pwc-archive/papers-with-abstracts huggingface dataset:
Sparse and noisy LiDAR completion with RGB guidance and uncertainty
Authors: Wouter Van Gansbeke, Davy Neven, Bert de Brabandere, Luc van Gool
Date: 2019-02-14 00:00:00
Tasks: Autonomous Vehicles, Depth Completion, Depth Estimation, Depth Prediction

This work proposes a new method to accurately complete sparse LiDAR maps
guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is
indispensable in order to achieve precise depth predictions. A multitude of
applications depend on the awareness of their surroundings, and use depth cues
to reason and react accordingly. On the one hand, monocular depth prediction
methods fail to generate absolute and precise depth maps. On the other hand,
stereoscopic approaches are still significantly outperformed by LiDAR based
approaches. The goal of the depth completion task is to generate dense depth
predictions from sparse and irregular point clouds which are mapped to a 2D
plane. We propose a new framework which extracts both global and local
information in order to produce proper depth maps. We argue that simple depth
completion does not require a deep network. However, we additionally propose a
fusion method with RGB guidance from a monocular camera in order to leverage
object information and to correct mistakes in the sparse input. This improves
the accuracy significantly. Moreover, confidence masks are exploited in order
to take into account the uncertainty in the depth predictions from each
modality. This fusion method outperforms the state-of-the-art and ranks first
on the KITTI depth completion benchmark. Our code with visualizations is
available.
Multimodal music information processing and retrieval: survey and future challenges
Authors: Federico Simonetta, Stavros Ntalampiras, Federico Avanzini
Date: 2019-02-14 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval, Survey

Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years.
Study of dynamical system based obstacle avoidance via manipulating orthogonal coordinates
Authors: Wei-Ya Ren
Date: 2019-02-14 00:00:00

In this paper, we consider the general problem of obstacle avoidance based on
dynamical system. The modulation matrix is developed by introducing orthogonal
coordinates, which makes the modulation matrix more reasonable. The new
trajectory's direction can be represented by the linear combination of
orthogonal coordinates. A orthogonal coordinates manipulating approach is
proposed by introducing rotating matrix to solve the local minimal problem and
provide more reasonable motions in 3-D or higher dimension space. The proposed
method also provide a solution for patrolling around a convex shape.
Experimental results on several designed dynamical systems demonstrate the
effectiveness of the proposed approach.
Automatic Labeled LiDAR Data Generation based on Precise Human Model
Authors: Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi, Yoko SASAKI
Date: 2019-02-14 00:00:00

Following improvements in deep neural networks, state-of-the-art networks
have been proposed for human recognition using point clouds captured by LiDAR.
However, the performance of these networks strongly depends on the training
data. An issue with collecting training data is labeling. Labeling by humans is
necessary to obtain the ground truth label; however, labeling requires huge
costs. Therefore, we propose an automatic labeled data generation pipeline, for
which we can change any parameters or data generation environments. Our
approach uses a human model named Dhaiba and a background of Miraikan and
consequently generated realistic artificial data. We present 500k+ data
generated by the proposed pipeline. This paper also describes the specification
of the pipeline and data details with evaluations of various approaches.
Breast Cancer: Model Reconstruction and Image Registration from Segmented Deformed Image using Visual and Force based Analysis
Authors: Shuvendu Rana, Rory Hampson, Gordon Dobie
Date: 2019-02-14 00:00:00
Tasks: Image Reconstruction, Image Registration, Image Stitching, Surface Reconstruction

Breast lesion localization using tactile imaging is a new and developing direction in medical science. To achieve the goal, proper image reconstruction and image registration can be a valuable asset. In this paper, a new approach of the segmentation-based image surface reconstruction algorithm is used to reconstruct the surface of a breast phantom. In breast tissue, the sub-dermal vein network is used as a distinguishable pattern for reconstruction. The proposed image capturing device contacts the surface of the phantom, and surface deformation will occur due to applied force at the time of scanning. A novel force based surface rectification system is used to reconstruct a deformed surface image to its original structure. For the construction of the full surface from rectified images, advanced affine scale-invariant feature transform (A-SIFT) is proposed to reduce the affine effect in time when data capturing. Camera position based image stitching approach is applied to construct the final original non-rigid surface. The proposed model is validated in theoretical models and real scenarios, to demonstrate its advantages with respect to competing methods. The result of the proposed method, applied to path reconstruction, ends with a positioning accuracy of 99.7%
Sinkhorn Divergence of Topological Signature Estimates for Time Series Classification
Authors: Colin Stephen
Date: 2019-02-14 00:00:00
Tasks: Classification, Fault Detection, General Classification, Time Series, Time Series Analysis, Time Series Classification

