pwc-archive/papers-with-abstracts huggingface dataset:
GA-Net: Guided Aggregation Net for End-to-end Stereo Matching
Authors: Feihu Zhang, Victor Prisacariu, Ruigang Yang, Philip H. S. Torr
Date: 2019-04-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Stereo Depth Estimation, Stereo Matching

In the stereo matching task, matching cost aggregation is crucial in both
traditional methods and deep neural network models in order to accurately
estimate disparities. We propose two novel neural net layers, aimed at
capturing local and the whole-image cost dependencies respectively. The first
is a semi-global aggregation layer which is a differentiable approximation of
the semi-global matching, the second is the local guided aggregation layer
which follows a traditional cost filtering strategy to refine thin structures.
These two layers can be used to replace the widely used 3D convolutional layer
which is computationally costly and memory-consuming as it has cubic
computational/memory complexity. In the experiments, we show that nets with a
two-layer guided aggregation block easily outperform the state-of-the-art
GC-Net which has nineteen 3D convolutional layers. We also train a deep guided
aggregation network (GA-Net) which gets better accuracies than state-of-the-art
methods on both Scene Flow dataset and KITTI benchmarks.
Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study
Authors: Tim Oblak, Klemen Grm, Aleš Jaklič, Peter Peer, Vitomir Štruc, Franc Solina
Date: 2019-04-13 00:00:00
Tasks: parameter estimation

It has been a longstanding goal in computer vision to describe the 3D physical space in terms of parameterized volumetric models that would allow autonomous machines to understand and interact with their surroundings. Such models are typically motivated by human visual perception and aim to represents all elements of the physical word ranging from individual objects to complex scenes using a small set of parameters. One of the de facto stadards to approach this problem are superquadrics - volumetric models that define various 3D shape primitives and can be fitted to actual 3D data (either in the form of point clouds or range images). However, existing solutions to superquadric recovery involve costly iterative fitting procedures, which limit the applicability of such techniques in practice. To alleviate this problem, we explore in this paper the possibility to recover superquadrics from range images without time consuming iterative parameter estimation techniques by using contemporary deep-learning models, more specifically, convolutional neural networks (CNNs). We pose the superquadric recovery problem as a regression task and develop a CNN regressor that is able to estimate the parameters of a superquadric model from a given range image. We train the regressor on a large set of synthetic range images, each containing a single (unrotated) superquadric shape and evaluate the learned model in comparaitve experiments with the current state-of-the-art. Additionally, we also present a qualitative analysis involving a dataset of real-world objects. The results of our experiments show that the proposed regressor not only outperforms the existing state-of-the-art, but also ensures a 270x faster execution time.
Combining Physical Simulators and Object-Based Networks for Control
Authors: Anurag Ajay, Maria Bauza, Jiajun Wu, Nima Fazeli, Joshua B. Tenenbaum, Alberto Rodriguez, Leslie P. Kaelbling
Date: 2019-04-13 00:00:00
Tasks: Object

Physics engines play an important role in robot planning and control;
however, many real-world control problems involve complex contact dynamics that
cannot be characterized analytically. Most physics engines therefore employ .
approximations that lead to a loss in precision. In this paper, we propose a
hybrid dynamics model, simulator-augmented interaction networks (SAIN),
combining a physics engine with an object-based neural network for dynamics
modeling. Compared with existing models that are purely analytical or purely
data-driven, our hybrid model captures the dynamics of interacting objects in a
more accurate and data-efficient manner.Experiments both in simulation and on a
real robot suggest that it also leads to better performance when used in
complex control tasks. Finally, we show that our model generalizes to novel
environments with varying object shapes and materials.
Deep-learning PDEs with unlabeled data and hardwiring physics laws
Authors: S. Mohammad H. Hashemi, Demetri Psaltis
Date: 2019-04-13 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Providing fast and accurate solutions to partial differential equations is a
problem of continuous interest to the fields of applied mathematics and
physics. With the recent advances in machine learning, the adoption learning
techniques in this domain is being eagerly pursued. We build upon earlier works
on linear and homogeneous PDEs, and develop convolutional deep neural networks
that can accurately solve nonlinear and non-homogeneous equations without the
need for labeled data. The architecture of these networks is readily accessible
for scientific disciplines who deal with PDEs and know the basics of deep
learning.
Direct Sparse Mapping
Authors: Jon Zubizarreta, Iker Aguinaga, J. M. M. Montiel
Date: 2019-04-13 00:00:00
Tasks: Simultaneous Localization and Mapping

