pwc-archive/papers-with-abstracts huggingface dataset:
DNA: Deeply-supervised Nonlinear Aggregation for Salient Object Detection
Authors: Yun Liu, Ming-Ming Cheng, Xin-Yu Zhang, Guang-Yu Nie, Meng Wang
Date: 2019-03-28 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Saliency Prediction, Salient Object Detection

Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multi-scale convolutional features in convolutional neural networks (CNNs). Many popular methods impose deep supervision to perform side-output predictions that are linearly aggregated for final saliency prediction. In this paper, we theoretically and experimentally demonstrate that linear aggregation of side-output predictions is suboptimal, and it only makes limited use of the side-output information obtained by deep supervision. To solve this problem, we propose Deeply-supervised Nonlinear Aggregation (DNA) for better leveraging the complementary information of various side-outputs. Compared with existing methods, it i) aggregates side-output features rather than predictions, and ii) adopts nonlinear instead of linear transformations. Experiments demonstrate that DNA can successfully break through the bottleneck of current linear approaches. Specifically, the proposed saliency detector, a modified U-Net architecture with DNA, performs favorably against state-of-the-art methods on various datasets and evaluation metrics without bells and whistles.
Asymmetric Deep Semantic Quantization for Image Retrieval
Authors: Zhan Yang, Osolo Ian Raymond, Wuqing Sun, Jun Long
Date: 2019-03-29 00:00:00
Tasks: Image Retrieval, Quantization, Retrieval

Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning based techniques, hashing can outperform non-learning based hashing technique in many applications. However, we argue that the current deep learning based hashing methods ignore some critical problems (e.g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes). In this paper, we propose a novel image hashing method, termed as \textbf{\underline{A}}symmetric \textbf{\underline{D}}eep \textbf{\underline{S}}emantic \textbf{\underline{Q}}uantization (\textbf{ADSQ}). \textbf{ADSQ} is implemented using three stream frameworks, which consist of one \emph{LabelNet} and two \emph{ImgNets}. The \emph{LabelNet} leverages the power of three fully-connected layers, which are used to capture rich semantic information between image pairs. For the two \emph{ImgNets}, they each adopt the same convolutional neural network structure, but with different weights (i.e., asymmetric convolutional neural networks). The two \emph{ImgNets} are used to generate discriminative compact hash codes. Specifically, the function of the \emph{LabelNet} is to capture rich semantic information that is used to guide the two \emph{ImgNets} in minimizing the gap between the real-continuous features and the discrete binary codes. Furthermore, \textbf{ADSQ} can utilize the most critical semantic information to guide the feature learning process and consider the consistency of the common semantic space and Hamming space. Experimental results on three benchmarks (i.e., CIFAR-10, NUS-WIDE, and ImageNet) demonstrate that the proposed \textbf{ADSQ} can outperforms current state-of-the-art methods.
Multimodal Emotion Classification
Authors: Anurag Illendula, Amit Sheth
Date: 2019-03-13 00:00:00
Tasks: Classification, Emotion Classification, General Classification

Most NLP and Computer Vision tasks are limited to scarcity of labelled data.
In social media emotion classification and other related tasks, hashtags have
been used as indicators to label data. With the rapid increase in emoji usage
of social media, emojis are used as an additional feature for major social NLP
tasks. However, this is less explored in case of multimedia posts on social
media where posts are composed of both image and text. At the same time, w.e
have seen a surge in the interest to incorporate domain knowledge to improve
machine understanding of text. In this paper, we investigate whether domain
knowledge for emoji can improve the accuracy of emotion classification task. We
exploit the importance of different modalities from social media post for
emotion classification task using state-of-the-art deep learning architectures.
Our experiments demonstrate that the three modalities (text, emoji and images)
encode different information to express emotion and therefore can complement
each other. Our results also demonstrate that emoji sense depends on the
textual context, and emoji combined with text encodes better information than
considered separately. The highest accuracy of 71.98\% is achieved with a
training data of 550k posts.
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
Authors: Kai Zhang, WangMeng Zuo, Lei Zhang
Date: 2019-03-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deblurring, Image Restoration, Image Super-Resolution, Super-Resolution

