pwc-archive/papers-with-abstracts huggingface dataset:
PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image
Authors: Chen Liu, Jimei Yang, Duygu Ceylan, Ersin Yumer, Yasutaka Furukawa
Date: 2018-04-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Depth Prediction, Segmentation

This paper proposes a deep neural network (DNN) for piece-wise planar
depthmap reconstruction from a single RGB image. While DNNs have brought
remarkable progress to single-image depth prediction, piece-wise planar
depthmap reconstruction requires a structured geometry representation, and has
been a difficult task to master even for DNNs. The proposed end-to-end DNN
learns to directly infer a set of plane parameters and corresponding plane
segmentation masks from a single RGB image. We have generated more than 50,000
piece-wise planar depthmaps for training and testing from ScanNet, a
large-scale RGBD video database. Our qualitative and quantitative evaluations
demonstrate that the proposed approach outperforms baseline methods in terms of
both plane segmentation and depth estimation accuracy. To the best of our
knowledge, this paper presents the first end-to-end neural architecture for
piece-wise planar reconstruction from a single RGB image. Code and data are
available at https://github.com/art-programmer/PlaneNet.
Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification
Authors: Kshiteesh Hegde, Malik Magdon-Ismail, Ram Ramanathan, Bishal Thapa
Date: 2018-04-17 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, Small Data Image Classification, Transfer Learning

We propose a novel subgraph image representation for classification of
network fragments with the targets being their parent networks. The graph image
representation is based on 2D image embeddings of adjacency matrices. We use
this image representation in two modes. First, as the input to a machine
learning algorithm. Second, as the input to a pure transfer learner. Our
conclusions from several datasets are that (a) deep learning using our
structured image features performs the best compared to benchmark graph kernel
and classical features based methods; and, (b) pure transfer learning works
effectively with minimum interference from the user and is robust against small
data.
Multimodal Emoji Prediction
Authors: Francesco Barbieri, Miguel Ballesteros, Francesco Ronzano, Horacio Saggion
Date: 2018-03-06 00:00:00
Proceeding: NAACL 2018 6
Tasks: Prediction

Emojis are small images that are commonly included in social media text
messages. The combination of visual and textual content in the same message
builds up a modern way of communication, that automatic systems are not used to
deal with. In this paper we extend recent advances in emoji prediction by
putting forward a multimodal approach that is able to predict emojis in
Instagram posts. Instagram posts are composed of pictures together with texts
which sometimes include emojis. We show that these emojis can be predicted by
using the text, but also using the picture. Our main finding is that
incorporating the two synergistic modalities, in a combined model, improves
accuracy in an emoji prediction task. This result demonstrates that these two
modalities (text and images) encode different information on the use of emojis
and therefore can complement each other.
Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment
Authors: Yingjun Ye, Xiaohui Zhang, Jian Sun
Date: 2018-04-17 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Automated vehicles are deemed to be the key element for the intelligent
transportation system in the future. Many studies have been made to improve the
Automated vehicles' ability of environment recognition and vehicle control,
while the attention paid to decision making is not enough though the decision
algorithms so far are very preliminary. Therefore, a framework of the
decision-making training and learning is put forward in this paper. It consists
of two parts: the deep reinforcement learning training program and the
high-fidelity virtual simulation environment. Then the basic microscopic
behavior, car-following, is trained within this framework. In addition,
theoretical analysis and experiments were conducted on setting reward function
for accelerating training using deep reinforcement learning. The results show
that on the premise of driving comfort, the efficiency of the trained Automated
vehicle increases 7.9% compared to the classical traffic model, intelligent
driver model. Later on, on a more complex three-lane section, we trained the
integrated model combines both car-following and lane-changing behavior, the
average speed further grows 2.4%. It indicates that our framework is effective
for Automated vehicle's decision-making learning.
Synthetic data generation for Indic handwritten text recognition
Authors: Partha Pratim Roy, Akash Mohta, Bidyut. B. Chaudhuri
Date: 2018-04-17 00:00:00
Tasks: Handwritten Text Recognition, Synthetic Data Generation

