pwc-archive/papers-with-abstracts huggingface dataset:
Theoretical Foundations of Defeasible Description Logics
Authors: Katarina Britz, Giovanni Casini, Thomas Meyer, Kody Moodley, Uli Sattler, Ivan Varzinczak
Date: 2019-04-16 00:00:00

We extend description logics (DLs) with non-monotonic reasoning features. We
start by investigating a notion of defeasible subsumption in the spirit of
defeasible conditionals as studied by Kraus, Lehmann and Magidor in the
propositional case. In particular, we consider a natural and intuitive
semantics for defeasible subsumption, and investigate KLM-style syntactic
properties for both preferential and rational subsumption. Our contribution
includes two representation results linking our semantic constructions to the
set of preferential and rational properties considered. Besides showing that
our semantics is appropriate, these results pave the way for more effective
decision procedures for defeasible reasoning in DLs. Indeed, we also analyse
the problem of non-monotonic reasoning in DLs at the level of entailment and
present an algorithm for the computation of rational closure of a defeasible
ontology. Importantly, our algorithm relies completely on classical entailment
and shows that the computational complexity of reasoning over defeasible
ontologies is no worse than that of reasoning in the underlying classical DL
ALC.
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
Authors: Ryan Eloff, André Nortje, Benjamin van Niekerk, Avashna Govender, Leanne Nortje, Arnu Pretorius, Elan van Biljon, Ewald van der Westhuizen, Lisa van Staden, Herman Kamper
Date: 2019-04-16 00:00:00
Tasks: Acoustic Unit Discovery, Decoder, Speech Synthesis

For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis. Unsupervised discrete subword modelling could be useful for studies of phonetic category learning in infants or in low-resource speech technology requiring symbolic input. We use an autoencoder (AE) architecture with intermediate discretisation. We decouple acoustic unit discovery from speaker modelling by conditioning the AE's decoder on the training speaker identity. At test time, unit discovery is performed on speech from an unseen speaker, followed by unit decoding conditioned on a known target speaker to obtain reconstructed filterbanks. This output is fed to a neural vocoder to synthesise speech in the target speaker's voice. For discretisation, categorical variational autoencoders (CatVAEs), vector-quantised VAEs (VQ-VAEs) and straight-through estimation are compared at different compression levels on two languages. Our final model uses convolutional encoding, VQ-VAE discretisation, deconvolutional decoding and an FFTNet vocoder. We show that decoupled speaker conditioning intrinsically improves discrete acoustic representations, yielding competitive synthesis quality compared to the challenge baseline.
Long-Term Human Video Generation of Multiple Futures Using Poses
Authors: Naoya Fushishita, Antonio Tejero-de-Pablos, Yusuke Mukuta, Tatsuya Harada
Date: 2019-04-16 00:00:00
Tasks: Autonomous Driving, Pose Prediction, Video Generation, Video Prediction

Predicting future human behavior from an input human video is a useful task for applications such as autonomous driving and robotics. While most previous works predict a single future, multiple futures with different behavior can potentially occur. Moreover, if the predicted future is too short (e.g., less than one second), it may not be fully usable by a human or other systems. In this paper, we propose a novel method for future human pose prediction capable of predicting multiple long-term futures. This makes the predictions more suitable for real applications. Also, from the input video and the predicted human behavior, we generate future videos. First, from an input human video, we generate sequences of future human poses (i.e., the image coordinates of their body-joints) via adversarial learning. Adversarial learning suffers from mode collapse, which makes it difficult to generate a variety of multiple poses. We solve this problem by utilizing two additional inputs to the generator to make the outputs diverse, namely, a latent code (to reflect various behaviors) and an attraction point (to reflect various trajectories). In addition, we generate long-term future human poses using a novel approach based on unidimensional convolutional neural networks. Last, we generate an output video based on the generated poses for visualization. We evaluate the generated future poses and videos using three criteria (i.e., realism, diversity and accuracy), and show that our proposed method outperforms other state-of-the-art works.
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Authors: Martin Simon, Karl Amende, Andrea Kraus, Jens Honer, Timo Sämann, Hauke Kaulbersch, Stefan Milz, Horst Michael Gross
Date: 2019-04-16 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Object, object-detection, Object Detection, Semantic Segmentation, Translation

