pwc-archive/papers-with-abstracts huggingface dataset:
Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks
Authors: Chenyang Lu, Marinus Jacobus Gerardus van de Molengraft, Gijs Dubbelman
Date: 2018-04-06 00:00:00
Tasks: Bird's-Eye View Semantic Segmentation, Decoder, GPU

In this work, we research and evaluate end-to-end learning of monocular
semantic-metric occupancy grid mapping from weak binocular ground truth. The
network learns to predict four classes, as well as a camera to bird's eye view
mapping. At the core, it utilizes a variational encoder-decoder network that
encodes the front-view visual information of the driving scene and subsequently
decodes it into a 2-D top-view Cartesian coordinate system. The evaluations on
Cityscapes show that the end-to-end learning of semantic-metric occupancy grids
outperforms the deterministic mapping approach with flat-plane assumption by
more than 12% mean IoU. Furthermore, we show that the variational sampling with
a relatively small embedding vector brings robustness against vehicle dynamic
perturbations, and generalizability for unseen KITTI data. Our network achieves
real-time inference rates of approx. 35 Hz for an input image with a resolution
of 256x512 pixels and an output map with 64x64 occupancy grid cells using a
Titan V GPU.
Riemann-Theta Boltzmann Machine
Authors: Daniel Krefl, Stefano Carrazza, Babak Haghighat, Jens Kahlen
Date: 2017-12-20 00:00:00

A general Boltzmann machine with continuous visible and discrete integer valued hidden states is introduced. Under mild assumptions about the connection matrices, the probability density function of the visible units can be solved for analytically, yielding a novel parametric density function involving a ratio of Riemann-Theta functions. The conditional expectation of a hidden state for given visible states can also be calculated analytically, yielding a derivative of the logarithmic Riemann-Theta function. The conditional expectation can be used as activation function in a feedforward neural network, thereby increasing the modelling capacity of the network. Both the Boltzmann machine and the derived feedforward neural network can be successfully trained via standard gradient- and non-gradient-based optimization techniques.
Convolutional 2D Knowledge Graph Embeddings
Authors: Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel
Date: 2017-07-05 00:00:00
Tasks: Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction

Link prediction for knowledge graphs is the task of predicting missing
relationships between entities. Previous work on link prediction has focused on
shallow, fast models which can scale to large knowledge graphs. However, these
models learn less expressive features than deep, multi-layer models -- which
potentially limits performance. In this work, we introduce ConvE, a multi-layer
convolutional network model for link prediction, and report state-of-the-art
results for several established datasets. We also show that the model is highly
parameter efficient, yielding the same performance as DistMult and R-GCN with
8x and 17x fewer parameters. Analysis of our model suggests that it is
particularly effective at modelling nodes with high indegree -- which are
common in highly-connected, complex knowledge graphs such as Freebase and
YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer
from test set leakage, due to inverse relations from the training set being
present in the test set -- however, the extent of this issue has so far not
been quantified. We find this problem to be severe: a simple rule-based model
can achieve state-of-the-art results on both WN18 and FB15k. To ensure that
models are evaluated on datasets where simply exploiting inverse relations
cannot yield competitive results, we investigate and validate several commonly
used datasets -- deriving robust variants where necessary. We then perform
experiments on these robust datasets for our own and several previously
proposed models and find that ConvE achieves state-of-the-art Mean Reciprocal
Rank across most datasets.
XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained Problems
Authors: Frederic Boussemart, Christophe Lecoutre, Gilles Audemard, Cédric Piette
Date: 2016-11-10 00:00:00
Tasks: Benchmarking

