions of parameters, whereas many applications and devices require small inference models. However, efforts to migrate DNNs to such devices typically entail a significant loss of classification accuracy. To address these challenges, we propose a two-step neural network synthesis methodology, called DR+SCANN, that combines two complementary approaches to design compact and accurate DNNs. At the core of our framework is the SCANN methodology that uses three basic architecture-changing operations, namely connection growth, neuron growth, and connection pruning, to synthesize feed-forward architectures with arbitrary structure. SCANN encapsulates three synthesis methodologies that apply a repeated grow-and-prune paradigm to three architectural starting points. DR+SCANN combines the SCANN methodology with dataset dimensionality reduction to alleviate the curse of dimensionality. We demonstrate the efficacy of SCANN and DR+SCANN on various image and non-image datasets. We evaluate SCANN on MNIST and ImageNet benchmarks. In addition, we also evaluate the efficacy of using dimensionality reduction alongside SCANN (DR+SCANN) on nine small to medium-size datasets. We also show that our synthesis methodology yields neural networks that are much better at navigating the accuracy vs. energy efficiency space. This would enable neural network-based inference even on Internet-of-Things sensors.