This paper presents a novel approach to generate synthetic dataset for
handwritten word recognition systems. It is difficult to recognize handwritten
scripts for which sufficient training data is not readily available or it may
be expensive to collect such data. Hence, it becomes hard to train recognition
systems owing to lack of proper dataset. To overcome such problems, synthetic
data could be used to create or expand the existing training dataset to improve
recognition performance. Any available digital data from online newspaper and
such sources can be used to generate synthetic data. In this paper, we propose
to add distortion/deformation to digital data in such a way that the underlying
pattern is preserved, so that the image so produced bears a close similarity to
actual handwritten samples. The images thus produced can be used independently
to train the system or be combined with natural handwritten data to augment the
original dataset and improve the recognition system. We experimented using
synthetic data to improve the recognition accuracy of isolated characters and
words. The framework is tested on 2 Indic scripts - Devanagari (Hindi) and
Bengali (Bangla), for numeral, character and word recognition. We have obtained
encouraging results from the experiment. Finally, the experiment with Latin
text verifies the utility of the approach.
Temporal Coherent and Graph Optimized Manifold Ranking for Visual Tracking
Authors: Bo Jiang, Doudou Lin, Bin Luo, Jin Tang
Date: 2018-04-17 00:00:00
Tasks: Graph Ranking, Visual Tracking

Recently, weighted patch representation has been widely studied for
alleviating the impact of background information included in bounding box to
improve visual tracking results. However, existing weighted patch
representation models generally exploit spatial structure information among
patches in each frame separately which ignore (1) unary featureof each patch
and (2) temporal correlation among patches in different frames. To address this
problem, we propose a novel unified temporal coherence and graph optimized
ranking model for weighted patch representation in visual tracking problem.
There are three main contributions of this paper. First, we propose to employ a
flexible graph ranking for patch weight computation which exploits both
structure information among patches and unary feature of each patch
simultaneously. Second, we propose a new more discriminative ranking model by
further considering the temporal correlation among patches in different frames.
Third, a neighborhood preserved, low-rank graph is learned and incorporated to
build a unified optimized ranking model. Experiments on two benchmark datasets
show the benefits of our model.
PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-modalities
Authors: Lan Wang, Chenqiang Gao, Luyu Yang, Yue Zhao, WangMeng Zuo, Deyu Meng
Date: 2018-04-17 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Detection, Action Recognition, Activity Detection, Representation Learning, Temporal Action Localization

Data of different modalities generally convey complimentary but heterogeneous
information, and a more discriminative representation is often preferred by
combining multiple data modalities like the RGB and infrared features. However
in reality, obtaining both data channels is challenging due to many
limitations. For example, the RGB surveillance cameras are often restricted
from private spaces, which is in conflict with the need of abnormal activity
detection for personal security. As a result, using partial data channels to
build a full representation of multi-modalities is clearly desired. In this
paper, we propose a novel Partial-modal Generative Adversarial Networks
(PM-GANs) that learns a full-modal representation using data from only partial
modalities. The full representation is achieved by a generated representation
in place of the missing data channel. Extensive experiments are conducted to
verify the performance of our proposed method on action recognition, compared
with four state-of-the-art methods. Meanwhile, a new Infrared-Visible Dataset
for action recognition is introduced, and will be the first publicly available
action dataset that contains paired infrared and visible spectrum.
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
Authors: Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
Date: 2018-04-15 00:00:00
Tasks: Diversity

