pwc-archive/papers-with-abstracts huggingface dataset:
CED: Color Event Camera Dataset
Authors: Cedric Scheerlinck, Henri Rebecq, Timo Stoffregen, Nick Barnes, Robert Mahony, Davide Scaramuzza
Date: 2019-04-24 00:00:00
Tasks: Event-based vision, Image Reconstruction

Event cameras are novel, bio-inspired visual sensors, whose pixels output
asynchronous and independent timestamped spikes at local intensity changes,
called 'events'. Event cameras offer advantages over conventional frame-based
cameras in terms of latency, high dynamic range (HDR) and temporal resolution.
Until recently, event cameras have been limited to outputting events in the
intensity channel, however, recent advances have resulted in the development of
color event cameras, such as the Color-DAVIS346. In this work, we present and
release the first Color Event Camera Dataset (CED), containing 50 minutes of
footage with both color frames and events. CED features a wide variety of
indoor and outdoor scenes, which we hope will help drive forward event-based
vision research. We also present an extension of the event camera simulator
ESIM that enables simulation of color events. Finally, we present an evaluation
of three state-of-the-art image reconstruction methods that can be used to
convert the Color-DAVIS346 into a continuous-time, HDR, color video camera to
visualise the event stream, and for use in downstream vision applications.
OperatorNet: Recovering 3D Shapes From Difference Operators
Authors: Ruqi Huang, Marie-Julie Rakotosaona, Panos Achlioptas, Leonidas Guibas, Maks Ovsjanikov
Date: 2019-04-24 00:00:00
Proceeding: ICCV 2019 10

This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices. To this end we introduce a novel neural architecture, called OperatorNet, which takes as input a set of linear operators representing a shape and produces its 3D embedding. We demonstrate that this approach significantly outperforms previous purely geometric methods for the same problem. Furthermore, we introduce a novel functional operator, which encodes the extrinsic or pose-dependent shape information, and thus complements purely intrinsic pose-oblivious operators, such as the classical Laplacian. Coupled with this novel operator, our reconstruction network achieves very high reconstruction accuracy, even in the presence of incomplete information about a shape, given a soft or functional map expressed in a reduced basis. Finally, we demonstrate that the multiplicative functional algebra enjoyed by these operators can be used to synthesize entirely new unseen shapes, in the context of shape interpolation and shape analogy applications.
An Exploratory Analysis of Biased Learners in Soft-Sensing Frames
Authors: Aysun Urhan, Burak Alakent
Date: 2019-04-24 00:00:00

Data driven soft sensor design has recently gained immense popularity, due to
advances in sensory devices, and a growing interest in data mining. While
partial least squares (PLS) is traditionally used in the process literature for
designing soft sensors, the statistical literature has focused on sparse
learners, such as Lasso and relevance vector machine (RVM), to solve the high
dimensional data problem. In the current study, predictive performances of
three regression techniques, PLS, Lasso and RVM were assessed and compared
under various offline and online soft sensing scenarios applied on datasets
from five real industrial plants, and a simulated process. In offline learning,
predictions of RVM and Lasso were found to be superior to those of PLS when a
large number of time-lagged predictors were used. Online prediction results
gave a slightly more complicated picture. It was found that the minimum
prediction error achieved by PLS under moving window (MW), or just-in-time
learning scheme was decreased up to ~5-10% using Lasso, or RVM. However, when a
small MW size was used, or the optimum number of PLS components was as low as
~1, prediction performance of PLS surpassed RVM, which was found to yield
occasional unstable predictions. PLS and Lasso models constructed via online
parameter tuning generally did not yield better predictions compared to those
constructed via offline tuning. We present evidence to suggest that retaining a
large portion of the available process measurement data in the predictor
matrix, instead of preselecting variables, would be more advantageous for
sparse learners in increasing prediction accuracy. As a result, Lasso is
recommended as a better substitute for PLS in soft sensors; while performance
of RVM should be validated before online application.
Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio
Authors: Kaito Fujii, Shinsaku Sakaue
Date: 2019-04-24 00:00:00
Tasks: Decision Making, feature selection, Sequential Decision Making, Stochastic Optimization

