pwc-archive/papers-with-abstracts huggingface dataset:
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering
Authors: Medhini Narasimhan, Alexander G. Schwing
Date: 2018-09-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Factual Visual Question Answering, General Knowledge, Misconceptions, Question Answering, Retrieval, Visual Question Answering, Visual Question Answering (VQA)

Question answering is an important task for autonomous agents and virtual
assistants alike and was shown to support the disabled in efficiently
navigating an overwhelming environment. Many existing methods focus on
observation-based questions, ignoring our ability to seamlessly combine
observed content with general knowledge. To understand interactions with a
knowledge base, a dataset has been introduced recently and keyword matching
techniques were shown to yield compelling results despite being vulnerable to
misconceptions due to synonyms and homographs. To address this issue, we
develop a learning-based approach which goes straight to the facts via a
learned embedding space. We demonstrate state-of-the-art results on the
challenging recently introduced fact-based visual question answering dataset,
outperforming competing methods by more than 5%.
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation
Authors: Yuan-Ting Hu, Jia-Bin Huang, Alexander G. Schwing
Date: 2018-09-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Deep Learning, Optical Flow Estimation, Saliency Prediction, Segmentation, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Salient Object Detection, Video Segmentation, Video Semantic Segmentation

Unsupervised video segmentation plays an important role in a wide variety of
applications from object identification to compression. However, to date, fast
motion, motion blur and occlusions pose significant challenges. To address
these challenges for unsupervised video segmentation, we develop a novel
saliency estimation technique as well as a novel neighborhood graph, based on
optical flow and edge cues. Our approach leads to significantly better initial
foreground-background estimates and their robust as well as accurate diffusion
across time. We evaluate our proposed algorithm on the challenging DAVIS,
SegTrack v2 and FBMS-59 datasets. Despite the usage of only a standard edge
detector trained on 200 images, our method achieves state-of-the-art results
outperforming deep learning based methods in the unsupervised setting. We even
demonstrate competitive results comparable to deep learning based methods in
the semi-supervised setting on the DAVIS dataset.
VideoMatch: Matching based Video Object Segmentation
Authors: Yuan-Ting Hu, Jia-Bin Huang, Alexander G. Schwing
Date: 2018-09-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Memorization, Object, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Video object segmentation is challenging yet important in a wide variety of
applications for video analysis. Recent works formulate video object
segmentation as a prediction task using deep nets to achieve appealing
state-of-the-art performance. Due to the formulation as a prediction task, most
of these methods require fine-tuning during test time, such that the deep nets
memorize the appearance of the objects of interest in the given video. However,
fine-tuning is time-consuming and computationally expensive, hence the
algorithms are far from real time. To address this issue, we develop a novel
matching based algorithm for video object segmentation. In contrast to
memorization based classification techniques, the proposed approach learns to
match extracted features to a provided template without memorizing the
appearance of the objects. We validate the effectiveness and the robustness of
the proposed method on the challenging DAVIS-16, DAVIS-17, Youtube-Objects and
JumpCut datasets. Extensive results show that our method achieves comparable
performance without fine-tuning and is much more favorable in terms of
computational time.
Text2Scene: Generating Compositional Scenes from Textual Descriptions
Authors: Fuwen Tan, Song Feng, Vicente Ordonez
Date: 2018-09-04 00:00:00
Proceeding: CVPR 2019 6

In this paper, we propose Text2Scene, a model that generates various forms of compositional scene representations from natural language descriptions. Unlike recent works, our method does NOT use Generative Adversarial Networks (GANs). Text2Scene instead learns to sequentially generate objects and their attributes (location, size, appearance, etc) at every time step by attending to different parts of the input text and the current status of the generated scene. We show that under minor modifications, the proposed framework can handle the generation of different forms of scene representations, including cartoon-like scenes, object layouts corresponding to real images, and synthetic images. Our method is not only competitive when compared with state-of-the-art GAN-based methods using automatic metrics and superior based on human judgments but also has the advantage of producing interpretable results.
Accelerating Beam Sweeping in mmWave Standalone 5G New Radios using Recurrent Neural Networks
Authors: Asim Mazin, Mohamed Elkourdi, Richard D. Gitlin
Date: 2018-09-04 00:00:00