Accurate detection of 3D objects is a fundamental problem in computer vision
and has an enormous impact on autonomous cars, augmented/virtual reality and
many applications in robotics. In this work we present a novel fusion of neural
network based state-of-the-art 3D detector and visual semantic segmentation in
the context of autonomous driving. Additionally, we introduce
Scale-Rotation-Translation score (SRTs), a fast and highly parameterizable
evaluation metric for comparison of object detections, which speeds up our
inference time up to 20\% and halves training time. On top, we apply
state-of-the-art online multi target feature tracking on the object
measurements to further increase accuracy and robustness utilizing temporal
information. Our experiments on KITTI show that we achieve same results as
state-of-the-art in all related categories, while maintaining the performance
and accuracy trade-off and still run in real-time. Furthermore, our model is
the first one that fuses visual semantic with 3D object detection.
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Authors: Shun Zheng, Wei Cao, Wei Xu, Jiang Bian
Date: 2019-04-16 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Document-level Event Extraction, Event Extraction, Sentence

Most existing event extraction (EE) methods merely extract event arguments within the sentence scope. However, such sentence-level EE methods struggle to handle soaring amounts of documents from emerging applications, such as finance, legislation, health, etc., where event arguments always scatter across different sentences, and even multiple such event mentions frequently co-exist in the same document. To address these challenges, we propose a novel end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic graph to fulfill the document-level EE (DEE) effectively. Moreover, we reformalize a DEE task with the no-trigger-words design to ease the document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we build a large-scale real-world dataset consisting of Chinese financial announcements with the challenges mentioned above. Extensive experiments with comprehensive analyses illustrate the superiority of Doc2EDAG over state-of-the-art methods. Data and codes can be found at https://github.com/dolphin-zs/Doc2EDAG.
Understanding the Behaviors of BERT in Ranking
Authors: Yifan Qiao, Chenyan Xiong, Zheng-Hao Liu, Zhiyuan Liu
Date: 2019-04-16 00:00:00
Tasks: Document Ranking, Passage Ranking, Passage Reranking, Question Answering, Reranking

This paper studies the performances and behaviors of BERT in ranking tasks.
We explore several different ways to leverage the pre-trained BERT and
fine-tune it on two ranking tasks: MS MARCO passage reranking and TREC Web
Track ad hoc document ranking. Experimental results on MS MARCO demonstrate the
strong effectiveness of BERT in question-answering focused passage ranking
tasks, as well as the fact that BERT is a strong interaction-based seq2seq
matching model. Experimental results on TREC show the gaps between the BERT
pre-trained on surrounding contexts and the needs of ad hoc document ranking.
Analyses illustrate how BERT allocates its attentions between query-document
tokens in its Transformer layers, how it prefers semantic matches between
paraphrase tokens, and how that differs with the soft match patterns learned by
a click-trained neural ranker.
Disentangling Pose from Appearance in Monochrome Hand Images
Authors: Yikang Li, Chris Twigg, Yuting Ye, Lingling Tao, Xiaogang Wang
Date: 2019-04-16 00:00:00
Tasks: 2D Pose Estimation, Disentanglement, Hand Pose Estimation, Pose Estimation

Hand pose estimation from the monocular 2D image is challenging due to the
variation in lighting, appearance, and background. While some success has been
achieved using deep neural networks, they typically require collecting a large
dataset that adequately samples all the axes of variation of hand images. It
would, therefore, be useful to find a representation of hand pose which is
independent of the image appearance~(like hand texture, lighting, background),
so that we can synthesize unseen images by mixing pose-appearance combinations.
In this paper, we present a novel technique that disentangles the
representation of pose from a complementary appearance factor in 2D monochrome
images. We supervise this disentanglement process using a network that learns
to generate images of hand using specified pose+appearance features. Unlike
previous work, we do not require image pairs with a matching pose; instead, we
use the pose annotations already available and introduce a novel use of cycle
consistency to ensure orthogonality between the factors. Experimental results
show that our self-disentanglement scheme successfully decomposes the hand
image into the pose and its complementary appearance features of comparable
quality as the method using paired data. Additionally, training the model with
extra synthesized images with unseen hand-appearance combinations by re-mixing
pose and appearance factors from different images can improve the 2D pose
estimation performance.
A Deep Journey into Super-resolution: A survey
Authors: Saeed Anwar, Salman Khan, Nick Barnes
Date: 2019-04-16 00:00:00
Tasks: Image Super-Resolution, Super-Resolution, Survey

