pwc-archive/papers-with-abstracts huggingface dataset:
HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing
Authors: Wei Jiang, Zhenghua Li, Yu Zhang, Min Zhang
Date: 2019-03-11 00:00:00
Tasks: General Classification, Semantic Parsing, UCCA Parsing

This paper describes a simple UCCA semantic graph parsing approach. The key
idea is to convert a UCCA semantic graph into a constituent tree, in which
extra labels are deliberately designed to mark remote edges and discontinuous
nodes for future recovery. In this way, we can make use of existing syntactic
parsing techniques. Based on the data statistics, we recover discontinuous
nodes directly according to the output labels of the constituent parser and use
a biaffine classification model to recover the more complex remote edges. The
classification model and the constituent parser are simultaneously trained
under the multi-task learning framework. We use the multilingual BERT as extra
features in the open tracks. Our system ranks the first place in the six
English/German closed/open tracks among seven participating systems. For the
seventh cross-lingual track, where there is little training data for French, we
propose a language embedding approach to utilize English and German training
data, and our result ranks the second place.
MSFD:Multi-Scale Receptive Field Face Detector
Authors: Qiushan Guo, Yuan Dong, Yu Guo, Hongliang Bai
Date: 2019-03-11 00:00:00
Tasks: GPU

We aim to study the multi-scale receptive fields of a single convolutional
neural network to detect faces of varied scales. This paper presents our
Multi-Scale Receptive Field Face Detector (MSFD), which has superior
performance on detecting faces at different scales and enjoys real-time
inference speed. MSFD agglomerates context and texture by hierarchical
structure. More additional information and rich receptive field bring
significant improvement but generate marginal time consumption. We
simultaneously propose an anchor assignment strategy which can cover faces with
a wide range of scales to improve the recall rate of small faces and rotated
faces. To reduce the false positive rate, we train our detector with focal loss
which keeps the easy samples from overwhelming. As a result, MSFD reaches
superior results on the FDDB, Pascal-Faces and WIDER FACE datasets, and can run
at 31 FPS on GPU for VGA-resolution images.
An Innovative Word Encoding Method For Text Classification Using Convolutional Neural Network
Authors: Amr Adel Helmy, Yasser M. K. Omar, Rania Hodhod
Date: 2019-03-11 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

Text classification plays a vital role today especially with the intensive
use of social networking media. Recently, different architectures of
convolutional neural networks have been used for text classification in which
one-hot vector, and word embedding methods are commonly used. This paper
presents a new language independent word encoding method for text
classification. The proposed model converts raw text data to low-level feature
dimension with minimal or no preprocessing steps by using a new approach called
binary unique number of word "BUNOW". BUNOW allows each unique word to have an
integer ID in a dictionary that is represented as a k-dimensional vector of its
binary equivalent. The output vector of this encoding is fed into a
convolutional neural network (CNN) model for classification. Moreover, the
proposed model reduces the neural network parameters, allows faster computation
with few network layers, where a word is atomic representation the document as
in word level, and decrease memory consumption for character level
representation. The provided CNN model is able to work with other languages or
multi-lingual text without the need for any changes in the encoding method. The
model outperforms the character level and very deep character level CNNs models
in terms of accuracy, network parameters, and memory consumption; the results
show total classification accuracy 91.99% and error 8.01% using AG's News
dataset compared to the state of art methods that have total classification
accuracy 91.45% and error 8.55%, in addition to the reduction in input feature
vector and neural network parameters by 62% and 34%, respectively.
Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering
Authors: Shima Kamyab, Rasool Sabzi, Zohreh Azimifar
Date: 2019-03-11 00:00:00
Tasks: Diversity, Inverse Rendering