We propose a new concept named adaptive submodularity ratio to study the
greedy policy for sequential decision making. While the greedy policy is known
to perform well for a wide variety of adaptive stochastic optimization problems
in practice, its theoretical properties have been analyzed only for a limited
class of problems. We narrow the gap between theory and practice by using
adaptive submodularity ratio, which enables us to prove approximation
guarantees of the greedy policy for a substantially wider class of problems.
Examples of newly analyzed problems include important applications such as
adaptive influence maximization and adaptive feature selection. Our adaptive
submodularity ratio also provides bounds of adaptivity gaps. Experiments
confirm that the greedy policy performs well with the applications being
considered compared to standard heuristics.
A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data
Authors: Jiyu Chen, Karin Verspoor, Zenan Zhai
Date: 2019-04-24 00:00:00
Proceeding: NAACL 2019 6
Tasks: Feature Engineering, Relation, Relation Extraction, Word Embeddings

This paper focuses on a traditional relation extraction task in the context
of limited annotated data and a narrow knowledge domain. We explore this task
with a clinical corpus consisting of 200 breast cancer follow-up treatment
letters in which 16 distinct types of relations are annotated. We experiment
with an approach to extracting typed relations called window-bounded
co-occurrence (WBC), which uses an adjustable context window around entity
mentions of a relevant type, and compare its performance with a more typical
intra-sentential co-occurrence baseline. We further introduce a new
bag-of-concepts (BoC) approach to feature engineering based on the
state-of-the-art word embeddings and word synonyms. We demonstrate the
competitiveness of BoC by comparing with methods of higher complexity, and
explore its effectiveness on this small dataset.
Neural Logic Reinforcement Learning
Authors: Zhengyao Jiang, Shan Luo
Date: 2019-04-24 00:00:00
Tasks: Deep Reinforcement Learning, Inductive logic programming, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (DRL) has achieved significant breakthroughs in various tasks. However, most DRL algorithms suffer a problem of generalizing the learned policy which makes the learning performance largely affected even by minor modifications of the training environment. Except that, the use of deep neural networks makes the learned policies hard to be interpretable. To address these two challenges, we propose a novel algorithm named Neural Logic Reinforcement Learning (NLRL) to represent the policies in reinforcement learning by first-order logic. NLRL is based on policy gradient methods and differentiable inductive logic programming that have demonstrated significant advantages in terms of interpretability and generalisability in supervised tasks. Extensive experiments conducted on cliff-walking and blocks manipulation tasks demonstrate that NLRL can induce interpretable policies achieving near-optimal performance while demonstrating good generalisability to environments of different initial states and problem sizes.
Generating Token-Level Explanations for Natural Language Inference
Authors: James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal
Date: 2019-04-24 00:00:00
Proceeding: NAACL 2019 6
Tasks: Multiple Instance Learning, Natural Language Inference, Sentence, Sentence-Pair Classification