While deep neural networks (DNN) based single image super-resolution (SISR)
methods are rapidly gaining popularity, they are mainly designed for the
widely-used bicubic degradation, and there still remains the fundamental
challenge for them to super-resolve low-resolution (LR) image with arbitrary
blur kernels. In the meanwhile, plug-and-play image restoration has been
recognized with high flexibility due to its modular structure for easy plug-in
of denoiser priors. In this paper, we propose a principled formulation and
framework by extending bicubic degradation based deep SISR with the help of
plug-and-play framework to handle LR images with arbitrary blur kernels.
Specifically, we design a new SISR degradation model so as to take advantage of
existing blind deblurring methods for blur kernel estimation. To optimize the
new degradation induced energy function, we then derive a plug-and-play
algorithm via variable splitting technique, which allows us to plug any
super-resolver prior rather than the denoiser prior as a modular part.
Quantitative and qualitative evaluations on synthetic and real LR images
demonstrate that the proposed deep plug-and-play super-resolution framework is
flexible and effective to deal with blurry LR images.
Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks
Authors: Zhe He, Adrian Spurr, Xucong Zhang, Otmar Hilliges
Date: 2019-03-29 00:00:00
Proceeding: ICCV 2019 10
Tasks: Gaze Estimation, gaze redirection

Gaze redirection is the task of changing the gaze to a desired direction for a given monocular eye patch image. Many applications such as videoconferencing, films, games, and generation of training data for gaze estimation require redirecting the gaze, without distorting the appearance of the area surrounding the eye and while producing photo-realistic images. Existing methods lack the ability to generate perceptually plausible images. In this work, we present a novel method to alleviate this problem by leveraging generative adversarial training to synthesize an eye image conditioned on a target gaze direction. Our method ensures perceptual similarity and consistency of synthesized images to the real images. Furthermore, a gaze estimation loss is used to control the gaze direction accurately. To attain high-quality images, we incorporate perceptual and cycle consistency losses into our architecture. In extensive evaluations we show that the proposed method outperforms state-of-the-art approaches in terms of both image quality and redirection precision. Finally, we show that generated images can bring significant improvement for the gaze estimation task if used to augment real training data.
Adversarial Robustness vs Model Compression, or Both?
Authors: Shaokai Ye, Kaidi Xu, Sijia Liu, Jan-Henrik Lambrechts, huan zhang, Aojun Zhou, Kaisheng Ma, Yanzhi Wang, Xue Lin
Date: 2019-03-29 00:00:00
Tasks: Adversarial Robustness, model, Model Compression, Network Pruning

It is well known that deep neural networks (DNNs) are vulnerable to adversarial attacks, which are implemented by adding crafted perturbations onto benign examples. Min-max robust optimization based adversarial training can provide a notion of security against adversarial attacks. However, adversarial robustness requires a significantly larger capacity of the network than that for the natural training with only benign examples. This paper proposes a framework of concurrent adversarial training and weight pruning that enables model compression while still preserving the adversarial robustness and essentially tackles the dilemma of adversarial training. Furthermore, this work studies two hypotheses about weight pruning in the conventional setting and finds that weight pruning is essential for reducing the network model size in the adversarial setting, training a small model from scratch even with inherited initialization from the large model cannot achieve both adversarial robustness and high standard accuracy. Code is available at https://github.com/yeshaokai/Robustness-Aware-Pruning-ADMM.
CroP: Color Constancy Benchmark Dataset Generator
Authors: Nikola Banić, Karlo Koščević, Marko Subašić, Sven Lončarić
Date: 2019-03-29 00:00:00
Tasks: Color Constancy

Implementing color constancy as a pre-processing step in contemporary digital
cameras is of significant importance as it removes the influence of scene
illumination on object colors. Several benchmark color constancy datasets have
been created for the purpose of developing and testing new color constancy
methods. However, they all have numerous drawbacks including a small number of
images, erroneously extracted ground-truth illuminations, long histories of
misuses, violations of their stated assumptions, etc. To overcome such and
similar problems, in this paper a color constancy benchmark dataset generator
is proposed. For a given camera sensor it enables generation of any number of
realistic raw images taken in a subset of the real world, namely images of
printed photographs. Datasets with such images share many positive features
with other existing real-world datasets, while some of the negative features
are completely eliminated. The generated images can be successfully used to
train methods that afterward achieve high accuracy on real-world datasets. This
opens the way for creating large enough datasets for advanced deep learning
techniques. Experimental results are presented and discussed. The source code
is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.
Predictability of diffusion-based recommender systems
Authors: Peng Zhang, Leyang Xue, An Zeng
Date: 2019-03-29 00:00:00
Tasks: Recommendation Systems