Deep generative models are stochastic neural networks capable of learning the
distribution of data so as to generate new samples. Conditional Variational
Autoencoder (CVAE) is a powerful deep generative model aiming at maximizing the
lower bound of training data log-likelihood. In the CVAE structure, there is
appropriate regularizer, which makes it applicable for suitably constraining
the solution space in solving ill-posed problems and providing high
generalization power. Considering the stochastic prediction characteristic in
CVAE, depending on the problem at hand, it is desirable to be able to control
the uncertainty in CVAE predictions. Therefore, in this paper we analyze the
impact of CVAE's condition on the diversity of solutions given by our designed
CVAE in 3D shape inverse rendering as a prediction problem. The experimental
results using Modelnet10 and Shapenet datasets show the appropriate performance
of our designed CVAE and verify the hypothesis: \emph{"The more informative the
conditions in terms of object pose are, the less diverse the CVAE predictions
are}".
The Unconstrained Ear Recognition Challenge 2019 - ArXiv Version With Appendix
Authors: Žiga Emeršič, Aruna Kumar S. V., B. S. Harish, Weronika Gutfeter, Jalil Nourmohammadi Khiarak, Andrzej Pacut, Earnest Hansley, Mauricio Pamplona Segundo, Sudeep Sarkar, Hyeonjung Park, Gi Pyo Nam, Ig-Jae Kim, Sagar G. Sangodkar, Ümit Kaçar, Murvet Kirci, Li Yuan, Jishou Yuan, Haonan Zhao, Fei Lu, Junying Mao, Xiaoshuang Zhang, Dogucan Yaman, Fevziye Irem Eyiokur, Kadir Bulut Özler, Hazim Kemal Ekenel, Debbrota Paul Chowdhury, Sambit Bakshi, Pankaj K. Sa, Banshidhar Majhi, Peter Peer, Vitomir Štruc
Date: 2019-03-11 00:00:00
Tasks: Benchmarking, Person Recognition

This paper presents a summary of the 2019 Unconstrained Ear Recognition
Challenge (UERC), the second in a series of group benchmarking efforts centered
around the problem of person recognition from ear images captured in
uncontrolled settings. The goal of the challenge is to assess the performance
of existing ear recognition techniques on a challenging large-scale ear dataset
and to analyze performance of the technology from various viewpoints, such as
generalization abilities to unseen data characteristics, sensitivity to
rotations, occlusions and image resolution and performance bias on sub-groups
of subjects, selected based on demographic criteria, i.e. gender and ethnicity.
Research groups from 12 institutions entered the competition and submitted a
total of 13 recognition approaches ranging from descriptor-based methods to
deep-learning models. The majority of submissions focused on ensemble based
methods combining either representations from multiple deep models or
hand-crafted with learned image descriptors. Our analysis shows that methods
incorporating deep learning models clearly outperform techniques relying solely
on hand-crafted descriptors, even though both groups of techniques exhibit
similar behaviour when it comes to robustness to various covariates, such
presence of occlusions, changes in (head) pose, or variability in image
resolution. The results of the challenge also show that there has been
considerable progress since the first UERC in 2017, but that there is still
ample room for further research in this area.
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
Authors: Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine
Date: 2019-03-11 00:00:00

Touch sensing is widely acknowledged to be important for dexterous robotic
manipulation, but exploiting tactile sensing for continuous, non-prehensile
manipulation is challenging. General purpose control techniques that are able
to effectively leverage tactile sensing as well as accurate physics models of
contacts and forces remain largely elusive, and it is unclear how to even
specify a desired behavior in terms of tactile percepts. In this paper, we take
a step towards addressing these issues by combining high-resolution tactile
sensing with data-driven modeling using deep neural network dynamics models. We
propose deep tactile MPC, a framework for learning to perform tactile servoing
from raw tactile sensor inputs, without manual supervision. We show that this
method enables a robot equipped with a GelSight-style tactile sensor to
manipulate a ball, analog stick, and 20-sided die, learning from unsupervised
autonomous interaction and then using the learned tactile predictive model to
reposition each object to user-specified configurations, indicated by a goal
tactile reading. Videos, visualizations and the code are available here:
https://sites.google.com/view/deeptactilempc
Singing voice conversion with non-parallel data
Authors: Xin Chen, Wei Chu, Jinxi Guo, Ning Xu
Date: 2019-03-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Voice Conversion