Photometric bundle adjustment, PBA, accurately estimates geometry from video. However, current PBA systems have a temporary map that cannot manage scene reobservations. We present, DSM, a full monocular visual SLAM based on PBA. Its persistent map handles reobservations, yielding the most accurate results up to date on EuRoC for a direct method.
Texture image analysis and texture classification methods - A review
Authors: Laleh Armi, Shervan Fekri-Ershad
Date: 2019-04-13 00:00:00
Tasks: Classification, Defect Detection, General Classification, image-classification, Image Classification, Medical Image Analysis, Object Recognition, Texture Classification

Tactile texture refers to the tangible feel of a surface and visual texture
refers to see the shape or contents of the image. In the image processing, the
texture can be defined as a function of spatial variation of the brightness
intensity of the pixels. Texture is the main term used to define objects or
concepts of a given image. Texture analysis plays an important role in computer
vision cases such as object recognition, surface defect detection, pattern
recognition, medical image analysis, etc. Since now many approaches have been
proposed to describe texture images accurately. Texture analysis methods
usually are classified into four categories: statistical methods, structural,
model-based and transform-based methods. This paper discusses the various
methods used for texture or analysis in details. New researches shows the power
of combinational methods for texture analysis, which can't be in specific
category. This paper provides a review on well known combinational methods in a
specific section with details. This paper counts advantages and disadvantages
of well-known texture image descriptors in the result part. Main focus in all
of the survived methods is on discrimination performance, computational
complexity and resistance to challenges such as noise, rotation, etc. A brief
review is also made on the common classifiers used for texture image
classification. Also, a survey on texture image benchmark datasets is included.
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers
Authors: Bowen Zhao, Xi Xiao, Wanpeng Zhang, Bin Zhang, Shu-Tao Xia
Date: 2019-04-13 00:00:00
Tasks: Dimensionality Reduction

Principal Component Analysis (PCA) is a popular tool for dimensionality
reduction and feature extraction in data analysis. There is a probabilistic
version of PCA, known as Probabilistic PCA (PPCA). However, standard PCA and
PPCA are not robust, as they are sensitive to outliers. To alleviate this
problem, this paper introduces the Self-Paced Learning mechanism into PPCA, and
proposes a novel method called Self-Paced Probabilistic Principal Component
Analysis (SP-PPCA). Furthermore, we design the corresponding optimization
algorithm based on the alternative search strategy and the
expectation-maximization algorithm. SP-PPCA looks for optimal projection
vectors and filters out outliers iteratively. Experiments on both synthetic
problems and real-world datasets clearly demonstrate that SP-PPCA is able to
reduce or eliminate the impact of outliers.
HAKE: Human Activity Knowledge Engine
Authors: Yong-Lu Li, Liang Xu, Xinpeng Liu, Xijie Huang, Yue Xu, Mingyang Chen, Ze Ma, Shiyi Wang, Hao-Shu Fang, Cewu Lu
Date: 2019-04-13 00:00:00
Tasks: Action Detection, Human-Object Interaction Detection, Transfer Learning

Human activity understanding is crucial for building automatic intelligent system. With the help of deep learning, activity understanding has made huge progress recently. But some challenges such as imbalanced data distribution, action ambiguity, complex visual patterns still remain. To address these and promote the activity understanding, we build a large-scale Human Activity Knowledge Engine (HAKE) based on the human body part states. Upon existing activity datasets, we annotate the part states of all the active persons in all images, thus establish the relationship between instance activity and body part states. Furthermore, we propose a HAKE based part state recognition model with a knowledge extractor named Activity2Vec and a corresponding part state based reasoning network. With HAKE, our method can alleviate the learning difficulty brought by the long-tail data distribution, and bring in interpretability. Now our HAKE has more than 7 M+ part state annotations and is still under construction. We first validate our approach on a part of HAKE in this preliminary paper, where we show 7.2 mAP performance improvement on Human-Object Interaction recognition, and 12.38 mAP improvement on the one-shot subsets.
Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes
Authors: Chengquan Zhang, Borong Liang, Zuming Huang, Mengyi En, Junyu Han, Errui Ding, Xinghao Ding
Date: 2019-04-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Scene Text Detection, Text Detection

Previous scene text detection methods have progressed substantially over the
past years. However, limited by the receptive field of CNNs and the simple
representations like rectangle bounding box or quadrangle adopted to describe
text, previous methods may fall short when dealing with more challenging text
instances, such as extremely long text and arbitrarily shaped text. To address
these two problems, we present a novel text detector namely LOMO, which
localizes the text progressively for multiple times (or in other word, LOok
More than Once). LOMO consists of a direct regressor (DR), an iterative
refinement module (IRM) and a shape expression module (SEM). At first, text
proposals in the form of quadrangle are generated by DR branch. Next, IRM
progressively perceives the entire long text by iterative refinement based on
the extracted feature blocks of preliminary proposals. Finally, a SEM is
introduced to reconstruct more precise representation of irregular text by
considering the geometry properties of text instance, including text region,
text center line and border offsets. The state-of-the-art results on several
public benchmarks including ICDAR2017-RCTW, SCUT-CTW1500, Total-Text, ICDAR2015
and ICDAR17-MLT confirm the striking robustness and effectiveness of LOMO.
Improving detection of protein-ligand binding sites with 3D segmentation
Authors: Marta M. Stepniewska-Dziubinska, Piotr Zielenkiewicz, Pawel Siedlecki
Date: 2019-04-13 00:00:00
Tasks: Drug Discovery