Millimeter wave (mmWave) is a key technology to support high data rate
demands for 5G applications. Highly directional transmissions are crucial at
these frequencies to compensate for high isotropic pathloss. This reliance on
di- rectional beamforming, however, makes the cell discovery (cell search)
challenging since both base station (gNB) and user equipment (UE) jointly
perform a search over angular space to locate potential beams to initiate
communication. In the cell discovery phase, sequential beam sweeping is
performed through the angular coverage region in order to transmit
synchronization signals. The sweeping pattern can either be a linear rotation
or a hopping pattern that makes use of additional information. This paper
proposes beam sweeping pattern prediction, based on the dynamic distribution of
user traffic, using a form of recurrent neural networks (RNNs) called Gated
Recurrent Unit (GRU). The spatial distribution of users is inferred from data
in call detail records (CDRs) of the cellular network. Results show that the
users spatial distribution and their approximate location (direction) can be
accurately predicted based on CDRs data using GRU, which is then used to
calculate the sweeping pattern in the angular domain during cell search.
Adversarial Attacks on Node Embeddings via Graph Poisoning
Authors: Aleksandar Bojchevski, Stephan Günnemann
Date: 2018-09-04 00:00:00
Proceeding: ICLR 2019 5
Tasks: Representation Learning

The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods, there is currently no study of their robustness to adversarial attacks. We provide the first adversarial vulnerability analysis on the widely used family of methods based on random walks. We derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks. We further show that our attacks are transferable since they generalize to many models and are successful even when the attacker is restricted.
Graph Convolutional Neural Networks based on Quantum Vertex Saliency
Authors: Lu Bai, Yuhang Jiao, Luca Rossi, Lixin Cui, Jian Cheng, Edwin R. Hancock
Date: 2018-09-04 00:00:00
Tasks: General Classification, Graph Classification

This paper proposes a new Quantum Spatial Graph Convolutional Neural Network
(QSGCNN) model that can directly learn a classification function for graphs of
arbitrary sizes. Unlike state-of-the-art Graph Convolutional Neural Network
(GCNN) models, the proposed QSGCNN model incorporates the process of
identifying transitive aligned vertices between graphs, and transforms
arbitrary sized graphs into fixed-sized aligned vertex grid structures. In
order to learn representative graph characteristics, a new quantum spatial
graph convolution is proposed and employed to extract multi-scale vertex
features, in terms of quantum information propagation between grid vertices of
each graph. Since the quantum spatial convolution preserves the grid structures
of the input vertices (i.e., the convolution layer does not change the original
spatial sequence of vertices), the proposed QSGCNN model allows to directly
employ the traditional convolutional neural network architecture to further
learn from the global graph topology, providing an end-to-end deep learning
architecture that integrates the graph representation and learning in the
quantum spatial graph convolution layer and the traditional convolutional layer
for graph classifications. We demonstrate the effectiveness of the proposed
QSGCNN model in relation to existing state-of-the-art methods. The proposed
QSGCNN model addresses the shortcomings of information loss and imprecise
information representation arising in existing GCN models associated with the
use of SortPooling or SumPooling layers. Experiments on benchmark graph
classification datasets demonstrate the effectiveness of the proposed QSGCNN
model.
Chi-Square Test Neural Network: A New Binary Classifier based on Backpropagation Neural Network
Authors: Yuan Wu, Lingling Li, Lian Li
Date: 2018-09-04 00:00:00
Tasks: Binary Classification, Classification, General Classification

We introduce the chi-square test neural network: a single hidden layer
backpropagation neural network using chi-square test theorem to redefine the
cost function and the error function. The weights and thresholds are modified
using standard backpropagation algorithm. The proposed approach has the
advantage of making consistent data distribution over training and testing
sets. It can be used for binary classification. The experimental results on
real world data sets indicate that the proposed algorithm can significantly
improve the classification accuracy comparing to related approaches.
A Novel Neural Sequence Model with Multiple Attentions for Word Sense Disambiguation
Authors: Mahtab Ahmed, Muhammad Rifayat Samee, Robert E. Mercer
Date: 2018-09-04 00:00:00
Tasks: Decoder, Sentence, Word Sense Disambiguation

Word sense disambiguation (WSD) is a well researched problem in computational
linguistics. Different research works have approached this problem in different
ways. Some state of the art results that have been achieved for this problem
are by supervised models in terms of accuracy, but they often fall behind
flexible knowledge-based solutions which use engineered features as well as
human annotators to disambiguate every target word. This work focuses on
bridging this gap using neural sequence models incorporating the well-known
attention mechanism. The main gist of our work is to combine multiple
attentions on different linguistic features through weights and to provide a
unified framework for doing this. This weighted attention allows the model to
easily disambiguate the sense of an ambiguous word by attending over a suitable
portion of a sentence. Our extensive experiments show that multiple attention
enables a more versatile encoder-decoder model leading to state of the art
results.
JobComposer: Career Path Optimization via Multicriteria Utility Learning
Authors: Richard J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo
Date: 2018-09-04 00:00:00