Deep convolutional networks based super-resolution is a fast-growing field with numerous practical applications. In this exposition, we extensively compare 30+ state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution. We introduce a taxonomy for deep-learning based super-resolution networks that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based and adversarial designs. We also provide comparisons between the models in terms of network complexity, memory footprint, model input and output, learning details, the type of network losses and important architectural differences (e.g., depth, skip-connections, filters). The extensive evaluation performed, shows the consistent and rapid growth in the accuracy in the past few years along with a corresponding boost in model complexity and the availability of large-scale datasets. It is also observed that the pioneering methods identified as the benchmark have been significantly outperformed by the current contenders. Despite the progress in recent years, we identify several shortcomings of existing techniques and provide future research directions towards the solution of these open problems.
A Deep Optimization Approach for Image Deconvolution
Authors: Zhijian Luo, Siyu Chen, Yuntao Qian
Date: 2019-04-16 00:00:00
Tasks: Image Deconvolution

In blind image deconvolution, priors are often leveraged to constrain the
solution space, so as to alleviate the under-determinacy. Priors which are
trained separately from the task of deconvolution tend to be instable, or
ineffective. We propose the Golf Optimizer, a novel but simple form of network
that learns deep priors from data with better propagation behavior. Like
playing golf, our method first estimates an aggressive propagation towards
optimum using one network, and recurrently applies a residual CNN to learn the
gradient of prior for delicate correction on restoration. Experiments show that
our network achieves competitive performance on GoPro dataset, and our model is
extremely lightweight compared with the state-of-art works.
Reinforcement Learning for Nested Polar Code Construction
Authors: Lingchen Huang, Huazi Zhang, Rong Li, Yiqun Ge, Jun Wang
Date: 2019-04-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques. First, an MDP environment with state, action, and reward is defined in the context of polar coding. Specifically, a state represents the construction of an $(N,K)$ polar code, an action specifies its reduction to an $(N,K-1)$ subcode, and reward is the decoding performance. A neural network architecture consisting of both policy and value networks is proposed to generate actions based on the observed states, aiming at maximizing the overall rewards. A loss function is defined to trade off between exploitation and exploration. To further improve learning efficiency and quality, an `integrated learning' paradigm is proposed. It first employs a genetic algorithm to generate a population of (sub-)optimal polar codes for each $(N,K)$, and then uses them as prior knowledge to refine the policy in RL. Such a paradigm is shown to accelerate the training process, and converge at better performances. Simulation results show that the proposed learning-based polar constructions achieve comparable, or even better, performances than the state of the art under successive cancellation list (SCL) decoders. Last but not least, this is achieved without exploiting any expert knowledge from polar coding theory in the learning algorithms.
RES-PCA: A Scalable Approach to Recovering Low-rank Matrices
Authors: Chong Peng, Chenglizhao Chen, Zhao Kang, Jianbo Li, Qiang Cheng
Date: 2019-04-16 00:00:00
Proceeding: CVPR 2019 6

Robust principal component analysis (RPCA) has drawn significant attentions
due to its powerful capability in recovering low-rank matrices as well as
successful appplications in various real world problems. The current
state-of-the-art algorithms usually need to solve singular value decomposition
of large matrices, which generally has at least a quadratic or even cubic
complexity. This drawback has limited the application of RPCA in solving real
world problems. To combat this drawback, in this paper we propose a new type of
RPCA method, RES-PCA, which is linearly efficient and scalable in both data
size and dimension. For comparison purpose, AltProj, an existing scalable
approach to RPCA requires the precise knowlwdge of the true rank; otherwise, it
may fail to recover low-rank matrices. By contrast, our method works with or
without knowing the true rank; even when both methods work, our method is
faster. Extensive experiments have been performed and testified to the
effectiveness of proposed method quantitatively and in visual quality, which
suggests that our method is suitable to be employed as a light-weight, scalable
component for RPCA in any application pipelines.
Discriminative Ridge Machine: A Classifier for High-Dimensional Data or Imbalanced Data
Authors: Chong Peng, Qiang Cheng
Date: 2019-04-16 00:00:00
Tasks: regression

We introduce a discriminative regression approach to supervised classification in this paper. It estimates a representation model while accounting for discriminativeness between classes, thereby enabling accurate derivation of categorical information. This new type of regression models extends existing models such as ridge, lasso, and group lasso through explicitly incorporating discriminative information. As a special case we focus on a quadratic model that admits a closed-form analytical solution. The corresponding classifier is called discriminative regression machine (DRM). Three iterative algorithms are further established for the DRM to enhance the efficiency and scalability for real applications. Our approach and the algorithms are applicable to general types of data including images, high-dimensional data, and imbalanced data. We compare the DRM with currently state-of-the-art classifiers. Our extensive experimental results show superior performance of the DRM and confirm the effectiveness of the proposed approach.
Method for Constructing Artificial Intelligence Player with Abstraction to Markov Decision Processes in Multiplayer Game of Mahjong
Authors: Moyuru Kurita, Kunihito Hoki
Date: 2019-04-16 00:00:00