We propose a major revision of the format XCSP 2.1, called XCSP3, to build integrated representations of combinatorial constrained problems. This new format is able to deal with mono/multi optimization, many types of variables, cost functions, reification, views, annotations, variable quantification, distributed, probabilistic and qualitative reasoning. The new format is made compact, highly readable, and rather easy to parse. Interestingly, it captures the structure of the problem models, through the possibilities of declaring arrays of variables, and identifying syntactic and semantic groups of constraints. The number of constraints is kept under control by introducing a limited set of basic constraint forms, and producing almost automatically some of their variations through lifting, restriction, sliding, logical combination and relaxation mechanisms. As a result, XCSP3 encompasses practically all constraints that can be found in major constraint solvers developed by the CP community. A website, which is developed conjointly with the format, contains many models and series of instances. The user can make sophisticated queries for selecting instances from very precise criteria. The objective of XCSP3 is to ease the effort required to test and compare different algorithms by providing a common test-bed of combinatorial constrained instances.
On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks
Authors: Egor Lakomkin, Mohammad Ali Zamani, Cornelius Weber, Sven Magg, Stefan Wermter
Date: 2018-04-06 00:00:00
Tasks: Data Augmentation, Emotion Recognition, Speech Emotion Recognition

Speech emotion recognition (SER) is an important aspect of effective
human-robot collaboration and received a lot of attention from the research
community. For example, many neural network-based architectures were proposed
recently and pushed the performance to a new level. However, the applicability
of such neural SER models trained only on in-domain data to noisy conditions is
currently under-researched. In this work, we evaluate the robustness of
state-of-the-art neural acoustic emotion recognition models in human-robot
interaction scenarios. We hypothesize that a robot's ego noise, room
conditions, and various acoustic events that can occur in a home environment
can significantly affect the performance of a model. We conduct several
experiments on the iCub robot platform and propose several novel ways to reduce
the gap between the model's performance during training and testing in
real-world conditions. Furthermore, we observe large improvements in the model
performance on the robot and demonstrate the necessity of introducing several
data augmentation techniques like overlaying background noise and loudness
variations to improve the robustness of the neural approaches.
Wasserstein Divergence for GANs
Authors: Jiqing Wu, Zhiwu Huang, Janine Thoma, Dinesh Acharya, Luc van Gool
Date: 2017-12-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Generation

In many domains of computer vision, generative adversarial networks (GANs)
have achieved great success, among which the family of Wasserstein GANs (WGANs)
is considered to be state-of-the-art due to the theoretical contributions and
competitive qualitative performance. However, it is very challenging to
approximate the $k$-Lipschitz constraint required by the Wasserstein-1
metric~(W-met). In this paper, we propose a novel Wasserstein
divergence~(W-div), which is a relaxed version of W-met and does not require
the $k$-Lipschitz constraint. As a concrete application, we introduce a
Wasserstein divergence objective for GANs~(WGAN-div), which can faithfully
approximate W-div through optimization. Under various settings, including
progressive growing training, we demonstrate the stability of the proposed
WGAN-div owing to its theoretical and practical advantages over WGANs. Also, we
study the quantitative and visual performance of WGAN-div on standard image
synthesis benchmarks of computer vision, showing the superior performance of
WGAN-div compared to the state-of-the-art methods.
Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems
Authors: Franziska Schirrmacher, Thomas Köhler, Christian Riess
Date: 2018-04-06 00:00:00
Tasks: Denoising, Image Restoration

Inverse problems play a central role for many classical computer vision and image processing tasks. Many inverse problems are ill-posed, and hence require a prior to regularize the solution space. However, many of the existing priors, like total variation, are based on ad-hoc assumptions that have difficulties to represent the actual distribution of natural images. Thus, a key challenge in research on image processing is to find better suited priors to represent natural images. In this work, we propose the Adaptive Quantile Sparse Image (AQuaSI) prior. It is based on a quantile filter, can be used as a joint filter on guidance data, and be readily plugged into a wide range of numerical optimization algorithms. We demonstrate the efficacy of the proposed prior in joint RGB/depth upsampling, on RGB/NIR image restoration, and in a comparison with related regularization by denoising approaches.
Motion Segmentation by Exploiting Complementary Geometric Models
Authors: Xun Xu, Loong-Fah Cheong, Zhuwen Li
Date: 2018-04-06 00:00:00
Proceeding: CVPR 2018 6
Tasks: Clustering, Motion Segmentation, Segmentation