Distinguishing between classes of time series sampled from dynamic systems is
a common challenge in systems and control engineering, for example in the
context of health monitoring, fault detection, and quality control. The
challenge is increased when no underlying model of a system is known,
measurement noise is present, and long signals need to be interpreted. In this
paper we address these issues with a new non parametric classifier based on
topological signatures. Our model learns classes as weighted kernel density
estimates (KDEs) over persistent homology diagrams and predicts new trajectory
labels using Sinkhorn divergences on the space of diagram KDEs to quantify
proximity. We show that this approach accurately discriminates between states
of chaotic systems that are close in parameter space, and its performance is
robust to noise.
A Novel Just-Noticeable-Difference-based Saliency-Channel Attention Residual Network for Full-Reference Image Quality Predictions
Authors: Soomin Seo, Sehwan Ki, Munchurl Kim
Date: 2019-02-14 00:00:00
Tasks: Image Quality Assessment

Recently, due to the strength of deep convolutional neural networks (CNN), many CNN-based image quality assessment (IQA) models have been studied. However, previous CNN-based IQA models likely have yet to utilize the characteristics of the human visual system (HVS) fully for IQA problems when they simply entrust everything to the CNN, expecting it to learn from a training dataset. However, in this paper, we propose a novel saliency-channel attention residual network based on the just-noticeable-difference (JND) concept for full-reference image quality assessments (FR-IQA). It is referred to as JND-SalCAR and shows significant improvements in large IQA datasets with various types of distortion. The proposed JND-SalCAR effectively learns how to incorporate human psychophysical characteristics, such as visual saliency and JND, into image quality predictions. In the proposed network, a SalCAR block is devised so that perceptually important features can be extracted with the help of saliency-based spatial attention and channel attention schemes. In addition, a saliency map serves as a guideline for predicting a patch weight map in order to afford stable training of end-to-end optimization for the JND-SalCAR. To the best of our knowledge, our work presents the first HVS-inspired trainable FR-IQA network that considers both visual saliency and the JND characteristics of the HVS. When the visual saliency map and the JND probability map are explicitly given as priors, they can be usefully combined to predict IQA scores rated by humans more precisely, eventually leading to performance improvements and faster convergence. The experimental results show that the proposed JND-SalCAR significantly outperforms all recent state-of-the-art FR-IQA methods on large IQA datasets in terms of the Spearman rank order coefficient (SRCC) and the Pearson linear correlation coefficient (PLCC).
Generalisation in fully-connected neural networks for time series forecasting
Authors: Anastasia Borovykh, Cornelis W. Oosterlee, Sander M. Bohte
Date: 2019-02-14 00:00:00
Tasks: Learning Theory, Time Series, Time Series Analysis, Time Series Forecasting

In this paper we study the generalization capabilities of fully-connected neural networks trained in the context of time series forecasting. Time series do not satisfy the typical assumption in statistical learning theory of the data being i.i.d. samples from some data-generating distribution. We use the input and weight Hessians, that is the smoothness of the learned function with respect to the input and the width of the minimum in weight space, to quantify a network's ability to generalize to unseen data. While such generalization metrics have been studied extensively in the i.i.d. setting of for example image recognition, here we empirically validate their use in the task of time series forecasting. Furthermore we discuss how one can control the generalization capability of the network by means of the training process using the learning rate, batch size and the number of training iterations as controls. Using these hyperparameters one can efficiently control the complexity of the output function without imposing explicit constraints.
Transfer Learning for Sequence Labeling Using Source Model and Target Data
Authors: Lingzhen Chen, Alessandro Moschitti
Date: 2019-02-14 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Transfer Learning

In this paper, we propose an approach for transferring the knowledge of a
neural model for sequence labeling, learned from the source domain, to a new
model trained on a target domain, where new label categories appear. Our
transfer learning (TL) techniques enable to adapt the source model using the
target data and new categories, without accessing to the source data. Our
solution consists in adding new neurons in the output layer of the target model
and transferring parameters from the source model, which are then fine-tuned
with the target data. Additionally, we propose a neural adapter to learn the
difference between the source and the target label distribution, which provides
additional important information to the target model. Our experiments on Named
Entity Recognition show that (i) the learned knowledge in the source model can
be effectively transferred when the target data contains new categories and
(ii) our neural adapter further improves such transfer.
On instabilities of deep learning in image reconstruction - Does AI come at a cost?
Authors: Vegard Antun, Francesco Renna, Clarice Poon, Ben Adcock, Anders C. Hansen
Date: 2019-02-14 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, Image Reconstruction