Surrogate-assistance approaches have long been used in computationally
expensive domains to improve the data-efficiency of optimization algorithms.
Neuroevolution, however, has so far resisted the application of these
techniques because it requires the surrogate model to make fitness predictions
based on variable topologies, instead of a vector of parameters. Our main
insight is that we can sidestep this problem by using kernel-based surrogate
models, which require only the definition of a distance measure between
individuals. Our second insight is that the well-established Neuroevolution of
Augmenting Topologies (NEAT) algorithm provides a computationally efficient
distance measure between dissimilar networks in the form of "compatibility
distance", initially designed to maintain topological diversity. Combining
these two ideas, we introduce a surrogate-assisted neuroevolution algorithm
that combines NEAT and a surrogate model built using a compatibility distance
kernel. We demonstrate the data-efficiency of this new algorithm on the low
dimensional cart-pole swing-up problem, as well as the higher dimensional
half-cheetah running task. In both tasks the surrogate-assisted variant
achieves the same or better results with several times fewer function
evaluations as the original NEAT.
A Saliency-based Convolutional Neural Network for Table and Chart Detection in Digitized Documents
Authors: I. Kavasidis, S. Palazzo, C. Spampinato, C. Pino, D. Giordano, D. Giuffrida, P. Messina
Date: 2018-04-17 00:00:00

Deep Convolutional Neural Networks (DCNNs) have recently been applied
successfully to a variety of vision and multimedia tasks, thus driving
development of novel solutions in several application domains. Document
analysis is a particularly promising area for DCNNs: indeed, the number of
available digital documents has reached unprecedented levels, and humans are no
longer able to discover and retrieve all the information contained in these
documents without the help of automation. Under this scenario, DCNNs offers a
viable solution to automate the information extraction process from digital
documents. Within the realm of information extraction from documents, detection
of tables and charts is particularly needed as they contain a visual summary of
the most valuable information contained in a document. For a complete
automation of visual information extraction process from tables and charts, it
is necessary to develop techniques that localize them and identify precisely
their boundaries. In this paper we aim at solving the table/chart detection
task through an approach that combines deep convolutional neural networks,
graphical models and saliency concepts. In particular, we propose a
saliency-based fully-convolutional neural network performing multi-scale
reasoning on visual cues followed by a fully-connected conditional random field
(CRF) for localizing tables and charts in digital/digitized documents.
Performance analysis carried out on an extended version of ICDAR 2013 (with
annotated charts as well as tables) shows that our approach yields promising
results, outperforming existing models.
Transport-Based Pattern Theory: A Signal Transformation Approach
Authors: Liam Cattell, Gustavo K. Rohde
Date: 2018-02-20 00:00:00

In many scientific fields imaging is used to relate a certain physical
quantity to other dependent variables. Therefore, images can be considered as a
map from a real-world coordinate system to the non-negative measurements being
acquired. In this work we describe an approach for simultaneous modeling and
inference of such data, using the mathematics of optimal transport. To achieve
this, we describe a numerical implementation of the linear optimal transport
transform, based on the solution of the Monge-Ampere equation, which uses
Brenier's theorem to characterize the solution of the Monge functional as the
derivative of a convex potential function. We use our implementation of the
transform to compute a curl-free mapping between two images, and show that it
is able to match images with lower error that existing methods. Moreover, we
provide theoretical justification for properties of the linear optimal
transport framework observed in the literature, including a theorem for the
linear separation of data classes. Finally, we use our optimal transport method
to empirically demonstrate that the linear separability theorem holds, by
rendering non-linearly separable data as linearly separable following transform
to transport space.
HAMLET: Interpretable Human And Machine co-LEarning Technique
Authors: Olivier Deiss, Siddharth Biswal, Jing Jin, Haoqi Sun, M. Brandon Westover, Jimeng Sun
Date: 2018-03-26 00:00:00
Tasks: General Classification