Many real-world sequences cannot be conveniently categorized as general or
degenerate; in such cases, imposing a false dichotomy in using the fundamental
matrix or homography model for motion segmentation would lead to difficulty.
Even when we are confronted with a general scene-motion, the fundamental matrix
approach as a model for motion segmentation still suffers from several defects,
which we discuss in this paper. The full potential of the fundamental matrix
approach could only be realized if we judiciously harness information from the
simpler homography model. From these considerations, we propose a multi-view
spectral clustering framework that synergistically combines multiple models
together. We show that the performance can be substantially improved in this
way. We perform extensive testing on existing motion segmentation datasets,
achieving state-of-the-art performance on all of them; we also put forth a more
realistic and challenging dataset adapted from the KITTI benchmark, containing
real-world effects such as strong perspectives and strong forward translations
not seen in the traditional datasets.
Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder
Authors: Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Date: 2018-04-06 00:00:00
Tasks: Expressive Speech Synthesis, Speech Synthesis

Recent advances in neural autoregressive models have improve the performance
of speech synthesis (SS). However, as they lack the ability to model global
characteristics of speech (such as speaker individualities or speaking styles),
particularly when these characteristics have not been labeled, making neural
autoregressive SS systems more expressive is still an open issue. In this
paper, we propose to combine VoiceLoop, an autoregressive SS model, with
Variational Autoencoder (VAE). This approach, unlike traditional autoregressive
SS systems, uses VAE to model the global characteristics explicitly, enabling
the expressiveness of the synthesized speech to be controlled in an
unsupervised manner. Experiments using the VCTK and Blizzard2012 datasets show
the VAE helps VoiceLoop to generate higher quality speech and to control the
expressions in its synthesized speech by incorporating global characteristics
into the speech generating process.
Adversarial Binary Coding for Efficient Person Re-identification
Authors: Zheng Liu, Jie Qin, Annan Li, Yunhong Wang, Luc van Gool
Date: 2018-03-29 00:00:00
Tasks: Person Re-Identification, Triplet

Person re-identification (ReID) aims at matching persons across different
views/scenes. In addition to accuracy, the matching efficiency has received
more and more attention because of demanding applications using large-scale
data. Several binary coding based methods have been proposed for efficient
ReID, which either learn projections to map high-dimensional features to
compact binary codes, or directly adopt deep neural networks by simply
inserting an additional fully-connected layer with tanh-like activations.
However, the former approach requires time-consuming hand-crafted feature
extraction and complicated (discrete) optimizations; the latter lacks the
necessary discriminative information greatly due to the straightforward
activation functions. In this paper, we propose a simple yet effective
framework for efficient ReID inspired by the recent advances in adversarial
learning. Specifically, instead of learning explicit projections or adding
fully-connected mapping layers, the proposed Adversarial Binary Coding (ABC)
framework guides the extraction of binary codes implicitly and effectively. The
discriminability of the extracted codes is further enhanced by equipping the
ABC with a deep triplet network for the ReID task. More importantly, the ABC
and triplet network are simultaneously optimized in an end-to-end manner.
Extensive experiments on three large-scale ReID benchmarks demonstrate the
superiority of our approach over the state-of-the-art methods.
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning
Authors: Michal Byra, Tomasz Sznajder, Danijel Korzinek, Hanna Piotrzkowska-Wroblewska, Katarzyna Dobruch-Sobczak, Andrzej Nowicki, Krzysztof Marasek
Date: 2018-04-06 00:00:00
Tasks: Classification, Data Augmentation, General Classification, Image Reconstruction, Lesion Classification, Medical Image Analysis, Transfer Learning