With online professional network platforms (OPNs, e.g., LinkedIn, Xing, etc.)
becoming popular on the web, people are now turning to these platforms to
create and share their professional profiles, to connect with others who share
similar professional aspirations and to explore new career opportunities. These
platforms however do not offer a long-term roadmap to guide career progression
and improve workforce employability. The career trajectories of OPN users can
serve as a reference but they are not always optimal. A career plan can also be
devised through consultation with career coaches, whose knowledge may however
be limited to a few industries. To address the above limitations, we present a
novel data-driven approach dubbed JobComposer to automate career path planning
and optimization. Its key premise is that the observed career trajectories in
OPNs may not necessarily be optimal, and can be improved by learning to
maximize the sum of payoffs attainable by following a career path. At its
heart, JobComposer features a decomposition-based multicriteria utility
learning procedure to achieve the best tradeoff among different payoff criteria
in career path planning. Extensive studies using a city state-based OPN dataset
demonstrate that JobComposer returns career paths better than other baseline
methods and the actual career paths.
The Effect of Context on Metaphor Paraphrase Aptness Judgments
Authors: Yuri Bizzoni, Shalom Lappin
Date: 2018-09-04 00:00:00
Proceeding: WS 2019 5
Tasks: Sentence

We conduct two experiments to study the effect of context on metaphor
paraphrase aptness judgments. The first is an AMT crowd source task in which
speakers rank metaphor paraphrase candidate sentence pairs in short document
contexts for paraphrase aptness. In the second we train a composite DNN to
predict these human judgments, first in binary classifier mode, and then as
gradient ratings. We found that for both mean human judgments and our DNN's
predictions, adding document context compresses the aptness scores towards the
center of the scale, raising low out of context ratings and decreasing high out
of context scores. We offer a provisional explanation for this compression
effect.
Iris recognition in cases of eye pathology
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-09-04 00:00:00
Tasks: Image Segmentation, Iris Recognition, Semantic Segmentation

This chapter provides insight on how iris recognition, one of the leading
biometric identification technologies in the world, can be impacted by
pathologies and illnesses present in the eye, what are the possible
repercussions of this influence, and what are the possible means for taking
such effects into account when matching iris samples.
  To make this study possible, a special database of iris images has been used,
representing more than 20 different medical conditions of the ocular region
(including cataract, glaucoma, rubeosis iridis, synechiae, iris defects,
corneal pathologies and other) and containing almost 3000 samples collected
from 230 distinct irises. Then, with the use of four different iris recognition
methods, a series of experiments has been conducted, concluding in several
important observations.
  One of the most popular ocular disorders worldwide - the cataract - is shown
to worsen genuine comparison scores when results obtained from
cataract-affected eyes are compared to those coming from healthy irises. An
analysis devoted to different types of impact on eye structures caused by
diseases is also carried out with significant results. The enrollment process
is highly sensitive to those eye conditions that make the iris obstructed or
introduce geometrical distortions. Disorders affecting iris geometry, or
producing obstructions are exceptionally capable of degrading the genuine
comparison scores, so that the performance of the entire biometric system can
be influenced. Experiments also reveal that imperfect execution of the image
segmentation stage is the most prominent contributor to recognition errors.
A Roadmap for Robust End-to-End Alignment
Authors: Lê Nguyên Hoang
Date: 2018-09-04 00:00:00

This paper discussed the {\it robust alignment} problem, that is, the problem of aligning the goals of algorithms with human preferences. It presented a general roadmap to tackle this issue. Interestingly, this roadmap identifies 5 critical steps, as well as many relevant aspects of these 5 steps. In other words, we have presented a large number of hopefully more tractable subproblems that readers are highly encouraged to tackle. Hopefully, this combination allows to better highlight the most pressing problems, how every expertise can be best used to, and how combining the solutions to subproblems might add up to solve robust alignment.
A Neural Network Aided Approach for LDPC Coded DCO-OFDM with Clipping Distortion
Authors: Yuan He, Ming Jiang, Chunming Zhao
Date: 2018-09-04 00:00:00
Tasks: Decoder

In this paper, a neural network-aided bit-interleaved coded modulation
(NN-BICM) receiver is designed to mitigate the nonlinear clipping distortion in
the LDPC coded direct currentbiased optical orthogonal frequency division
multiplexing (DCOOFDM) systems. Taking the cross-entropy as loss function, a
feed forward network is trained by backpropagation algorithm to output the
condition probability through the softmax activation function, thereby
assisting in a modified log-likelihood ratio (LLR) improvement. To reduce the
complexity, this feed-forward network simplifies the input layer with a single
symbol and the corresponding Gaussian variance instead of focusing on the
inter-carrier interference between multiple subcarriers. On the basis of the
neural network-aided BICM with Gray labelling, we propose a novel stacked
network architecture of the bitinterleaved coded modulation with iterative
decoding (NN-BICMID). Its performance has been improved further by calculating
the condition probability with the aid of a priori probability that derived
from the extrinsic LLRs in the LDPC decoder at the last iteration, at the
expense of customizing neural network detectors at each iteration time
separately. Utilizing the optimal DC bias as the midpoint of the dynamic
region, the simulation results demonstrate that both the NN-BICM and NN-BICM-ID
schemes achieve noticeable performance gains than other counterparts, in which
the NN-BICM-ID clearly outperforms NN-BICM with various modulation and coding
schemes.
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Authors: Paul-Edouard Sarlin, Frédéric Debraine, Marcin Dymczyk, Roland Siegwart, Cesar Cadena
Date: 2018-09-04 00:00:00
Tasks: Pose Estimation, Visual Localization