Efficient label acquisition processes are key to obtaining robust
classifiers. However, data labeling is often challenging and subject to high
levels of label noise. This can arise even when classification targets are well
defined, if instances to be labeled are more difficult than the prototypes used
to define the class, leading to disagreements among the expert community. Here,
we enable efficient training of deep neural networks. From low-confidence
labels, we iteratively improve their quality by simultaneous learning of
machines and experts. We call it Human And Machine co-LEarning Technique
(HAMLET). Throughout the process, experts become more consistent, while the
algorithm provides them with explainable feedback for confirmation. HAMLET uses
a neural embedding function and a memory module filled with diverse reference
embeddings from different classes. Its output includes classification labels
and highly relevant reference embeddings as explanation. We took the study of
brain monitoring at intensive care unit (ICU) as an application of HAMLET on
continuous electroencephalography (cEEG) data. Although cEEG monitoring yields
large volumes of data, labeling costs and difficulty make it hard to build a
classifier. Additionally, while experts agree on the labels of clear-cut
examples of cEEG patterns, labeling many real-world cEEG data can be extremely
challenging. Thus, a large minority of sequences might be mislabeled. HAMLET
has shown significant performance gain against deep learning and other
baselines, increasing accuracy from 7.03% to 68.75% on challenging inputs.
Besides improved performance, clinical experts confirmed the interpretability
of those reference embeddings in helping explaining the classification results
by HAMLET.
Simple Baselines for Human Pose Estimation and Tracking
Authors: Bin Xiao, Haiping Wu, Yichen Wei
Date: 2018-04-17 00:00:00
Proceeding: ECCV 2018 9
Tasks: 2D Human Pose Estimation, Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation, Pose Tracking

There has been significant progress on pose estimation and increasing
interests on pose tracking in recent years. At the same time, the overall
algorithm and system complexity increases as well, making the algorithm
analysis and comparison more difficult. This work provides simple and effective
baseline methods. They are helpful for inspiring and evaluating new ideas for
the field. State-of-the-art results are achieved on challenging benchmarks. The
code will be available at https://github.com/leoxiaobin/pose.pytorch.
MetaBags: Bagged Meta-Decision Trees for Regression
Authors: Jihed Khiari, Luis Moreira-Matias, Ammar Shaker, Bernard Zenko, Saso Dzeroski
Date: 2018-04-17 00:00:00
Tasks: Inductive Bias, Meta-Learning, regression

Ensembles are popular methods for solving practical supervised learning
problems. They reduce the risk of having underperforming models in
production-grade software. Although critical, methods for learning
heterogeneous regression ensembles have not been proposed at large scale,
whereas in classical ML literature, stacking, cascading and voting are mostly
restricted to classification problems. Regression poses distinct learning
challenges that may result in poor performance, even when using well
established homogeneous ensemble schemas such as bagging or boosting.
  In this paper, we introduce MetaBags, a novel, practically useful stacking
framework for regression. MetaBags is a meta-learning algorithm that learns a
set of meta-decision trees designed to select one base model (i.e. expert) for
each query, and focuses on inductive bias reduction. A set of meta-decision
trees are learned using different types of meta-features, specially created for
this purpose - to then be bagged at meta-level. This procedure is designed to
learn a model with a fair bias-variance trade-off, and its improvement over
base model performance is correlated with the prediction diversity of different
experts on specific input space subregions. The proposed method and
meta-features are designed in such a way that they enable good predictive
performance even in subregions of space which are not adequately represented in
the available training data.
  An exhaustive empirical testing of the method was performed, evaluating both
generalization error and scalability of the approach on synthetic, open and
real-world application datasets. The obtained results show that our method
significantly outperforms existing state-of-the-art approaches.
IGCV$2$: Interleaved Structured Sparse Convolutional Neural Networks
Authors: Guotian Xie, Jingdong Wang, Ting Zhang, Jian-Huang Lai, Richang Hong, Guo-Jun Qi
Date: 2018-04-17 00:00:00