The task of Natural Language Inference (NLI) is widely modeled as supervised
sentence pair classification. While there has been a lot of work recently on
generating explanations of the predictions of classifiers on a single piece of
text, there have been no attempts to generate explanations of classifiers
operating on pairs of sentences. In this paper, we show that it is possible to
generate token-level explanations for NLI without the need for training data
explicitly annotated for this purpose. We use a simple LSTM architecture and
evaluate both LIME and Anchor explanations for this task. We compare these to a
Multiple Instance Learning (MIL) method that uses thresholded attention make
token-level predictions. The approach we present in this paper is a novel
extension of zero-shot single-sentence tagging to sentence pairs for NLI. We
conduct our experiments on the well-studied SNLI dataset that was recently
augmented with manually annotation of the tokens that explain the entailment
relation. We find that our white-box MIL-based method, while orders of
magnitude faster, does not reach the same accuracy as the black-box methods.
A CNN-RNN Architecture for Multi-Label Weather Recognition
Authors: Bin Zhao, Xuelong. Li, Xiaoqiang Lu, Zhigang Wang
Date: 2019-04-24 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Weather Recognition plays an important role in our daily lives and many
computer vision applications. However, recognizing the weather conditions from
a single image remains challenging and has not been studied thoroughly.
Generally, most previous works treat weather recognition as a single-label
classification task, namely, determining whether an image belongs to a specific
weather class or not. This treatment is not always appropriate, since more than
one weather conditions may appear simultaneously in a single image. To address
this problem, we make the first attempt to view weather recognition as a
multi-label classification task, i.e., assigning an image more than one labels
according to the displayed weather conditions. Specifically, a CNN-RNN based
multi-label classification approach is proposed in this paper. The
convolutional neural network (CNN) is extended with a channel-wise attention
model to extract the most correlated visual features. The Recurrent Neural
Network (RNN) further processes the features and excavates the dependencies
among weather classes. Finally, the weather labels are predicted step by step.
Besides, we construct two datasets for the weather recognition task and explore
the relationships among different weather conditions. Experimental results
demonstrate the superiority and effectiveness of the proposed approach. The new
constructed datasets will be available at
https://github.com/wzgwzg/Multi-Label-Weather-Recognition.
The VIA Annotation Software for Images, Audio and Video
Authors: Abhishek Dutta, Andrew Zisserman
Date: 2019-04-24 00:00:00

In this paper, we introduce a simple and standalone manual annotation tool for images, audio and video: the VGG Image Annotator (VIA). This is a light weight, standalone and offline software package that does not require any installation or setup and runs solely in a web browser. The VIA software allows human annotators to define and describe spatial regions in images or video frames, and temporal segments in audio or video. These manual annotations can be exported to plain text data formats such as JSON and CSV and therefore are amenable to further processing by other software tools. VIA also supports collaborative annotation of a large dataset by a group of human annotators. The BSD open source license of this software allows it to be used in any academic project or commercial application.
Multi-scale deep neural networks for real image super-resolution
Authors: Shangqi Gao, Xiahai Zhuang
Date: 2019-04-24 00:00:00
Tasks: GPU, Image Super-Resolution, Super-Resolution

Single image super-resolution (SR) is extremely difficult if the upscaling
factors of image pairs are unknown and different from each other, which is
common in real image SR. To tackle the difficulty, we develop two multi-scale
deep neural networks (MsDNN) in this work. Firstly, due to the high computation
complexity in high-resolution spaces, we process an input image mainly in two
different downscaling spaces, which could greatly lower the usage of GPU
memory. Then, to reconstruct the details of an image, we design a multi-scale
residual network (MsRN) in the downscaling spaces based on the residual blocks.
Besides, we propose a multi-scale dense network based on the dense blocks to
compare with MsRN. Finally, our empirical experiments show the robustness of
MsDNN for image SR when the upscaling factor is unknown. According to the
preliminary results of NTIRE 2019 image SR challenge, our team
(ZXHresearch@fudan) ranks 21-st among all participants. The implementation of
MsDNN is released https://github.com/shangqigao/gsq-image-SR
Layer Dynamics of Linearised Neural Nets
Authors: Saurav Basu, Koyel Mukherjee, Shrihari Vasudevan
Date: 2019-04-24 00:00:00