The recommendation methods based on network diffusion have been shown to
perform well in both recommendation accuracy and diversity. Nowdays, numerous
extensions have been made to further improve the performance of such methods.
However, to what extent can items be predicted by diffusion-based algorithms
still lack of understanding. Here, we mainly propose a method to quantify the
predictability of diffusion-based algorithms. Accordingly, we conduct
experiments on Movielens and Netflix data sets. The results show that the
higher recommendation accuracy based on diffusion algorithms can still be
achieved by optimizing the way of resource allocation on a density network. On
a sparse network, the possibility of improving accuracy is relatively low due
to the fact that the current accuracy of diffusion-based methods is very close
its predictability. In this case, we find that the predictability can be
improved significantly by multi-steps diffusion, especially for users with less
historical information. In contrast to common belief, there are plausible
circumstances where the higher predictability of diffusion-based methods do not
correspond to those users with more historical recording. Thus, we proposed the
diffusion coverage and item average degree to explain this phenomenon. In
addition, we demonstrate the recommendation accuracy in real online system is
overestimated by random partition used in the literature, suggesting the
recommendation in real online system may be a harder task.
Re-Ranking Words to Improve Interpretability of Automatically Generated Topics
Authors: Areej Alokaili, Nikolaos Aletras, Mark Stevenson
Date: 2019-03-29 00:00:00
Proceeding: WS 2019 5
Tasks: Interpretable Machine Learning, Re-Ranking, Retrieval

Topics models, such as LDA, are widely used in Natural Language Processing.
Making their output interpretable is an important area of research with
applications to areas such as the enhancement of exploratory search interfaces
and the development of interpretable machine learning models. Conventionally,
topics are represented by their n most probable words, however, these
representations are often difficult for humans to interpret. This paper
explores the re-ranking of topic words to generate more interpretable topic
representations. A range of approaches are compared and evaluated in two
experiments. The first uses crowdworkers to associate topics represented by
different word rankings with related documents. The second experiment is an
automatic approach based on a document retrieval task applied on multiple
domains. Results in both experiments demonstrate that re-ranking words improves
topic interpretability and that the most effective re-ranking schemes were
those which combine information about the importance of words both within
topics and their relative frequency in the entire corpus. In addition, close
correlation between the results of the two evaluation approaches suggests that
the automatic method proposed here could be used to evaluate re-ranking methods
without the need for human judgements.
Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues
Authors: Sushant Kafle, Cecilia O. Alm, Matt Huenerfauth
Date: 2019-03-28 00:00:00
Proceeding: WS 2019 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Prosodic cues in conversational speech aid listeners in discerning a message. We investigate whether acoustic cues in spoken dialogue can be used to identify the importance of individual words to the meaning of a conversation turn. Individuals who are Deaf and Hard of Hearing often rely on real-time captions in live meetings. Word error rate, a traditional metric for evaluating automatic speech recognition, fails to capture that some words are more important for a system to transcribe correctly than others. We present and evaluate neural architectures that use acoustic features for 3-class word importance prediction. Our model performs competitively against state-of-the-art text-based word-importance prediction models, and it demonstrates particular benefits when operating on imperfect ASR output.
In Search of Meaning: Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse
Authors: Mahmoud El-Haj, Paul Rayson, Martin Walker, Steven Young, Vasiliki Simaki
Date: 2019-03-28 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Sense Disambiguation

We critically assess mainstream accounting and finance research applying
methods from computational linguistics (CL) to study financial discourse. We
also review common themes and innovations in the literature and assess the
incremental contributions of work applying CL methods over manual content
analysis. Key conclusions emerging from our analysis are: (a) accounting and
finance research is behind the curve in terms of CL methods generally and word
sense disambiguation in particular; (b) implementation issues mean the proposed
benefits of CL are often less pronounced than proponents suggest; (c)
structural issues limit practical relevance; and (d) CL methods and high
quality manual analysis represent complementary approaches to analyzing
financial discourse. We describe four CL tools that have yet to gain traction
in mainstream AF research but which we believe offer promising ways to enhance
the study of meaning in financial discourse. The four tools are named entity
recognition (NER), summarization, semantics and corpus linguistics.
Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition
Authors: Shane Settle, Kartik Audhkhasi, Karen Livescu, Michael Picheny
Date: 2019-03-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Word Embeddings