In this paper, we study the problem of designing efficient convolutional
neural network architectures with the interest in eliminating the redundancy in
convolution kernels. In addition to structured sparse kernels, low-rank kernels
and the product of low-rank kernels, the product of structured sparse kernels,
which is a framework for interpreting the recently-developed interleaved group
convolutions (IGC) and its variants (e.g., Xception), has been attracting
increasing interests.
  Motivated by the observation that the convolutions contained in a group
convolution in IGC can be further decomposed in the same manner, we present a
modularized building block, {IGCV$2$:} interleaved structured sparse
convolutions. It generalizes interleaved group convolutions, which is composed
of two structured sparse kernels, to the product of more structured sparse
kernels, further eliminating the redundancy. We present the complementary
condition and the balance condition to guide the design of structured sparse
kernels, obtaining a balance among three aspects: model size, computation
complexity and classification accuracy. Experimental results demonstrate the
advantage on the balance among these three aspects compared to interleaved
group convolutions and Xception, and competitive performance compared to other
state-of-the-art architecture design methods.
An Integrated Inverse Space Sparse Representation Framework for Tumor Classification
Authors: Xiaohui Yang, Wen-Ming Wu, Yun-Mei Chen, Xianqi Li, Juan Zhang, Dan Long, Li-Jun Yang
Date: 2018-03-09 00:00:00
Tasks: Classification, General Classification, Sparse Representation-based Classification, Specificity

Microarray gene expression data-based tumor classification is an active and
challenging issue. In this paper, an integrated tumor classification framework
is presented, which aims to exploit information in existing available samples,
and focuses on the small sample problem and unbalanced classification problem.
Firstly, an inverse space sparse representation based classification (ISSRC)
model is proposed by considering the characteristics of gene-based tumor data,
such as sparsity and a small number of training samples. A decision information
factors (DIF)-based gene selection method is constructed to enhance the
representation ability of the ISSRC. It is worth noting that the DIF is
established from reducing clinical misdiagnosis rate and dimension of small
sample data. For further improving the representation ability and
classification stability of the ISSRC, feature learning is conducted on the
selected gene subset. The feature learning method is constructed by
complementing the advantages of non-negative matrix factorization (NMF) and
deep learning. Without confusion, the ISSRC combined with gene selection and
feature learning is called the integrated ISSRC, whose stability, optimization
and the corresponding convergence are analyzed. Extensive experiments on six
public microarray gene expression datasets show the integrated ISSRC-based
tumor classification framework is superior to classical and state-of-the-art
methods. There are significant improvements in classification accuracy,
specificity and sensitivity, whether there is a tumor in the early diagnosis,
what kind of tumor, or whether metastasis occurs after tumor surgery.
Investigating Backtranslation in Neural Machine Translation
Authors: Alberto Poncelas, Dimitar Shterionov, Andy Way, Gideon Maillette de Buy Wenniger, Peyman Passban
Date: 2018-04-17 00:00:00
Tasks: Machine Translation, NMT, Translation

A prerequisite for training corpus-based machine translation (MT) systems --
either Statistical MT (SMT) or Neural MT (NMT) -- is the availability of
high-quality parallel data. This is arguably more important today than ever
before, as NMT has been shown in many studies to outperform SMT, but mostly
when large parallel corpora are available; in cases where data is limited, SMT
can still outperform NMT.
  Recently researchers have shown that back-translating monolingual data can be
used to create synthetic parallel corpora, which in turn can be used in
combination with authentic parallel data to train a high-quality NMT system.
Given that large collections of new parallel text become available only quite
rarely, backtranslation has become the norm when building state-of-the-art NMT
systems, especially in resource-poor scenarios.
  However, we assert that there are many unknown factors regarding the actual
effects of back-translated data on the translation capabilities of an NMT
model. Accordingly, in this work we investigate how using back-translated data
as a training corpus -- both as a separate standalone dataset as well as
combined with human-generated parallel data -- affects the performance of an
NMT model. We use incrementally larger amounts of back-translated data to train
a range of NMT systems for German-to-English, and analyse the resulting
translation performance.