Despite the phenomenal success of deep learning in recent years, there
remains a gap in understanding the fundamental mechanics of neural nets. More
research is focussed on handcrafting complex and larger networks, and the
design decisions are often ad-hoc and based on intuition. Some recent research
has aimed to demystify the learning dynamics in neural nets by attempting to
build a theory from first principles, such as characterising the non-linear
dynamics of specialised \textit{linear} deep neural nets (such as orthogonal
networks). In this work, we expand and derive properties of learning dynamics
respected by general multi-layer linear neural nets. Although an
over-parameterisation of a single layer linear network, linear multi-layer
neural nets offer interesting insights that explain how learning dynamics
proceed in small pockets of the data space. We show in particular that multiple
layers in linear nets grow at approximately the same rate, and there are
distinct phases of learning with markedly different layer growth. We then apply
a linearisation process to a general RelU neural net and show how nonlinearity
breaks down the growth symmetry observed in liner neural nets. Overall, our
work can be viewed as an initial step in building a theory for understanding
the effect of layer design on the learning dynamics from first principles.
Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning
Authors: Peng Xu, Zhaohong Deng, Chen Cui, Te Zhang, Kup-Sze Choi, Gu Suhang, Jun Wang, Shitong Wang
Date: 2019-04-24 00:00:00
Tasks: Clustering, Sparse Learning

The superior interpretability and uncertainty modeling ability of
Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex
nonlinear systems intuitively and efficiently. However, classical TSK FS
usually adopts the whole feature space of the data for model construction,
which can result in lengthy rules for high-dimensional data and lead to
degeneration in interpretability. Furthermore, for highly nonlinear modeling
task, it is usually necessary to use a large number of rules which further
weakens the clarity and interpretability of TSK FS. To address these issues, a
concise zero-order TSK FS construction method, called ESSC-SL-CTSK-FS, is
proposed in this paper by integrating the techniques of enhanced soft subspace
clustering (ESSC) and sparse learning (SL). In this method, ESSC is used to
generate the antecedents and various sparse subspace for different fuzzy rules,
whereas SL is used to optimize the consequent parameters of the fuzzy rules,
based on which the number of fuzzy rules can be effectively reduced. Finally,
the proposed ESSC-SL-CTSK-FS method is used to construct con-cise zero-order
TSK FS that can explain the scenes in high-dimensional data modeling more
clearly and easily. Experiments are conducted on various real-world datasets to
confirm the advantages.
A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition
Authors: Yanli Ji, Feixiang Xu, Yang Yang, Fumin Shen, Heng Tao Shen, Wei-Shi Zheng
Date: 2019-04-24 00:00:00
Tasks: Action Analysis, Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Current researches of action recognition mainly focus on single-view and
multi-view recognition, which can hardly satisfies the requirements of
human-robot interaction (HRI) applications to recognize actions from arbitrary
views. The lack of datasets also sets up barriers. To provide data for
arbitrary-view action recognition, we newly collect a large-scale RGB-D action
dataset for arbitrary-view action analysis, including RGB videos, depth and
skeleton sequences. The dataset includes action samples captured in 8 fixed
viewpoints and varying-view sequences which covers the entire 360 degree view
angles. In total, 118 persons are invited to act 40 action categories, and
25,600 video samples are collected. Our dataset involves more participants,
more viewpoints and a large number of samples. More importantly, it is the
first dataset containing the entire 360 degree varying-view sequences. The
dataset provides sufficient data for multi-view, cross-view and arbitrary-view
action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to
tackle the problem of arbitrary-view action recognition. Experiment results
show that the VS-CNN achieves superior performance.
Bayesian leave-one-out cross-validation for large data
Authors: Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari
Date: 2019-04-24 00:00:00
Tasks: Model Selection

Model inference, such as model comparison, model checking, and model
selection, is an important part of model development. Leave-one-out
cross-validation (LOO) is a general approach for assessing the generalizability
of a model, but unfortunately, LOO does not scale well to large datasets. We
propose a combination of using approximate inference techniques and
probability-proportional-to-size-sampling (PPS) for fast LOO model evaluation
for large datasets. We provide both theoretical and empirical results showing
good properties for large data.
Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification
Authors: Konstantinos Drossos, Paul Magron, Tuomas Virtanen
Date: 2019-04-24 00:00:00
Tasks: Acoustic Scene Classification, Classification, Deep Learning, Domain Adaptation, General Classification, Scene Classification, Unsupervised Domain Adaptation