Singing voice conversion is a task to convert a song sang by a source singer
to the voice of a target singer. In this paper, we propose using a parallel
data free, many-to-one voice conversion technique on singing voices. A phonetic
posterior feature is first generated by decoding singing voices through a
robust Automatic Speech Recognition Engine (ASR). Then, a trained Recurrent
Neural Network (RNN) with a Deep Bidirectional Long Short Term Memory (DBLSTM)
structure is used to model the mapping from person-independent content to the
acoustic features of the target person. F0 and aperiodic are obtained through
the original singing voice, and used with acoustic features to reconstruct the
target singing voice through a vocoder. In the obtained singing voice, the
targeted and sourced singers sound similar. To our knowledge, this is the first
study that uses non parallel data to train a singing voice conversion system.
Subjective evaluations demonstrate that the proposed method effectively
converts singing voices.
Hybrid Reinforcement Learning with Expert State Sequences
Authors: Xiaoxiao Guo, Shiyu Chang, Mo Yu, Gerald Tesauro, Murray Campbell
Date: 2019-03-11 00:00:00
Tasks: Atari Games, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Existing imitation learning approaches often require that the complete
demonstration data, including sequences of actions and states, are available.
In this paper, we consider a more realistic and difficult scenario where a
reinforcement learning agent only has access to the state sequences of an
expert, while the expert actions are unobserved. We propose a novel
tensor-based model to infer the unobserved actions of the expert state
sequences. The policy of the agent is then optimized via a hybrid objective
combining reinforcement learning and imitation learning. We evaluated our
hybrid approach on an illustrative domain and Atari games. The empirical
results show that (1) the agents are able to leverage state expert sequences to
learn faster than pure reinforcement learning baselines, (2) our tensor-based
action inference model is advantageous compared to standard deep neural
networks in inferring expert actions, and (3) the hybrid policy optimization
objective is robust against noise in expert state sequences.
Spatial-Aware Non-Local Attention for Fashion Landmark Detection
Authors: Yixin Li, Shengqin Tang, Yun Ye, Jinwen Ma
Date: 2019-03-11 00:00:00
Tasks: Fine-Grained Image Classification, image-classification, Image Classification

Fashion landmark detection is a challenging task even using the current deep
learning techniques, due to the large variation and non-rigid deformation of
clothes. In order to tackle these problems, we propose Spatial-Aware Non-Local
(SANL) block, an attentive module in deep neural network which can utilize
spatial information while capturing global dependency. Actually, the SANL block
is constructed from the non-local block in the residual manner which can learn
the spatial related representation by taking a spatial attention map from
Grad-CAM. We then establish our fashion landmark detection framework on feature
pyramid network, equipped with four SANL blocks in the backbone. It is
demonstrated by the experimental results on two large-scale fashion datasets
that our proposed fashion landmark detection approach with the SANL blocks
outperforms the current state-of-the-art methods considerably. Some
supplementary experiments on fine-grained image classification also show the
effectiveness of the proposed SANL block.
Blameworthiness in Multi-Agent Settings
Authors: Meir Friedenberg, Joseph Y. Halpern
Date: 2019-03-11 00:00:00

We provide a formal definition of blameworthiness in settings where multiple
agents can collaborate to avoid a negative outcome. We first provide a method
for ascribing blameworthiness to groups relative to an epistemic state (a
distribution over causal models that describe how the outcome might arise). We
then show how we can go from an ascription of blameworthiness for groups to an
ascription of blameworthiness for individuals using a standard notion from
cooperative game theory, the Shapley value. We believe that getting a good
notion of blameworthiness in a group setting will be critical for designing
autonomous agents that behave in a moral manner.
Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games
Authors: Chun Kai Ling, Fei Fang, J. Zico Kolter
Date: 2019-03-11 00:00:00

