pwc-archive/papers-with-abstracts huggingface dataset:
Faithful Multimodal Explanation for Visual Question Answering
Authors: Jialin Wu, Raymond J. Mooney
Date: 2018-09-08 00:00:00
Proceeding: WS 2019 8
Tasks: Explanatory Visual Question Answering, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

AI systems' ability to explain their reasoning is critical to their utility and trustworthiness. Deep neural networks have enabled significant progress on many challenging problems such as visual question answering (VQA). However, most of them are opaque black boxes with limited explanatory capability. This paper presents a novel approach to developing a high-performing VQA system that can elucidate its answers with integrated textual and visual explanations that faithfully reflect important aspects of its underlying reasoning while capturing the style of comprehensible human explanations. Extensive experimental evaluation demonstrates the advantages of this approach compared to competing methods with both automatic evaluation metrics and human evaluation metrics.
Handling Concept Drift via Model Reuse
Authors: Peng Zhao, Le-Wen Cai, Zhi-Hua Zhou
Date: 2018-09-08 00:00:00
Tasks: model

In many real-world applications, data are often collected in the form of
stream, and thus the distribution usually changes in nature, which is referred
as concept drift in literature. We propose a novel and effective approach to
handle concept drift via model reuse, leveraging previous knowledge by reusing
models. Each model is associated with a weight representing its reusability
towards current data, and the weight is adaptively adjusted according to the
model performance. We provide generalization and regret analysis. Experimental
results also validate the superiority of our approach on both synthetic and
real-world datasets.
Video Smoke Detection Based on Deep Saliency Network
Authors: Gao Xu, Yongming Zhang, Qixing Zhang, Gaohua Lin, Zhong Wang, Yang Jia, Jinjun Wang
Date: 2018-09-08 00:00:00
Tasks: Fire Detection, Saliency Detection

Video smoke detection is a promising fire detection method especially in open
or large spaces and outdoor environments. Traditional video smoke detection
methods usually consist of candidate region extraction and classification, but
lack powerful characterization for smoke. In this paper, we propose a novel
video smoke detection method based on deep saliency network. Visual saliency
detection aims to highlight the most important object regions in an image. The
pixel-level and object-level salient convolutional neural networks are combined
to extract the informative smoke saliency map. An end-to-end framework for
salient smoke detection and existence prediction of smoke is proposed for
application in video smoke detection. The deep feature map is combined with the
saliency map to predict the existence of smoke in an image. Initial and
augmented dataset are built to measure the performance of frameworks with
different design strategies. Qualitative and quantitative analysis at
frame-level and pixel-level demonstrate the excellent performance of the
ultimate framework.
Attentive Semantic Role Labeling with Boundary Indicator
Authors: Zhuosheng Zhang, Shexia He, Zuchao Li, Hai Zhao
Date: 2018-09-08 00:00:00
Tasks: Semantic Role Labeling, Sentence

The goal of semantic role labeling (SRL) is to discover the
predicate-argument structure of a sentence, which plays a critical role in deep
processing of natural language. This paper introduces simple yet effective
auxiliary tags for dependency-based SRL to enhance a syntax-agnostic model with
multi-hop self-attention. Our syntax-agnostic model achieves competitive
performance with state-of-the-art models on the CoNLL-2009 benchmarks both for
English and Chinese.
Explicit Contextual Semantics for Text Comprehension
Authors: Zhuosheng Zhang, Yuwei Wu, Zuchao Li, Hai Zhao
Date: 2018-09-08 00:00:00
Tasks: Machine Reading Comprehension, Natural Language Understanding, Reading Comprehension, Semantic Role Labeling