Deep learning algorithms, especially convolutional neural networks, have
become a methodology of choice in medical image analysis. However, recent
studies in computer vision show that even a small modification of input image
intensities may cause a deep learning model to classify the image differently.
In medical imaging, the distribution of image intensities is related to applied
image reconstruction algorithm. In this paper we investigate the impact of
ultrasound image reconstruction method on breast lesion classification with
neural transfer learning. Due to high dynamic range raw ultrasonic signals are
commonly compressed in order to reconstruct B-mode images. Based on raw data
acquired from breast lesions, we reconstruct B-mode images using different
compression levels. Next, transfer learning is applied for classification.
Differently reconstructed images are employed for training and evaluation. We
show that the modification of the reconstruction algorithm leads to decrease of
classification performance. As a remedy, we propose a method of data
augmentation. We show that the augmentation of the training set with
differently reconstructed B-mode images leads to a more robust and efficient
classification. Our study suggests that it is important to take into account
image reconstruction algorithms implemented in medical scanners during
development of computer aided diagnosis systems.
Peeking the Impact of Points of Interests on Didi
Authors: Yonghong Tian, Zeyu Li, Zhiwei Xu, Xuying Meng, Bing Zheng
Date: 2018-04-06 00:00:00

Recently, the online car-hailing service, Didi, has emerged as a leader in
the sharing economy. Used by passengers and drivers extensive, it becomes
increasingly important for the car-hailing service providers to minimize the
waiting time of passengers and optimize the vehicle utilization, thus to
improve the overall user experience. Therefore, the supply-demand estimation is
an indispensable ingredient of an efficient online car-hailing service. To
improve the accuracy of the estimation results, we analyze the implicit
relationships between the points of Interest (POI) and the supply-demand gap in
this paper. The different categories of POIs have positive or negative effects
on the estimation, we propose a POI selection scheme and incorporate it into
XGBoost [1] to achieve more accurate estimation results. Our experiment
demonstrates our method provides more accurate estimation results and more
stable estimation results than the existing methods.
Fooling Vision and Language Models Despite Localization and Attention Mechanism
Authors: Xiaojun Xu, Xinyun Chen, Chang Liu, Anna Rohrbach, Trevor Darrell, Dawn Song
Date: 2017-09-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Dense Captioning, Natural Language Understanding, Open-Ended Question Answering, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Adversarial attacks are known to succeed on classifiers, but it has been an
open question whether more complex vision systems are vulnerable. In this
paper, we study adversarial examples for vision and language models, which
incorporate natural language understanding and complex structures such as
attention, localization, and modular architectures. In particular, we
investigate attacks on a dense captioning model and on two visual question
answering (VQA) models. Our evaluation shows that we can generate adversarial
examples with a high success rate (i.e., > 90%) for these models. Our work
sheds new light on understanding adversarial attacks on vision systems which
have a language component and shows that attention, bounding box localization,
and compositional internal structures are vulnerable to adversarial attacks.
These observations will inform future work towards building effective defenses.
Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices
Authors: Xi Chen, Zonghang Li, Yupeng Zhang, Ruiming Long, Hongfang Yu, Xiaojiang Du, Mohsen Guizani
Date: 2018-04-06 00:00:00
Tasks: Diversity, Reinforcement Learning