With the recent advances in solving large, zero-sum extensive form games,
there is a growing interest in the inverse problem of inferring underlying game
parameters given only access to agent actions. Although a recent work provides
a powerful differentiable end-to-end learning frameworks which embed a game
solver within a deep-learning framework, allowing unknown game parameters to be
learned via backpropagation, this framework faces significant limitations when
applied to boundedly rational human agents and large scale problems, leading to
poor practicality. In this paper, we address these limitations and propose a
framework that is applicable for more practical settings. First, seeking to
learn the rationality of human agents in complex two-player zero-sum games, we
draw upon well-known ideas in decision theory to obtain a concise and
interpretable agent behavior model, and derive solvers and gradients for
end-to-end learning. Second, to scale up to large, real-world scenarios, we
propose an efficient first-order primal-dual method which exploits the
structure of extensive-form games, yielding significantly faster computation
for both game solving and gradient computation. When tested on randomly
generated games, we report speedups of orders of magnitude over previous
approaches. We also demonstrate the effectiveness of our model on both
real-world one-player settings and synthetic data.
Conformal Symplectic and Relativistic Optimization
Authors: Guilherme França, Jeremias Sulam, Daniel P. Robinson, René Vidal
Date: 2019-03-11 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Friction

Arguably, the two most popular accelerated or momentum-based optimization methods in machine learning are Nesterov's accelerated gradient and Polyaks's heavy ball, both corresponding to different discretizations of a particular second order differential equation with friction. Such connections with continuous-time dynamical systems have been instrumental in demystifying acceleration phenomena in optimization. Here we study structure-preserving discretizations for a certain class of dissipative (conformal) Hamiltonian systems, allowing us to analyze the symplectic structure of both Nesterov and heavy ball, besides providing several new insights into these methods. Moreover, we propose a new algorithm based on a dissipative relativistic system that normalizes the momentum and may result in more stable/faster optimization. Importantly, such a method generalizes both Nesterov and heavy ball, each being recovered as distinct limiting cases, and has potential advantages at no additional cost.
MTRNet: A Generic Scene Text Eraser
Authors: Osman Tursun, Rui Zeng, Simon Denman, Sabesan Sivapalan, Sridha Sridharan, Clinton Fookes
Date: 2019-03-11 00:00:00
Tasks: Curved Text Detection, Text Detection

Text removal algorithms have been proposed for uni-lingual scripts with regular shapes and layouts. However, to the best of our knowledge, a generic text removal method which is able to remove all or user-specified text regions regardless of font, script, language or shape is not available. Developing such a generic text eraser for real scenes is a challenging task, since it inherits all the challenges of multi-lingual and curved text detection and inpainting. To fill this gap, we propose a mask-based text removal network (MTRNet). MTRNet is a conditional adversarial generative network (cGAN) with an auxiliary mask. The introduced auxiliary mask not only makes the cGAN a generic text eraser, but also enables stable training and early convergence on a challenging large-scale synthetic dataset, initially proposed for text detection in real scenes. What's more, MTRNet achieves state-of-the-art results on several real-world datasets including ICDAR 2013, ICDAR 2017 MLT, and CTW1500, without being explicitly trained on this data, outperforming previous state-of-the-art methods trained directly on these datasets.
A Hybrid Framework for Action Recognition in Low-Quality Video Sequences
Authors: Tej Singh, Dinesh Kumar Vishwakarma
Date: 2019-03-11 00:00:00
Tasks: Action Recognition, Activity Recognition, Human Activity Recognition, Temporal Action Localization

Vision-based activity recognition is essential for security, monitoring and
surveillance applications. Further, real-time analysis having low-quality video
and contain less information about surrounding due to poor illumination, and
occlusions. Therefore, it needs a more robust and integrated model for low
quality and night security operations. In this context, we proposed a hybrid
model for illumination invariant human activity recognition based on sub-image
histogram equalization enhancement and k-key pose human silhouettes. This
feature vector gives good average recognition accuracy on three low exposure
video sequences subset of original actions video datasets. Finally, the
performance of the proposed approach is tested over three manually downgraded
low qualities Weizmann action, KTH, and Ballet Movement dataset. This model
outperformed on low exposure videos over existing technique and achieved
comparable classification accuracy to similar state-of-the-art methods.
Exploring OpenStreetMap Availability for Driving Environment Understanding
Authors: Yang Zheng, Izzat H. Izzat, John H. L. Hansen
Date: 2019-03-11 00:00:00
Tasks: Autonomous Driving, Semantic Segmentation