A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the acoustic scene classification (ASC) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel resulting from data captured with a different recording device. We build upon the theoretical model of H{\Delta}H-distance and previous adversarial discriminative deep learning method for ASC unsupervised domain adaptation, and we present an adversarial training based method using the Wasserstein distance. We improve the state-of-the-art mean accuracy on the data from the unseen conditions from 32% to 45%, using the TUT Acoustic Scenes dataset.
Deep Learning for Classification of Hyperspectral Data: A Comparative Review
Authors: Nicolas Audebert, Bertrand Saux, Sébastien Lefèvre
Date: 2019-04-24 00:00:00
Proceeding: IEEE Geoscience and Remote Sensing Magazine 2019 6
Tasks: Deep Learning, General Classification, Hyperspectral Image Classification

In recent years, deep learning techniques revolutionized the way remote
sensing data are processed. Classification of hyperspectral data is no
exception to the rule, but has intrinsic specificities which make application
of deep learning less straightforward than with other optical data. This
article presents a state of the art of previous machine learning approaches,
reviews the various deep learning approaches currently proposed for
hyperspectral classification, and identifies the problems and difficulties
which arise to implement deep neural networks for this task. In particular, the
issues of spatial and spectral resolution, data volume, and transfer of models
from multimedia images to hyperspectral data are addressed. Additionally, a
comparative study of various families of network architectures is provided and
a software toolbox is publicly released to allow experimenting with these
methods. 1 This article is intended for both data scientists with interest in
hyperspectral data and remote sensing experts eager to apply deep learning
techniques to their own dataset.
A General Framework for Edited Video and Raw Video Summarization
Authors: Xuelong. Li, Bin Zhao, Xiaoqiang Lu
Date: 2019-04-24 00:00:00
Tasks: Video Summarization

In this paper, we build a general summarization framework for both of edited
video and raw video summarization. Overall, our work can be divided into three
folds: 1) Four models are designed to capture the properties of video
summaries, i.e., containing important people and objects (importance),
representative to the video content (representativeness), no similar key-shots
(diversity) and smoothness of the storyline (storyness). Specifically, these
models are applicable to both edited videos and raw videos. 2) A comprehensive
score function is built with the weighted combination of the aforementioned
four models. Note that the weights of the four models in the score function,
denoted as property-weight, are learned in a supervised manner. Besides, the
property-weights are learned for edited videos and raw videos, respectively. 3)
The training set is constructed with both edited videos and raw videos in order
to make up the lack of training data. Particularly, each training video is
equipped with a pair of mixing-coefficients which can reduce the structure mess
in the training set caused by the rough mixture. We test our framework on three
datasets, including edited videos, short raw videos and long raw videos.
Experimental results have verified the effectiveness of the proposed framework.
Segmenting the Future
Authors: Hsu-kuang Chiu, Ehsan Adeli, Juan Carlos Niebles
Date: 2019-04-24 00:00:00
Tasks: Autonomous Driving, Decision Making, Decoder, Knowledge Distillation, Scene Understanding, Segmentation, Semantic Segmentation

Predicting the future is an important aspect for decision-making in robotics or autonomous driving systems, which heavily rely upon visual scene understanding. While prior work attempts to predict future video pixels, anticipate activities or forecast future scene semantic segments from segmentation of the preceding frames, methods that predict future semantic segmentation solely from the previous frame RGB data in a single end-to-end trainable model do not exist. In this paper, we propose a temporal encoder-decoder network architecture that encodes RGB frames from the past and decodes the future semantic segmentation. The network is coupled with a new knowledge distillation training framework specific for the forecasting task. Our method, only seeing preceding video frames, implicitly models the scene segments while simultaneously accounting for the object dynamics to infer the future scene semantic segments. Our results on Cityscapes and Apolloscape outperform the baseline and current state-of-the-art methods. Code is available at https://github.com/eddyhkchiu/segmenting_the_future/.