Deep learning, due to its unprecedented success in tasks such as image
classification, has emerged as a new tool in image reconstruction with
potential to change the field. In this paper we demonstrate a crucial
phenomenon: deep learning typically yields unstablemethods for image
reconstruction. The instabilities usually occur in several forms: (1) tiny,
almost undetectable perturbations, both in the image and sampling domain, may
result in severe artefacts in the reconstruction, (2) a small structural
change, for example a tumour, may not be captured in the reconstructed image
and (3) (a counterintuitive type of instability) more samples may yield poorer
performance. Our new stability test with algorithms and easy to use software
detects the instability phenomena. The test is aimed at researchers to test
their networks for instabilities and for government agencies, such as the Food
and Drug Administration (FDA), to secure safe use of deep learning methods.
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
Authors: Yanpeng Cao, Dayan Guan, Yulun Wu, Jiangxin Yang, Yanlong Cao, Michael Ying Yang
Date: 2019-02-14 00:00:00
Tasks: Autonomous Driving, Computational Efficiency, Pedestrian Detection

Effective fusion of complementary information captured by multi-modal sensors
(visible and infrared cameras) enables robust pedestrian detection under
various surveillance situations (e.g. daytime and nighttime). In this paper, we
present a novel box-level segmentation supervised learning framework for
accurate and real-time multispectral pedestrian detection by incorporating
features extracted in visible and infrared channels. Specifically, our method
takes pairs of aligned visible and infrared images with easily obtained
bounding box annotations as input and estimates accurate prediction maps to
highlight the existence of pedestrians. It offers two major advantages over the
existing anchor box based multispectral detection methods. Firstly, it
overcomes the hyperparameter setting problem occurred during the training phase
of anchor box based detectors and can obtain more accurate detection results,
especially for small and occluded pedestrian instances. Secondly, it is capable
of generating accurate detection results using small-size input images, leading
to improvement of computational efficiency for real-time autonomous driving
applications. Experimental results on KAIST multispectral dataset show that our
proposed method outperforms state-of-the-art approaches in terms of both
accuracy and speed.
Learn a Prior for RHEA for Better Online Planning
Authors: Xin Tong, Weiming Liu, Bin Li
Date: 2019-02-14 00:00:00
Tasks: Evolutionary Algorithms, MuJoCo, OpenAI Gym

Rolling Horizon Evolutionary Algorithms (RHEA) are a class of online planning
methods for real-time game playing; their performance is closely related to the
planning horizon and the search time allowed. In this paper, we propose to
learn a prior for RHEA in an offline manner by training a value network and a
policy network. The value network is used to reduce the planning horizon by
providing an estimation of future rewards, and the policy network is used to
initialize the population, which helps to narrow down the search scope. The
proposed algorithm, named prior-based RHEA (p-RHEA), trains policy and value
networks by performing planning and learning iteratively. In the planning
stage, the horizon-limited search assisted with the policy network and value
network is performed to improve the policies and collect training samples. In
the learning stage, the policy network and value network are trained with the
collected samples to learn better prior knowledge. Experimental results on
OpenAI Gym MuJoCo tasks show that the performance of the proposed p-RHEA is
significantly improved compared to that of RHEA.
Computed tomography data collection of the complete human mandible and valid clinical ground truth models
Authors: Jürgen Wallner, Irene Mischak, Jan Egger
Date: 2019-02-14 00:00:00
Tasks: Segmentation, valid

Image-based algorithmic software segmentation is an increasingly important
topic in many medical fields. Algorithmic segmentation is used for medical
three-dimensional visualization, diagnosis or treatment support, especially in
complex medical cases. However, accessible medical databases are limited, and
valid medical ground truth databases for the evaluation of algorithms are rare
and usually comprise only a few images. Inaccuracy or invalidity of medical
ground truth data and image-based artefacts also limit the creation of such
databases, which is especially relevant for CT data sets of the
maxillomandibular complex. This contribution provides a unique and accessible
data set of the complete mandible, including 20 valid ground truth segmentation
models originating from 10 CT scans from clinical practice without artefacts or
faulty slices. From each CT scan, two 3D ground truth models were created by
clinical experts through independent manual slice-by-slice segmentation, and
the models were statistically compared to prove their validity. These data
could be used to conduct serial image studies of the human mandible, evaluating
segmentation algorithms and developing adequate image tools.
3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation
Authors: Zhidong Liang, Ming Yang, Chunxiang Wang
Date: 2019-02-14 00:00:00
Tasks: 3D Instance Segmentation, 3D Semantic Instance Segmentation, Graph Embedding, Instance Segmentation, Semantic Segmentation