Who did what to whom is a major focus in natural language understanding, which is right the aim of semantic role labeling (SRL) task. Despite of sharing a lot of processing characteristics and even task purpose, it is surprisingly that jointly considering these two related tasks was never formally reported in previous work. Thus this paper makes the first attempt to let SRL enhance text comprehension and inference through specifying verbal predicates and their corresponding semantic roles. In terms of deep learning models, our embeddings are enhanced by explicit contextual semantic role labels for more fine-grained semantics. We show that the salient labels can be conveniently added to existing models and significantly improve deep learning models in challenging text comprehension tasks. Extensive experiments on benchmark machine reading comprehension and inference datasets verify that the proposed semantic learning helps our system reach new state-of-the-art over strong baselines which have been enhanced by well pretrained language models from the latest progress.
Adversarial Learning for Image Forensics Deep Matching with Atrous Convolution
Authors: Yaqi Liu, Xianfeng Zhao, Xiaobin Zhu, Yun Cao
Date: 2018-09-08 00:00:00
Tasks: Image Forensics

Constrained image splicing detection and localization (CISDL) is a newly
proposed challenging task for image forensics, which investigates two input
suspected images and identifies whether one image has suspected regions pasted
from the other. In this paper, we propose a novel adversarial learning
framework to train the deep matching network for CISDL. Our framework mainly
consists of three building blocks: 1) the deep matching network based on atrous
convolution (DMAC) aims to generate two high-quality candidate masks which
indicate the suspected regions of the two input images, 2) the detection
network is designed to rectify inconsistencies between the two corresponding
candidate masks, 3) the discriminative network drives the DMAC network to
produce masks that are hard to distinguish from ground-truth ones. In DMAC,
atrous convolution is adopted to extract features with rich spatial
information, the correlation layer based on the skip architecture is proposed
to capture hierarchical features, and atrous spatial pyramid pooling is
constructed to localize tampered regions at multiple scales. The detection
network and the discriminative network act as the losses with auxiliary
parameters to supervise the training of DMAC in an adversarial way. Extensive
experiments, conducted on 21 generated testing sets and two public datasets,
demonstrate the effectiveness of the proposed framework and the superior
performance of DMAC.
The Lower The Simpler: Simplifying Hierarchical Recurrent Models
Authors: Chao Wang, Hui Jiang
Date: 2018-09-08 00:00:00
Proceeding: NAACL 2019 6
Tasks: Decoder

To improve the training efficiency of hierarchical recurrent models without compromising their performance, we propose a strategy named as `the lower the simpler', which is to simplify the baseline models by making the lower layers simpler than the upper layers. We carry out this strategy to simplify two typical hierarchical recurrent models, namely Hierarchical Recurrent Encoder-Decoder (HRED) and R-NET, whose basic building block is GRU. Specifically, we propose Scalar Gated Unit (SGU), which is a simplified variant of GRU, and use it to replace the GRUs at the middle layers of HRED and R-NET. Besides, we also use Fixed-size Ordinally-Forgetting Encoding (FOFE), which is an efficient encoding method without any trainable parameter, to replace the GRUs at the bottom layers of HRED and R-NET. The experimental results show that the simplified HRED and the simplified R-NET contain significantly less trainable parameters, consume significantly less training time, and achieve slightly better performance than their baseline models.
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
Authors: Todor Mihaylov, Peter Clark, Tushar Khot, Ashish Sabharwal
Date: 2018-09-08 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Retrieval

We present a new kind of question answering dataset, OpenBookQA, modeled
after open book exams for assessing human understanding of a subject. The open
book that comes with our questions is a set of 1329 elementary level science
facts. Roughly 6000 questions probe an understanding of these facts and their
application to novel situations. This requires combining an open book fact
(e.g., metals conduct electricity) with broad common knowledge (e.g., a suit of
armor is made of metal) obtained from other sources. While existing QA datasets
over documents or knowledge bases, being generally self-contained, focus on
linguistic understanding, OpenBookQA probes a deeper understanding of both the
topic---in the context of common knowledge---and the language it is expressed
in. Human performance on OpenBookQA is close to 92%, but many state-of-the-art
pre-trained QA methods perform surprisingly poorly, worse than several simple
neural baselines we develop. Our oracle experiments designed to circumvent the
knowledge retrieval bottleneck demonstrate the value of both the open book and
additional facts. We leave it as a challenge to solve the retrieval problem in
this multi-hop setting and to close the large gap to human performance.
Structure-Preserving Transformation: Generating Diverse and Transferable Adversarial Examples
Authors: Dan Peng, Zizhan Zheng, Xiaofeng Zhang
Date: 2018-09-08 00:00:00
Tasks: image-classification, Image Classification