We propose a method for constructing artificial intelligence (AI) of mahjong,
which is a multiplayer imperfect information game. Since the size of the game
tree is huge, constructing an expert-level AI player of mahjong is challenging.
We define multiple Markov decision processes (MDPs) as abstractions of mahjong
to construct effective search trees. We also introduce two methods of inferring
state values of the original mahjong using these MDPs. We evaluated the
effectiveness of our method using gameplays vis-\`{a}-vis the current strongest
AI player.
Shared Predictive Cross-Modal Deep Quantization
Authors: Erkun Yang, Cheng Deng, Chao Li, Wei Liu, Jie Li, DaCheng Tao
Date: 2019-04-16 00:00:00
Tasks: Quantization

With explosive growth of data volume and ever-increasing diversity of data
modalities, cross-modal similarity search, which conducts nearest neighbor
search across different modalities, has been attracting increasing interest.
This paper presents a deep compact code learning solution for efficient
cross-modal similarity search. Many recent studies have proven that
quantization-based approaches perform generally better than hashing-based
approaches on single-modal similarity search. In this paper, we propose a deep
quantization approach, which is among the early attempts of leveraging deep
neural networks into quantization-based cross-modal similarity search. Our
approach, dubbed shared predictive deep quantization (SPDQ), explicitly
formulates a shared subspace across different modalities and two private
subspaces for individual modalities, and representations in the shared subspace
and the private subspaces are learned simultaneously by embedding them to a
reproducing kernel Hilbert space, where the mean embedding of different
modality distributions can be explicitly compared. In addition, in the shared
subspace, a quantizer is learned to produce the semantics preserving compact
codes with the help of label alignment. Thanks to this novel network
architecture in cooperation with supervised quantization training, SPDQ can
preserve intramodal and intermodal similarities as much as possible and greatly
reduce quantization error. Experiments on two popular benchmarks corroborate
that our approach outperforms state-of-the-art methods.
End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional Networks
Authors: Di Zhao, Lan Ma, Songnan Li, Dahai Yu
Date: 2019-04-16 00:00:00
Tasks: Denoising, Image Denoising

When taking photos in dim-light environments, due to the small amount of
light entering, the shot images are usually extremely dark, with a great deal
of noise, and the color cannot reflect real-world color. Under this condition,
the traditional methods used for single image denoising have always failed to
be effective. One common idea is to take multiple frames of the same scene to
enhance the signal-to-noise ratio. This paper proposes a recurrent fully
convolutional network (RFCN) to process burst photos taken under extremely
low-light conditions, and to obtain denoised images with improved brightness.
Our model maps raw burst images directly to sRGB outputs, either to produce a
best image or to generate a multi-frame denoised image sequence. This process
has proven to be capable of accomplishing the low-level task of denoising, as
well as the high-level task of color correction and enhancement, all of which
is end-to-end processing through our network. Our method has achieved better
results than state-of-the-art methods. In addition, we have applied the model
trained by one type of camera without fine-tuning on photos captured by
different cameras and have obtained similar end-to-end enhancements.
Object-Oriented Dynamics Learning through Multi-Level Abstraction
Authors: Guangxiang Zhu, Jianhao Wang, Zhizhou Ren, Zichuan Lin, Chongjie Zhang
Date: 2019-04-16 00:00:00
Tasks: Object, Relational Reasoning, Self-Supervised Learning

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.
GradMask: Reduce Overfitting by Regularizing Saliency
Authors: Becks Simpson, Francis Dutil, Yoshua Bengio, Joseph Paul Cohen
Date: 2019-04-16 00:00:00
Tasks: Lesion Segmentation

With too few samples or too many model parameters, overfitting can inhibit
the ability to generalise predictions to new data. Within medical imaging, this
can occur when features are incorrectly assigned importance such as distinct
hospital specific artifacts, leading to poor performance on a new dataset from
a different institution without those features, which is undesirable. Most
regularization methods do not explicitly penalize the incorrect association of
these features to the target class and hence fail to address this issue. We
propose a regularization method, GradMask, which penalizes saliency maps
inferred from the classifier gradients when they are not consistent with the
lesion segmentation. This prevents non-tumor related features to contribute to
the classification of unhealthy samples. We demonstrate that this method can
improve test accuracy between 1-3% compared to the baseline without GradMask,
showing that it has an impact on reducing overfitting.