pwc-archive/papers-with-abstracts huggingface dataset:
Using Multi-Sense Vector Embeddings for Reverse Dictionaries
Authors: Michael A. Hedderich, Andrew Yates, Dietrich Klakow, Gerard de Melo
Date: 2019-04-02 00:00:00
Proceeding: WS 2019 5

Popular word embedding methods such as word2vec and GloVe assign a single
vector representation to each word, even if a word has multiple distinct
meanings. Multi-sense embeddings instead provide different vectors for each
sense of a word. However, they typically cannot serve as a drop-in replacement
for conventional single-sense embeddings, because the correct sense vector
needs to be selected for each word. In this work, we study the effect of
multi-sense embeddings on the task of reverse dictionaries. We propose a
technique to easily integrate them into an existing neural network architecture
using an attention mechanism. Our experiments demonstrate that large
improvements can be obtained when employing multi-sense embeddings both in the
input sequence as well as for the target representation. An analysis of the
sense distributions and of the learned attention is provided as well.
Non-Rigid Point Set Registration Networks
Authors: Lingjing Wang, Jianchun Chen, Xiang Li, Yi Fang
Date: 2019-04-02 00:00:00

Point set registration is defined as a process to determine the spatial
transformation from the source point set to the target one. Existing methods
often iteratively search for the optimal geometric transformation to register a
given pair of point sets, driven by minimizing a predefined alignment loss
function. In contrast, the proposed point registration neural network (PR-Net)
actively learns the registration pattern as a parametric function from a
training dataset, consequently predict the desired geometric transformation to
align a pair of point sets. PR-Net can transfer the learned knowledge (i.e.
registration pattern) from registering training pairs to testing ones without
additional iterative optimization. Specifically, in this paper, we develop
novel techniques to learn shape descriptors from point sets that help formulate
a clear correlation between source and target point sets. With the defined
correlation, PR-Net tends to predict the transformation so that the source and
target point sets can be statistically aligned, which in turn leads to an
optimal spatial geometric registration. PR-Net achieves robust and superior
performance for non-rigid registration of point sets, even in presence of
Gaussian noise, outliers, and missing points, but requires much less time for
registering large number of pairs. More importantly, for a new pair of point
sets, PR-Net is able to directly predict the desired transformation using the
learned model without repetitive iterative optimization routine. Our code is
available at https://github.com/Lingjing324/PR-Net.
Cooperative Embeddings for Instance, Attribute and Category Retrieval
Authors: William Thong, Cees G. M. Snoek, Arnold W. M. Smeulders
Date: 2019-04-02 00:00:00
Tasks: Attribute, Image Retrieval, Retrieval

The goal of this paper is to retrieve an image based on instance, attribute
and category similarity notions. Different from existing works, which usually
address only one of these entities in isolation, we introduce a cooperative
embedding to integrate them while preserving their specific level of semantic
representation. An algebraic structure defines a superspace filled with
instances. Attributes are axis-aligned to form subspaces, while categories
influence the arrangement of similar instances. These relationships enable them
to cooperate for their mutual benefits for image retrieval. We derive a
proxy-based softmax embedding loss to learn simultaneously all similarity
measures in both superspace and subspaces. We evaluate our model on datasets
from two different domains. Experiments on image retrieval tasks show the
benefits of the cooperative embeddings for modeling multiple image
similarities, and for discovering style evolution of instances between- and
within-categories.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Authors: Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, Juergen Gall
Date: 2019-04-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Semantic Segmentation, Scene Understanding, Segmentation, Self-Driving Cars, Semantic Segmentation

Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light detection and ranging (LiDAR) provides precise geometric information about the environment and is thus a part of the sensor suites of almost all self-driving cars. Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR. In this paper, we introduce a large dataset to propel research on laser-based semantic segmentation. We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete $360^{o}$ field-of-view of the employed automotive LiDAR. We propose three benchmark tasks based on this dataset: (i) semantic segmentation of point clouds using a single scan, (ii) semantic segmentation using multiple past scans, and (iii) semantic scene completion, which requires to anticipate the semantic scene in the future. We provide baseline experiments and show that there is a need for more sophisticated models to efficiently tackle these tasks. Our dataset opens the door for the development of more advanced methods, but also provides plentiful data to investigate new research directions.
Context and Attribute Grounded Dense Captioning
Authors: Guojun Yin, Lu Sheng, Bin Liu, Nenghai Yu, Xiaogang Wang, Jing Shao
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Dense Captioning