Many robotics applications require precise pose estimates despite operating
in large and changing environments. This can be addressed by visual
localization, using a pre-computed 3D model of the surroundings. The pose
estimation then amounts to finding correspondences between 2D keypoints in a
query image and 3D points in the model using local descriptors. However,
computational power is often limited on robotic platforms, making this task
challenging in large-scale environments. Binary feature descriptors
significantly speed up this 2D-3D matching, and have become popular in the
robotics community, but also strongly impair the robustness to perceptual
aliasing and changes in viewpoint, illumination and scene structure. In this
work, we propose to leverage recent advances in deep learning to perform an
efficient hierarchical localization. We first localize at the map level using
learned image-wide global descriptors, and subsequently estimate a precise pose
from 2D-3D matches computed in the candidate places only. This restricts the
local search and thus allows to efficiently exploit powerful non-binary
descriptors usually dismissed on resource-constrained devices. Our approach
results in state-of-the-art localization performance while running in real-time
on a popular mobile platform, enabling new prospects for robotics research.
Parameter Transfer Extreme Learning Machine based on Projective Model
Authors: Chao Chen, Boyuan Jiang, Xinyu Jin
Date: 2018-09-04 00:00:00
Tasks: Domain Adaptation, feature selection, Transfer Learning

Recent years, transfer learning has attracted much attention in the community
of machine learning. In this paper, we mainly focus on the tasks of parameter
transfer under the framework of extreme learning machine (ELM). Unlike the
existing parameter transfer approaches, which incorporate the source model
information into the target by regularizing the di erence between the source
and target domain parameters, an intuitively appealing projective-model is
proposed to bridge the source and target model parameters. Specifically, we
formulate the parameter transfer in the ELM networks by the means of parameter
projection, and train the model by optimizing the projection matrix and
classifier parameters jointly. Further more, the `L2,1-norm structured sparsity
penalty is imposed on the source domain parameters, which encourages the joint
feature selection and parameter transfer. To evaluate the e ectiveness of the
proposed method, comprehensive experiments on several commonly used domain
adaptation datasets are presented. The results show that the proposed method
significantly outperforms the non-transfer ELM networks and other classical
transfer learning methods.
Aesthetic Discrimination of Graph Layouts
Authors: Moritz Klammler, Tamara Mchedlidze, Alexey Pak
Date: 2018-09-04 00:00:00
Tasks: Data Augmentation, Graph Generation

This paper addresses the following basic question: given two layouts of the
same graph, which one is more aesthetically pleasing? We propose a neural
network-based discriminator model trained on a labeled dataset that decides
which of two layouts has a higher aesthetic quality. The feature vectors used
as inputs to the model are based on known graph drawing quality metrics,
classical statistics, information-theoretical quantities, and two-point
statistics inspired by methods of condensed matter physics. The large corpus of
layout pairs used for training and testing is constructed using force-directed
drawing algorithms and the layouts that naturally stem from the process of
graph generation. It is further extended using data augmentation techniques.
The mean prediction accuracy of our model is 95.70%, outperforming
discriminators based on stress and on the linear combination of popular quality
metrics by a statistically significant margin.
Geometric Operator Convolutional Neural Network
Authors: Yangling Ma, Yixin Luo, Zhouwang Yang
Date: 2018-09-04 00:00:00

The Convolutional Neural Network (CNN) has been successfully applied in many
fields during recent decades; however it lacks the ability to utilize prior
domain knowledge when dealing with many realistic problems. We present a
framework called Geometric Operator Convolutional Neural Network (GO-CNN) that
uses domain knowledge, wherein the kernel of the first convolutional layer is
replaced with a kernel generated by a geometric operator function. This
framework integrates many conventional geometric operators, which allows it to
adapt to a diverse range of problems. Under certain conditions, we
theoretically analyze the convergence and the bound of the generalization
errors between GO-CNNs and common CNNs. Although the geometric operator
convolution kernels have fewer trainable parameters than common convolution
kernels, the experimental results indicate that GO-CNN performs more accurately
than common CNN on CIFAR-10/100. Furthermore, GO-CNN reduces dependence on the
amount of training examples and enhances adversarial stability. In the
practical task of medically diagnosing bone fractures, GO-CNN obtains 3%
improvement in terms of the recall.