Adversarial examples are perturbed inputs designed to fool machine learning
models. Most recent works on adversarial examples for image classification
focus on directly modifying pixels with minor perturbations. A common
requirement in all these works is that the malicious perturbations should be
small enough (measured by an L_p norm for some p) so that they are
imperceptible to humans. However, small perturbations can be unnecessarily
restrictive and limit the diversity of adversarial examples generated. Further,
an L_p norm based distance metric ignores important structure patterns hidden
in images that are important to human perception. Consequently, even the minor
perturbation introduced in recent works often makes the adversarial examples
less natural to humans. More importantly, they often do not transfer well and
are therefore less effective when attacking black-box models especially for
those protected by a defense mechanism. In this paper, we propose a
structure-preserving transformation (SPT) for generating natural and diverse
adversarial examples with extremely high transferability. The key idea of our
approach is to allow perceptible deviation in adversarial examples while
keeping structure patterns that are central to a human classifier. Empirical
results on the MNIST and the fashion-MNIST datasets show that adversarial
examples generated by our approach can easily bypass strong adversarial
training. Further, they transfer well to other target models with no loss or
little loss of successful attack rate.
Instance-based Deep Transfer Learning
Authors: Tianyang Wang, Jun Huan, Michelle Zhu
Date: 2018-09-08 00:00:00
Tasks: image-classification, Image Classification, Transfer Learning

Deep transfer learning recently has acquired significant research interest.
It makes use of pre-trained models that are learned from a source domain, and
utilizes these models for the tasks in a target domain. Model-based deep
transfer learning is probably the most frequently used method. However, very
little research work has been devoted to enhancing deep transfer learning by
focusing on the influence of data. In this paper, we propose an instance-based
approach to improve deep transfer learning in a target domain. Specifically, we
choose a pre-trained model from a source domain and apply this model to
estimate the influence of training samples in a target domain. Then we optimize
the training data of the target domain by removing the training samples that
will lower the performance of the pre-trained model. We later either fine-tune
the pre-trained model with the optimized training data in the target domain, or
build a new model which is initialized partially based on the pre-trained
model, and fine-tune it with the optimized training data in the target domain.
Using this approach, transfer learning can help deep learning models to capture
more useful features. Extensive experiments demonstrate the effectiveness of
our approach on boosting the quality of deep learning models for some common
computer vision tasks, such as image classification.
Generating Distractors for Reading Comprehension Questions from Real Examinations
Authors: Yifan Gao, Lidong Bing, Piji Li, Irwin King, Michael R. Lyu
Date: 2018-09-08 00:00:00
Tasks: Decoder, Distractor Generation, Multiple-choice, Reading Comprehension, Sentence

We investigate the task of distractor generation for multiple choice reading
comprehension questions from examinations. In contrast to all previous works,
we do not aim at preparing words or short phrases distractors, instead, we
endeavor to generate longer and semantic-rich distractors which are closer to
distractors in real reading comprehension from examinations. Taking a reading
comprehension article, a pair of question and its correct option as input, our
goal is to generate several distractors which are somehow related to the
answer, consistent with the semantic context of the question and have some
trace in the article. We propose a hierarchical encoder-decoder framework with
static and dynamic attention mechanisms to tackle this task. Specifically, the
dynamic attention can combine sentence-level and word-level attention varying
at each recurrent time step to generate a more readable sequence. The static
attention is to modulate the dynamic attention not to focus on question
irrelevant sentences or sentences which contribute to the correct option. Our
proposed framework outperforms several strong baselines on the first prepared
distractor generation dataset of real reading comprehension questions. For
human evaluation, compared with those distractors generated by baselines, our
generated distractors are more functional to confuse the annotators.
CNNs for Surveillance Footage Scene Classification
Authors: Utkarsh Contractor, Chinmayi Dixit, Deepti Mahajan
Date: 2018-09-08 00:00:00
Tasks: Classification, General Classification, Scene Classification