Dense captioning aims at simultaneously localizing semantic regions and
describing these regions-of-interest (ROIs) with short phrases or sentences in
natural language. Previous studies have shown remarkable progresses, but they
are often vulnerable to the aperture problem that a caption generated by the
features inside one ROI lacks contextual coherence with its surrounding context
in the input image. In this work, we investigate contextual reasoning based on
multi-scale message propagations from the neighboring contents to the target
ROIs. To this end, we design a novel end-to-end context and attribute grounded
dense captioning framework consisting of 1) a contextual visual mining module
and 2) a multi-level attribute grounded description generation module. Knowing
that captions often co-occur with the linguistic attributes (such as who, what
and where), we also incorporate an auxiliary supervision from hierarchical
linguistic attributes to augment the distinctiveness of the learned captions.
Extensive experiments and ablation studies on Visual Genome dataset demonstrate
the superiority of the proposed model in comparison to state-of-the-art
methods.
BCMA-ES: A Bayesian approach to CMA-ES
Authors: Eric Benhamou, David Saltiel, Sebastien Verel, Fabien Teytaud
Date: 2019-04-02 00:00:00

This paper introduces a novel theoretically sound approach for the celebrated
CMA-ES algorithm. Assuming the parameters of the multi variate normal
distribution for the minimum follow a conjugate prior distribution, we derive
their optimal update at each iteration step. Not only provides this Bayesian
framework a justification for the update of the CMA-ES algorithm but it also
gives two new versions of CMA-ES either assuming normal-Wishart or
normal-Inverse Wishart priors, depending whether we parametrize the likelihood
by its covariance or precision matrix. We support our theoretical findings by
numerical experiments that show fast convergence of these modified versions of
CMA-ES.
Vehicle Re-identification in Aerial Imagery: Dataset and Approach
Authors: Peng Wang, Bingliang Jiao, Lu Yang, Yifei Yang, Shizhou Zhang, Wei Wei, Yanning Zhang
Date: 2019-04-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: Vehicle Re-Identification

In this work, we construct a large-scale dataset for vehicle
re-identification (ReID), which contains 137k images of 13k vehicle instances
captured by UAV-mounted cameras. To our knowledge, it is the largest UAV-based
vehicle ReID dataset. To increase intra-class variation, each vehicle is
captured by at least two UAVs at different locations, with diverse view-angles
and flight-altitudes. We manually label a variety of vehicle attributes,
including vehicle type, color, skylight, bumper, spare tire and luggage rack.
Furthermore, for each vehicle image, the annotator is also required to mark the
discriminative parts that helps them to distinguish this particular vehicle
from others. Besides the dataset, we also design a specific vehicle ReID
algorithm to make full use of the rich annotation information. It is capable of
explicitly detecting discriminative parts for each specific vehicle and
significantly outperforms the evaluated baselines and state-of-the-art vehicle
ReID approaches.
On Geometric Structure of Activation Spaces in Neural Networks
Authors: Yuting Jia, Haiwen Wang, Shuo Shao, Huan Long, Yunsong Zhou, Xinbing Wang
Date: 2019-04-02 00:00:00
Tasks: General Classification

In this paper, we investigate the geometric structure of activation spaces of
fully connected layers in neural networks and then show applications of this
study. We propose an efficient approximation algorithm to characterize the
convex hull of massive points in high dimensional space. Based on this new
algorithm, four common geometric properties shared by the activation spaces are
concluded, which gives a rather clear description of the activation spaces. We
then propose an alternative classification method grounding on the geometric
structure description, which works better than neural networks alone.
Surprisingly, this data classification method can be an indicator of
overfitting in neural networks. We believe our work reveals several critical
intrinsic properties of modern neural networks and further gives a new metric
for evaluating them.
UAFS: Uncertainty-Aware Feature Selection for Problems with Missing Data
Authors: Andrew J. Becker, James P. Bagrow
Date: 2019-04-02 00:00:00
Tasks: feature selection, Imputation

Missing data are a concern in many real world data sets and imputation methods are often needed to estimate the values of missing data, but data sets with excessive missingness and high dimensionality challenge most approaches to imputation. Here we show that appropriate feature selection can be an effective preprocessing step for imputation, allowing for more accurate imputation and subsequent model predictions. The key feature of this preprocessing is that it incorporates uncertainty: by accounting for uncertainty due to missingness when selecting features we can reduce the degree of missingness while also limiting the number of uninformative features being used to make predictive models. We introduce a method to perform uncertainty-aware feature selection (UAFS), provide a theoretical motivation, and test UAFS on both real and synthetic problems, demonstrating that across a variety of data sets and levels of missingness we can improve the accuracy of imputations. Improved imputation due to UAFS also results in improved prediction accuracy when performing supervised learning using these imputed data sets. Our UAFS method is general and can be fruitfully coupled with a variety of imputation methods.
Effective Aesthetics Prediction with Multi-level Spatially Pooled Features
Authors: Vlad Hosu, Bastian Goldlucke, Dietmar Saupe
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Aesthetics Quality Assessment, Image Quality Assessment, Prediction