In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks especially, were proven more effective than classical models for tasks like predicting binding affinity for molecular complex. In this work we investigated the earlier stage of drug discovery process - finding druggable pockets on protein surface, that can be later used to design active molecules. For this purpose we developed a 3D fully convolutional neural network capable of binding site segmentation. Our solution has high prediction accuracy and provides intuitive representations of the results, which makes it easy to incorporate into drug discovery projects. The model's source code, together with scripts for most common use-cases is freely available at http://gitlab.com/cheminfIBB/kalasanty
An Integrated Autoencoder-Based Filter for Sparse Big Data
Authors: Baogui Xin, Wei Peng
Date: 2019-04-13 00:00:00

We propose a novel filter for sparse big data, called an integrated autoencoder (IAE), which utilizes auxiliary information to mitigate data sparsity. The proposed model achieves an appropriate balance between prediction accuracy, convergence speed, and complexity. We implement experiments on a GPS trajectory dataset, and the results demonstrate that the IAE is more accurate and robust than some state-of-the-art methods.
End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning
Authors: Tao Tu, Yuan-Jui Chen, Cheng-chieh Yeh, Hung-Yi Lee
Date: 2019-04-13 00:00:00
Tasks: Cross-Lingual Transfer, text-to-speech, Text to Speech, Transfer Learning

End-to-end text-to-speech (TTS) has shown great success on large quantities of paired text plus speech data. However, laborious data collection remains difficult for at least 95% of the languages over the world, which hinders the development of TTS in different languages. In this paper, we aim to build TTS systems for such low-resource (target) languages where only very limited paired data are available. We show such TTS can be effectively constructed by transferring knowledge from a high-resource (source) language. Since the model trained on source language cannot be directly applied to target language due to input space mismatch, we propose a method to learn a mapping between source and target linguistic symbols. Benefiting from this learned mapping, pronunciation information can be preserved throughout the transferring procedure. Preliminary experiments show that we only need around 15 minutes of paired data to obtain a relatively good TTS system. Furthermore, analytic studies demonstrated that the automatically discovered mapping correlate well with the phonetic expertise.
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
Authors: Kede Ma, Wentao Liu, Tongliang Liu, Zhou Wang, DaCheng Tao
Date: 2019-04-13 00:00:00
Tasks: Blind Image Quality Assessment, Image Quality Assessment, Learning-To-Rank

Objective assessment of image quality is fundamentally important in many
image processing tasks. In this work, we focus on learning blind image quality
assessment (BIQA) models which predict the quality of a digital image with no
access to its original pristine-quality counterpart as reference. One of the
biggest challenges in learning BIQA models is the conflict between the gigantic
image space (which is in the dimension of the number of image pixels) and the
extremely limited reliable ground truth data for training. Such data are
typically collected via subjective testing, which is cumbersome, slow, and
expensive. Here we first show that a vast amount of reliable training data in
the form of quality-discriminable image pairs (DIP) can be obtained
automatically at low cost by exploiting large-scale databases with diverse
image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no
subjective opinions are used for training) model using RankNet, a pairwise
learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a
perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index.
Extensive experiments on four benchmark IQA databases demonstrate that dipIQ
outperforms state-of-the-art OU-BIQA models. The robustness of dipIQ is also
significantly improved as confirmed by the group MAximum Differentiation (gMAD)
competition method. Furthermore, we extend the proposed framework by learning
models with ListNet (a listwise L2R algorithm) on quality-discriminable image
lists (DIL). The resulting DIL Inferred Quality (dilIQ) index achieves an
additional performance gain.
Visual-Inertial Mapping with Non-Linear Factor Recovery
Authors: Vladyslav Usenko, Nikolaus Demmel, David Schubert, Jörg Stückler, Daniel Cremers
Date: 2019-04-13 00:00:00
Tasks: Motion Estimation