This paper introduces a novel approach for 3D semantic instance segmentation
on point clouds. A 3D convolutional neural network called submanifold sparse
convolutional network is used to generate semantic predictions and instance
embeddings simultaneously. To obtain discriminative embeddings for each 3D
instance, a structure-aware loss function is proposed which considers both the
structure information and the embedding information. To get more consistent
embeddings for each 3D instance, attention-based k nearest neighbour (KNN) is
proposed to assign different weights for different neighbours. Based on the
attention-based KNN, we add a graph convolutional network after the sparse
convolutional network to get refined embeddings. Our network can be trained
end-to-end. A simple mean-shift algorithm is utilized to cluster refined
embeddings to get final instance predictions. As a result, our framework can
output both the semantic prediction and the instance prediction. Experiments
show that our approach outperforms all state-of-art methods on ScanNet
benchmark and NYUv2 dataset.
Non-Asymptotic Analysis of Monte Carlo Tree Search
Authors: Devavrat Shah, Qiaomin Xie, Zhi Xu
Date: 2019-02-14 00:00:00
Tasks: Reinforcement Learning

In this work, we consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of infinite-horizon discounted cost Markov Decision Process (MDP). While MCTS is believed to provide an approximate value function for a given state with enough simulations, the claimed proof in the seminal works is incomplete. This is due to the fact that the variant, the Upper Confidence Bound for Trees (UCT), analyzed in prior works utilizes "logarithmic" bonus term for balancing exploration and exploitation within the tree-based search, following the insights from stochastic multi-arm bandit (MAB) literature. In effect, such an approach assumes that the regret of the underlying recursively dependent non-stationary MABs concentrates around their mean exponentially in the number of steps, which is unlikely to hold as pointed out in literature, even for stationary MABs. As the key contribution of this work, we establish polynomial concentration property of regret for a class of non-stationary MAB. This in turn establishes that the MCTS with appropriate polynomial rather than logarithmic bonus term in UCB has the claimed property. Using this as a building block, we argue that MCTS, combined with nearest neighbor supervised learning, acts as a "policy improvement" operator: it iteratively improves value function approximation for all states, due to combining with supervised learning, despite evaluating at only finitely many states. In effect, we establish that to learn an $\varepsilon$ approximation of the value function with respect to $\ell_\infty$ norm, MCTS combined with nearest neighbor requires a sample size scaling as $\widetilde{O}\big(\varepsilon^{-(d+4)}\big)$, where $d$ is the dimension of the state space. This is nearly optimal due to a minimax lower bound of $\widetilde{\Omega}\big(\varepsilon^{-(d+2)}\big)$.
Long and Short Memory Balancing in Visual Co-Tracking using Q-Learning
Authors: Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba
Date: 2019-02-14 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Self-Learning

Employing one or more additional classifiers to break the self-learning loop
in tracing-by-detection has gained considerable attention. Most of such
trackers merely utilize the redundancy to address the accumulating label error
in the tracking loop, and suffer from high computational complexity as well as
tracking challenges that may interrupt all classifiers (e.g. temporal
occlusions). We propose the active co-tracking framework, in which the main
classifier of the tracker labels samples of the video sequence, and only
consults auxiliary classifier when it is uncertain. Based on the source of the
uncertainty and the differences of two classifiers (e.g. accuracy, speed,
update frequency, etc.), different policies should be taken to exchange the
information between two classifiers. Here, we introduce a reinforcement
learning approach to find the appropriate policy by considering the state of
the tracker in a specific sequence. The proposed method yields promising
results in comparison to the best tracking-by-detection approaches.
Categorical Metadata Representation for Customized Text Classification
Authors: Jihyeok Kim, Reinald Kim Amplayo, Kyungjae Lee, Sua Sung, Minji Seo, Seung-won Hwang
Date: 2019-02-14 00:00:00
Proceeding: TACL 2019 3
Tasks: Classification, General Classification, Sentence, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification, Word Embeddings

The performance of text classification has improved tremendously using
intelligently engineered neural-based models, especially those injecting
categorical metadata as additional information, e.g., using user/product
information for sentiment classification. These information have been used to
modify parts of the model (e.g., word embeddings, attention mechanisms) such
that results can be customized according to the metadata. We observe that
current representation methods for categorical metadata, which are devised for
human consumption, are not as effective as claimed in popular classification
methods, outperformed even by simple concatenation of categorical features in
the final layer of the sentence encoder. We conjecture that categorical
features are harder to represent for machine use, as available context only
indirectly describes the category, and even such context is often scarce (for
tail category). To this end, we propose to use basis vectors to effectively
incorporate categorical metadata on various parts of a neural-based model. This
additionally decreases the number of parameters dramatically, especially when
the number of categorical features is large. Extensive experiments on various
datasets with different properties are performed and show that through our
method, we can represent categorical metadata more effectively to customize
parts of the model, including unexplored ones, and increase the performance of
the model greatly.