We propose an effective deep learning approach to aesthetics quality
assessment that relies on a new type of pre-trained features, and apply it to
the AVA data set, the currently largest aesthetics database. While previous
approaches miss some of the information in the original images, due to taking
small crops, down-scaling or warping the originals during training, we propose
the first method that efficiently supports full resolution images as an input,
and can be trained on variable input sizes. This allows us to significantly
improve upon the state of the art, increasing the Spearman rank-order
correlation coefficient (SRCC) of ground-truth mean opinion scores (MOS) from
the existing best reported of 0.612 to 0.756. To achieve this performance, we
extract multi-level spatially pooled (MLSP) features from all convolutional
blocks of a pre-trained InceptionResNet-v2 network, and train a custom shallow
Convolutional Neural Network (CNN) architecture on these new features.
Easy Transfer Learning By Exploiting Intra-domain Structures
Authors: Jindong Wang, Yiqiang Chen, Han Yu, Meiyu Huang, Qiang Yang
Date: 2019-04-02 00:00:00
Tasks: Computational Efficiency, Domain Adaptation, Model Selection, Transfer Learning

Transfer learning aims at transferring knowledge from a well-labeled domain
to a similar but different domain with limited or no labels. Unfortunately,
existing learning-based methods often involve intensive model selection and
hyperparameter tuning to obtain good results. Moreover, cross-validation is not
possible for tuning hyperparameters since there are often no labels in the
target domain. This would restrict wide applicability of transfer learning
especially in computationally-constraint devices such as wearables. In this
paper, we propose a practically Easy Transfer Learning (EasyTL) approach which
requires no model selection and hyperparameter tuning, while achieving
competitive performance. By exploiting intra-domain structures, EasyTL is able
to learn both non-parametric transfer features and classifiers. Extensive
experiments demonstrate that, compared to state-of-the-art traditional and deep
methods, EasyTL satisfies the Occam's Razor principle: it is extremely easy to
implement and use while achieving comparable or better performance in
classification accuracy and much better computational efficiency. Additionally,
it is shown that EasyTL can increase the performance of existing transfer
feature learning methods.
A Holistic Representation Guided Attention Network for Scene Text Recognition
Authors: Lu Yang, Fan Dang, Peng Wang, Hui Li, Zhen Li, Yanning Zhang
Date: 2019-04-02 00:00:00
Tasks: Decoder, Irregular Text Recognition, Scene Text Recognition

Reading irregular scene text of arbitrary shape in natural images is still a challenging problem, despite the progress made recently. Many existing approaches incorporate sophisticated network structures to handle various shapes, use extra annotations for stronger supervision, or employ hard-to-train recurrent neural networks for sequence modeling. In this work, we propose a simple yet strong approach for scene text recognition. With no need to convert input images to sequence representations, we directly connect two-dimensional CNN features to an attention-based sequence decoder which guided by holistic representation. The holistic representation can guide the attention-based decoder focus on more accurate area. As no recurrent module is adopted, our model can be trained in parallel. It achieves 1.5x to 9.4x acceleration to backward pass and 1.3x to 7.9x acceleration to forward pass, compared with the RNN counterparts. The proposed model is trained with only word-level annotations. With this simple design, our method achieves state-of-the-art or competitive recognition performance on the evaluated regular and irregular scene text benchmark datasets.
Why ResNet Works? Residuals Generalize
Authors: Fengxiang He, Tongliang Liu, DaCheng Tao
Date: 2019-04-02 00:00:00

Residual connections significantly boost the performance of deep neural
networks. However, there are few theoretical results that address the influence
of residuals on the hypothesis complexity and the generalization ability of
deep neural networks. This paper studies the influence of residual connections
on the hypothesis complexity of the neural network in terms of the covering
number of its hypothesis space. We prove that the upper bound of the covering
number is the same as chain-like neural networks, if the total numbers of the
weight matrices and nonlinearities are fixed, no matter whether they are in the
residuals or not. This result demonstrates that residual connections may not
increase the hypothesis complexity of the neural network compared with the
chain-like counterpart. Based on the upper bound of the covering number, we
then obtain an $\mathcal O(1 / \sqrt{N})$ margin-based multi-class
generalization bound for ResNet, as an exemplary case of any deep neural
network with residual connections. Generalization guarantees for similar
state-of-the-art neural network architectures, such as DenseNet and ResNeXt,
are straight-forward. From our generalization bound, a practical implementation
is summarized: to approach a good generalization ability, we need to use
regularization terms to control the magnitude of the norms of weight matrices
not to increase too much, which justifies the standard technique of weight
decay.
Fast Bayesian Restoration of Poisson Corrupted Images with INLA
Authors: Takahiro Kawashima, Hayaru Shouno
Date: 2019-04-02 00:00:00