Cameras and inertial measurement units are complementary sensors for ego-motion estimation and environment mapping. Their combination makes visual-inertial odometry (VIO) systems more accurate and robust. For globally consistent mapping, however, combining visual and inertial information is not straightforward. To estimate the motion and geometry with a set of images large baselines are required. Because of that, most systems operate on keyframes that have large time intervals between each other. Inertial data on the other hand quickly degrades with the duration of the intervals and after several seconds of integration, it typically contains only little useful information. In this paper, we propose to extract relevant information for visual-inertial mapping from visual-inertial odometry using non-linear factor recovery. We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO. To obtain a globally consistent map we combine these factors with loop-closing constraints using bundle adjustment. The VIO factors make the roll and pitch angles of the global map observable, and improve the robustness and the accuracy of the mapping. In experiments on a public benchmark, we demonstrate superior performance of our method over the state-of-the-art approaches.
Maximum Correntropy Criterion with Variable Center
Authors: Badong Chen, Xin Wang, Yingsong Li, Jose C. Principe
Date: 2019-04-13 00:00:00
Tasks: Position

Correntropy is a local similarity measure defined in kernel space and the
maximum correntropy criterion (MCC) has been successfully applied in many areas
of signal processing and machine learning in recent years. The kernel function
in correntropy is usually restricted to the Gaussian function with center
located at zero. However, zero-mean Gaussian function may not be a good choice
for many practical applications. In this study, we propose an extended version
of correntropy, whose center can locate at any position. Accordingly, we
propose a new optimization criterion called maximum correntropy criterion with
variable center (MCC-VC). We also propose an efficient approach to optimize the
kernel width and center location in MCC-VC. Simulation results of regression
with linear in parameters (LIP) models confirm the desirable performance of the
new method.
Rethinking Classification and Localization for Object Detection
Authors: Yue Wu, Yinpeng Chen, Lu Yuan, Zicheng Liu, Lijuan Wang, Hongzhi Li, Yun Fu
Date: 2019-04-13 00:00:00
Proceeding: CVPR 2020 6
Tasks: Classification, General Classification, Object, object-detection, Object Detection

Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively.
Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)
Authors: Chandan Gautam, Aruna Tiwari, M. Tanveer
Date: 2019-04-13 00:00:00
Tasks: Classification, General Classification, Graph Embedding, One-Class Classification, One-class classifier, Outlier Detection, regression

A brain can detect outlier just by using only normal samples. Similarly,
one-class classification (OCC) also uses only normal samples to train the model
and trained model can be used for outlier detection. In this paper, a
multi-layer architecture for OCC is proposed by stacking various Graph-Embedded
Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion.
These Auto-Encoders are formulated under two types of Graph-Embedding, namely,
local and global variance-based embedding. This Graph-Embedding explores the
relationship between samples and multi-layers of Auto-Encoder project the input
features into new feature space. The last layer of this proposed architecture
is Graph-Embedded regression-based one-class classifier. The Auto-Encoders use
an unsupervised approach of learning and the final layer uses semi-supervised
(trained by only positive samples and obtained closed-form solution) approach
to learning. The proposed method is experimentally evaluated on 21 publicly
available benchmark datasets. Experimental results verify the effectiveness of
the proposed one-class classifiers over 11 existing state-of-the-art
kernel-based one-class classifiers. Friedman test is also performed to verify
the statistical significance of the claim of the superiority of the proposed
one-class classifiers over the existing state-of-the-art methods. By using two
types of Graph-Embedding, 4 variants of Graph-Embedded multi-layer KRR-based
one-class classifier has been presented in this paper. All 4 variants performed
better than the existing one-class classifiers in terms of various discussed
criteria in this paper. Hence, it can be a viable alternative for OCC task. In
the future, various other types of Auto-Encoders can be explored within
proposed architecture.
Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation
Authors: Chao Chen, Zhihang Fu, Zhihong Chen, Zhaowei Cheng, Xinyu Jin, Xian-Sheng Hua
Date: 2019-04-13 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Recent advances in unsupervised domain adaptation mainly focus on learning
shared representations by global distribution alignment without considering
class information across domains. The neglect of class information, however,
may lead to partial alignment (or even misalignment) and poor generalization
performance. For comprehensive alignment, we argue that the similarities across
different features in the source domain should be consistent with that of in
the target domain. Based on this assumption, we propose a new domain
discrepancy metric, i.e., Self-similarity Consistency (SSC), to enforce the
feature structure being consistent across domains. The renowned correlation
alignment (CORAL) is proven to be a special case, and a sub-optimal measure of
our proposed SSC. Furthermore, we also propose to mitigate the side effect of
the partial alignment and misalignment by incorporating the discriminative
information of the deep representations. Specifically, an embarrassingly simple
and effective feature norm constraint is exploited to enlarge the discrepancy
of inter-class samples. It relieves the requirements of strict alignment when
performing adaptation, therefore improving the adaptation performance
significantly. Extensive experiments on visual domain adaptation tasks
demonstrate the effectiveness of our proposed SSC metric and feature
discrimination approach.