With the great achievement of artificial intelligence, vehicle technologies
have advanced significantly from human centric driving towards fully automated
driving. An intelligent vehicle should be able to understand the driver's
perception of the environment as well as controlling behavior of the vehicle.
Since high digital map information has been available to provide rich
environmental context about static roads, buildings and traffic
infrastructures, it would be worthwhile to explore map data capability for
driving task understanding. Alternative to commercial used maps, the
OpenStreetMap (OSM) data is a free open dataset, which makes it unique for the
exploration research. This study is focused on two tasks that leverage OSM for
driving environment understanding. First, driving scenario attributes are
retrieved from OSM elements, which are combined with vehicle dynamic signals
for the driving event recognition. Utilizing steering angle changes and based
on a Bi-directional Recurrent Neural Network (Bi-RNN), a driving sequence is
segmented and classified as lane-keeping, lane-change-left, lane-change-right,
turn-left, and turn-right events. Second, for autonomous driving perception,
OSM data can be used to render virtual street views, represented as prior
knowledge to fuse with vision/laser systems for road semantic segmentation.
Five different types of road masks are generated from OSM, images, and Lidar
points, and fused to characterize the drivable space at the driver's
perspective. An alternative data-driven approach is based on a Fully
Convolutional Network (FCN), OSM availability for deep learning methods are
discussed to reveal potential usage on compensating street view images and
automatic road semantic annotation.
Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Authors: Joeri Hermans, Volodimir Begy, Gilles Louppe
Date: 2019-03-10 00:00:00
Proceeding: ICML 2020 1

Posterior inference with an intractable likelihood is becoming an increasingly common task in scientific domains which rely on sophisticated computer simulations. Typically, these forward models do not admit tractable densities forcing practitioners to make use of approximations. This work introduces a novel approach to address the intractability of the likelihood and the marginal model. We achieve this by learning a flexible amortized estimator which approximates the likelihood-to-evidence ratio. We demonstrate that the learned ratio estimator can be embedded in MCMC samplers to approximate likelihood-ratios between consecutive states in the Markov chain, allowing us to draw samples from the intractable posterior. Techniques are presented to improve the numerical stability and to measure the quality of an approximation. The accuracy of our approach is demonstrated on a variety of benchmarks against well-established techniques. Scientific applications in physics show its applicability.
One-Pass Sparsified Gaussian Mixtures
Authors: Eric Kightley, Stephen Becker
Date: 2019-03-10 00:00:00
Tasks: Clustering

We present a one-pass sparsified Gaussian mixture model (SGMM). Given $N$ data points in $P$ dimensions, $X$, the model fits $K$ Gaussian distributions to $X$ and (softly) classifies each point to these clusters. After paying an up-front cost of $\mathcal{O}(NP\log P)$ to precondition the data, we subsample $Q$ entries of each data point and discard the full $P$-dimensional data. SGMM operates in $\mathcal{O}(KNQ)$ time per iteration for diagonal or spherical covariances, independent of $P$, while estimating the model parameters in the full $P$-dimensional space, making it one-pass and hence suitable for streaming data. We derive the maximum likelihood estimators for the parameters in the sparsified regime, demonstrate clustering on synthetic and real data, and show that SGMM is faster than GMM while preserving accuracy.
Affordance Learning for End-to-End Visuomotor Robot Control
Authors: Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh, Ville Kyrki
Date: 2019-03-10 00:00:00
Tasks: Dataset Generation

Training end-to-end deep robot policies requires a lot of domain-, task-, and
hardware-specific data, which is often costly to provide. In this work, we
propose to tackle this issue by employing a deep neural network with a modular
architecture, consisting of separate perception, policy, and trajectory parts.
Each part of the system is trained fully on synthetic data or in simulation.
The data is exchanged between parts of the system as low-dimensional latent
representations of affordances and trajectories. The performance is then
evaluated in a zero-shot transfer scenario using Franka Panda robot arm.
Results demonstrate that a low-dimensional representation of scene affordances
extracted from an RGB image is sufficient to successfully train manipulator
policies. We also introduce a method for affordance dataset generation, which
is easily generalizable to new tasks, objects and environments, and requires no
manual pixel labeling.