Photon-limited images are often seen in fields such as medical imaging.
Although the number of collected photons on an image sensor statistically
follows Poisson distribution, this type of noise is intractable, unlike
Gaussian noise. In this study, we propose a Bayesian restoration method of
Poisson corrupted image using Integrated Nested Laplace Approximation (INLA),
which is a computational method to evaluate marginalized posterior
distributions of latent Gaussian models (LGMs). When the original image can be
regarded as ICAR (intrinsic conditional auto-regressive) model reasonably, our
method performs very faster than well-known ones such as loopy belief
propagation-based method and Markov chain Monte Carlo (MCMC) without decreasing
the accuracy.
Aiding Intra-Text Representations with Visual Context for Multimodal Named Entity Recognition
Authors: Omer Arshad, Ignazio Gallo, Shah Nawaz, Alessandro Calefati
Date: 2019-04-02 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

With massive explosion of social media such as Twitter and Instagram, people
daily share billions of multimedia posts, containing images and text.
Typically, text in these posts is short, informal and noisy, leading to
ambiguities which can be resolved using images. In this paper we explore
text-centric Named Entity Recognition task on these multimedia posts. We
propose an end to end model which learns a joint representation of a text and
an image. Our model extends multi-dimensional self attention technique, where
now image helps to enhance relationship between words. Experiments show that
our model is capable of capturing both textual and visual contexts with greater
accuracy, achieving state-of-the-art results on Twitter multimodal Named Entity
Recognition dataset.
Verification and Validation of Semantic Annotations
Authors: Oleksandra Panasiuk, Omar Holzknecht, Umutcan Şimşek, Elias Kärle, Dieter Fensel
Date: 2019-04-02 00:00:00

In this paper, we propose a framework to perform verification and validation of semantically annotated data. The annotations, extracted from websites, are verified against the schema.org vocabulary and Domain Specifications to ensure the syntactic correctness and completeness of the annotations. The Domain Specifications allow checking the compliance of annotations against corresponding domain-specific constraints. The validation mechanism will detect errors and inconsistencies between the content of the analyzed schema.org annotations and the content of the web pages where the annotations were found.
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier
Authors: Yuyang Zhou, Guang Cheng, Shanqing Jiang, Mian Dai
Date: 2019-04-02 00:00:00
Tasks: Anomaly Detection, Dimensionality Reduction, Ensemble Learning, feature selection, General Classification, Intrusion Detection, Network Intrusion Detection

Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics.
Looking back at Labels: A Class based Domain Adaptation Technique
Authors: Vinod Kumar Kurmi, Vinay P. Namboodiri
Date: 2019-04-02 00:00:00
Tasks: Domain Adaptation, Image Classification, Multi-class Classification

In this paper, we solve the problem of adapting classifiers across domains.
We consider the problem of domain adaptation for multi-class classification
where we are provided a labeled set of examples in a source dataset and we are
provided a target dataset with no supervision. In this setting, we propose an
adversarial discriminator based approach. While the approach based on
adversarial discriminator has been previously proposed; in this paper, we
present an informed adversarial discriminator. Our observation relies on the
analysis that shows that if the discriminator has access to all the information
available including the class structure present in the source dataset, then it
can guide the transformation of features of the target set of classes to a more
structure adapted space. Using this formulation, we obtain state-of-the-art
results for the standard evaluation on benchmark datasets. We further provide
detailed analysis which shows that using all the labeled information results in
an improved domain adaptation.
Unsupervised training of a deep clustering model for multichannel blind source separation
Authors: Lukas Drude, Daniel Hasenklever, Reinhold Haeb-Umbach
Date: 2019-04-02 00:00:00
Tasks: blind source separation, Clustering, Deep Clustering, Unsupervised Spatial Clustering

We propose a training scheme to train neural network-based source separation
algorithms from scratch when parallel clean data is unavailable. In particular,
we demonstrate that an unsupervised spatial clustering algorithm is sufficient
to guide the training of a deep clustering system. We argue that previous work
on deep clustering requires strong supervision and elaborate on why this is a
limitation. We demonstrate that (a) the single-channel deep clustering system
trained according to the proposed scheme alone is able to achieve a similar
performance as the multi-channel teacher in terms of word error rates and (b)
initializing the spatial clustering approach with the deep clustering result
yields a relative word error rate reduction of 26 % over the unsupervised
teacher.