In this project, we adapt high-performing CNN architectures to differentiate
between scenes with and without abandoned luggage. Using frames from two video
datasets, we compare the results of training different architectures on each
dataset as well as on combining the datasets. We additionally use network
visualization techniques to gain insight into what the neural network sees, and
the basis of the classification decision. We intend that our results benefit
further work in applying CNNs in surveillance and security-related tasks.
Exploration on Grounded Word Embedding: Matching Words and Images with Image-Enhanced Skip-Gram Model
Authors: Ruixuan Luo
Date: 2018-09-08 00:00:00
Tasks: Learning Word Embeddings, Word Embeddings

Word embedding is designed to represent the semantic meaning of a word with
low dimensional vectors. The state-of-the-art methods of learning word
embeddings (word2vec and GloVe) only use the word co-occurrence information.
The learned embeddings are real number vectors, which are obscure to human. In
this paper, we propose an Image-Enhanced Skip-Gram Model to learn grounded word
embeddings by representing the word vectors in the same hyper-plane with image
vectors. Experiments show that the image vectors and word embeddings learned by
our model are highly correlated, which indicates that our model is able to
provide a vivid image-based explanation to the word embeddings.
Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning
Authors: Nguyen Van Huynh, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz, Dusit Niyato, Ping Wang
Date: 2018-09-08 00:00:00
Tasks: Blocking, Reinforcement Learning

Ambient backscatter has been introduced with a wide range of applications for
low power wireless communications. In this article, we propose an optimal and
low-complexity dynamic spectrum access framework for RF-powered ambient
backscatter system. In this system, the secondary transmitter not only harvests
energy from ambient signals (from incumbent users), but also backscatters these
signals to its receiver for data transmission. Under the dynamics of the
ambient signals, we first adopt the Markov decision process (MDP) framework to
obtain the optimal policy for the secondary transmitter, aiming to maximize the
system throughput. However, the MDP-based optimization requires complete
knowledge of environment parameters, e.g., the probability of a channel to be
idle and the probability of a successful packet transmission, that may not be
practical to obtain. To cope with such incomplete knowledge of the environment,
we develop a low-complexity online reinforcement learning algorithm that allows
the secondary transmitter to "learn" from its decisions and then attain the
optimal policy. Simulation results show that the proposed learning algorithm
not only efficiently deals with the dynamics of the environment, but also
improves the average throughput up to 50% and reduces the blocking probability
and delay up to 80% compared with conventional methods.
Molecular Hypergraph Grammar with its Application to Molecular Optimization
Authors: Hiroshi Kajino
Date: 2018-09-08 00:00:00
Tasks: Bayesian Optimization, valid

Molecular optimization aims to discover novel molecules with desirable
properties. Two fundamental challenges are: (i) it is not trivial to generate
valid molecules in a controllable way due to hard chemical constraints such as
the valency conditions, and (ii) it is often costly to evaluate a property of a
novel molecule, and therefore, the number of property evaluations is limited.
These challenges are to some extent alleviated by a combination of a
variational autoencoder (VAE) and Bayesian optimization (BO). VAE converts a
molecule into/from its latent continuous vector, and BO optimizes a latent
continuous vector (and its corresponding molecule) within a limited number of
property evaluations. While the most recent work, for the first time, achieved
100% validity, its architecture is rather complex due to auxiliary neural
networks other than VAE, making it difficult to train. This paper presents a
molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a
single VAE to achieve 100% validity. Our idea is to develop a graph grammar
encoding the hard chemical constraints, called molecular hypergraph grammar
(MHG), which guides VAE to always generate valid molecules. We also present an
algorithm to construct MHG from a set of molecules.
On the Calibration of Nested Dichotomies for Large Multiclass Tasks
Authors: Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes
Date: 2018-09-08 00:00:00
Tasks: Binary Classification, General Classification