With the ever growing diversity of devices and applications that will be
connected to 5G networks, flexible and agile service orchestration with
acknowledged QoE that satisfies end-user's functional and QoS requirements is
necessary. SDN (Software-Defined Networking) and NFV (Network Function
Virtualization) are considered key enabling technologies for 5G core networks.
In this regard, this paper proposes a reinforcement learning based
QoS/QoE-aware Service Function Chaining (SFC) in SDN/NFV-enabled 5G slices.
First, it implements a lightweight QoS information collector based on LLDP,
which works in a piggyback fashion on the southbound interface of the SDN
controller, to enable QoS-awareness. Then, a DQN (Deep Q Network) based agent
framework is designed to support SFC in the context of NFV. The agent takes
into account the QoE and QoS as key aspects to formulate the reward so that it
is expected to maximize QoE while respecting QoS constraints. The experiment
results show that this framework exhibits good performance in QoE provisioning
and QoS requirements maintenance for SFC in dynamic network environments.
Multi-view Banded Spectral Clustering with Application to ICD9 Clustering
Authors: Luwan Zhang, Katherine Liao, Issac Kohane, Tianxi Cai
Date: 2018-04-06 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

Despite recent development in methodology, community detection remains a
challenging problem. Existing literature largely focuses on the standard
setting where a network is learned using an observed adjacency matrix from a
single data source. Constructing a shared network from multiple data sources is
more challenging due to the heterogeneity across populations. Additionally, no
existing method leverages the prior distance knowledge available in many
domains to help the discovery of the network structure. To bridge this gap, in
this paper we propose a novel spectral clustering method that optimally
combines multiple data sources while leveraging the prior distance knowledge.
The proposed method combines a banding step guided by the distance knowledge
with a subsequent weighting step to maximize consensus across multiple sources.
Its statistical performance is thoroughly studied under a multi-view stochastic
block model. We also provide a simple yet optimal rule of choosing weights in
practice. The efficacy and robustness of the method is fully demonstrated
through extensive simulations. Finally, we apply the method to cluster the
International classification of diseases, ninth revision (ICD9), codes and
yield a very insightful clustering structure by integrating information from a
large claim database and two healthcare systems.
Question Type Guided Attention in Visual Question Answering
Authors: Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar
Date: 2018-04-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Activity Recognition, Question Answering, Visual Question Answering, Visual Question Answering (VQA), Vocal Bursts Type Prediction

Visual Question Answering (VQA) requires integration of feature maps with
drastically different structures and focus of the correct regions. Image
descriptors have structures at multiple spatial scales, while lexical inputs
inherently follow a temporal sequence and naturally cluster into semantically
different question types. A lot of previous works use complex models to extract
feature representations but neglect to use high-level information summary such
as question types in learning. In this work, we propose Question Type-guided
Attention (QTA). It utilizes the information of question type to dynamically
balance between bottom-up and top-down visual features, respectively extracted
from ResNet and Faster R-CNN networks. We experiment with multiple VQA
architectures with extensive input ablation studies over the TDIUC dataset and
show that QTA systematically improves the performance by more than 5% across
multiple question type categories such as "Activity Recognition", "Utility" and
"Counting" on TDIUC dataset. By adding QTA on the state-of-art model MCB, we
achieve 3% improvement for overall accuracy. Finally, we propose a multi-task
extension to predict question types which generalizes QTA to applications that
lack of question type, with minimal performance loss.
Performance Evaluation of 3D Correspondence Grouping Algorithms
Authors: Jiaqi Yang, Ke Xian, Yang Xiao, Zhiguo Cao
Date: 2018-04-06 00:00:00
Tasks: 3D Object Recognition, Object Recognition, Point Cloud Registration, Retrieval

This paper presents a thorough evaluation of several widely-used 3D
correspondence grouping algorithms, motived by their significance in vision
tasks relying on correct feature correspondences. A good correspondence
grouping algorithm is desired to retrieve as many as inliers from initial
feature matches, giving a rise in both precision and recall. Towards this rule,
we deploy the experiments on three benchmarks respectively addressing shape
retrieval, 3D object recognition and point cloud registration scenarios. The
variety in application context brings a rich category of nuisances including
noise, varying point densities, clutter, occlusion and partial overlaps. It
also results to different ratios of inliers and correspondence distributions
for comprehensive evaluation. Based on the quantitative outcomes, we give a
summarization of the merits/demerits of the evaluated algorithms from both
performance and efficiency perspectives.