Nested dichotomies are used as a method of transforming a multiclass
classification problem into a series of binary problems. A tree structure is
induced that recursively splits the set of classes into subsets, and a binary
classification model learns to discriminate between the two subsets of classes
at each node. In this paper, we demonstrate that these nested dichotomies
typically exhibit poor probability calibration, even when the base binary
models are well calibrated. We also show that this problem is exacerbated when
the binary models are poorly calibrated. We discuss the effectiveness of
different calibration strategies and show that accuracy and log-loss can be
significantly improved by calibrating both the internal base models and the
full nested dichotomy structure, especially when the number of classes is high.
RealPoint3D: Point Cloud Generation from a Single Image with Complex Background
Authors: Yan Xia, Yang Zhang, Dingfu Zhou, Xinyu Huang, Cheng Wang, Ruigang Yang
Date: 2018-09-08 00:00:00
Tasks: 3D Generation, Point Cloud Generation

3D point cloud generation by the deep neural network from a single image has
been attracting more and more researchers' attention. However,
recently-proposed methods require the objects be captured with relatively clean
backgrounds, fixed viewpoint, while this highly limits its application in the
real environment. To overcome these drawbacks, we proposed to integrate the
prior 3D shape knowledge into the network to guide the 3D generation. By taking
additional 3D information, the proposed network can handle the 3D object
generation from a single real image captured from any viewpoint and complex
background. Specifically, giving a query image, we retrieve the nearest shape
model from a pre-prepared 3D model database. Then, the image together with the
retrieved shape model is fed into the proposed network to generate the
fine-grained 3D point cloud. The effectiveness of our proposed framework has
been verified on different kinds of datasets. Experimental results show that
the proposed framework achieves state-of-the-art accuracy compared to other
volumetric-based and point set generation methods. Furthermore, the proposed
framework works well for real images in complex backgrounds with various view
angles.
Ensembles of Nested Dichotomies with Multiple Subset Evaluation
Authors: Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes
Date: 2018-09-08 00:00:00

A system of nested dichotomies is a method of decomposing a multi-class
problem into a collection of binary problems. Such a system recursively applies
binary splits to divide the set of classes into two subsets, and trains a
binary classifier for each split. Many methods have been proposed to perform
this split, each with various advantages and disadvantages. In this paper, we
present a simple, general method for improving the predictive performance of
nested dichotomies produced by any subset selection techniques that employ
randomness to construct the subsets. We provide a theoretical expectation for
performance improvements, as well as empirical results showing that our method
improves the root mean squared error of nested dichotomies, regardless of
whether they are employed as an individual model or in an ensemble setting.
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
Authors: David Minnen, Johannes Ballé, George Toderici
Date: 2018-09-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Image Compression, MS-SSIM, SSIM

Recent models for learned image compression are based on autoencoders,
learning approximately invertible mappings from pixels to a quantized latent
representation. These are combined with an entropy model, a prior on the latent
representation that can be used with standard arithmetic coding algorithms to
yield a compressed bitstream. Recently, hierarchical entropy models have been
introduced as a way to exploit more structure in the latents than simple fully
factorized priors, improving compression performance while maintaining
end-to-end optimization. Inspired by the success of autoregressive priors in
probabilistic generative models, we examine autoregressive, hierarchical, as
well as combined priors as alternatives, weighing their costs and benefits in
the context of image compression. While it is well known that autoregressive
models come with a significant computational penalty, we find that in terms of
compression performance, autoregressive and hierarchical priors are
complementary and, together, exploit the probabilistic structure in the latents
better than all previous learned models. The combined model yields
state-of-the-art rate--distortion performance, providing a 15.8% average
reduction in file size over the previous state-of-the-art method based on deep
learning, which corresponds to a 59.8% size reduction over JPEG, more than 35%
reduction compared to WebP and JPEG2000, and bitstreams 8.4% smaller than BPG,
the current state-of-the-art image codec. To the best of our knowledge, our
model is the first learning-based method to outperform BPG on both PSNR and
MS-SSIM distortion metrics.