Direct acoustics-to-word (A2W) systems for end-to-end automatic speech
recognition are simpler to train, and more efficient to decode with, than
sub-word systems. However, A2W systems can have difficulties at training time
when data is limited, and at decoding time when recognizing words outside the
training vocabulary. To address these shortcomings, we investigate the use of
recently proposed acoustic and acoustically grounded word embedding techniques
in A2W systems. The idea is based on treating the final pre-softmax weight
matrix of an AWE recognizer as a matrix of word embedding vectors, and using an
externally trained set of word embeddings to improve the quality of this
matrix. In particular we introduce two ideas: (1) Enforcing similarity at
training time between the external embeddings and the recognizer weights, and
(2) using the word embeddings at test time for predicting out-of-vocabulary
words. Our word embedding model is acoustically grounded, that is it is learned
jointly with acoustic embeddings so as to encode the words' acoustic-phonetic
content; and it is parametric, so that it can embed any arbitrary (potentially
out-of-vocabulary) sequence of characters. We find that both techniques improve
the performance of an A2W recognizer on conversational telephone speech.
Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering
Authors: Lahari Poddar, Leonardo Neves, William Brendel, Luis Marujo, Sergey Tulyakov, Pradeep Karuturi
Date: 2019-03-29 00:00:00
Proceeding: NAACL 2019 6
Tasks: Clustering, General Classification

Tracking user reported bugs requires considerable engineering effort in going
through many repetitive reports and assigning them to the correct teams. This
paper proposes a neural architecture that can jointly (1) detect if two bug
reports are duplicates, and (2) aggregate them into latent topics. Leveraging
the assumption that learning the topic of a bug is a sub-task for detecting
duplicates, we design a loss function that can jointly perform both tasks but
needs supervision for only duplicate classification, achieving topic clustering
in an unsupervised fashion. We use a two-step attention module that uses
self-attention for topic clustering and conditional attention for duplicate
detection. We study the characteristics of two types of real world datasets
that have been marked for duplicate bugs by engineers and by non-technical
annotators. The results demonstrate that our model not only can outperform
state-of-the-art methods for duplicate classification on both cases, but can
also learn meaningful latent clusters without additional supervision.
A framework for fake review detection in online consumer electronics retailers
Authors: Rodrigo Barbado, Oscar Araque, Carlos A. Iglesias
Date: 2019-03-29 00:00:00
Tasks: General Classification

The impact of online reviews on businesses has grown significantly during
last years, being crucial to determine business success in a wide array of
sectors, ranging from restaurants, hotels to e-commerce. Unfortunately, some
users use unethical means to improve their online reputation by writing fake
reviews of their businesses or competitors. Previous research has addressed
fake review detection in a number of domains, such as product or business
reviews in restaurants and hotels. However, in spite of its economical
interest, the domain of consumer electronics businesses has not yet been
thoroughly studied. This article proposes a feature framework for detecting
fake reviews that has been evaluated in the consumer electronics domain. The
contributions are fourfold: (i) Construction of a dataset for classifying fake
reviews in the consumer electronics domain in four different cities based on
scraping techniques; (ii) definition of a feature framework for fake review
detection; (iii) development of a fake review classification method based on
the proposed framework and (iv) evaluation and analysis of the results for each
of the cities under study. We have reached an 82% F-Score on the classification
task and the Ada Boost classifier has been proven to be the best one by
statistical means according to the Friedman test.
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters
Authors: Evgeny Kim, Roman Klinger
Date: 2019-03-29 00:00:00
Proceeding: NAACL 2019 6
Tasks: Relation, Relation Classification

The development of a fictional plot is centered around characters who closely
interact with each other forming dynamic social networks. In literature
analysis, such networks have mostly been analyzed without particular relation
types or focusing on roles which the characters take with respect to each
other. We argue that an important aspect for the analysis of stories and their
development is the emotion between characters. In this paper, we combine these
aspects into a unified framework to classify emotional relationships of
fictional characters. We formalize it as a new task and describe the annotation
of a corpus, based on fan-fiction short stories. The extraction pipeline which
we propose consists of character identification (which we treat as given by an
oracle here) and the relation classification. For the latter, we provide
results using several approaches previously proposed for relation
identification with neural methods. The best result of 0.45 F1 is achieved with
a GRU with character position indicators on the task of predicting undirected
emotion relations in the associated social network graph.
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
Authors: Qibin Chen, Junyang Lin, Yichang Zhang, Hongxia Yang, Jingren Zhou, Jie Tang
Date: 2019-03-29 00:00:00
Tasks: Text Generation

Quality product descriptions are critical for providing competitive customer experience in an e-commerce platform. An accurate and attractive description not only helps customers make an informed decision but also improves the likelihood of purchase. However, crafting a successful product description is tedious and highly time-consuming. Due to its importance, automating the product description generation has attracted considerable interests from both research and industrial communities. Existing methods mainly use templates or statistical methods, and their performance could be rather limited. In this paper, we explore a new way to generate the personalized product description by combining the power of neural networks and knowledge base. Specifically, we propose a KnOwledge Based pErsonalized (or KOBE) product description generation model in the context of e-commerce. In KOBE, we extend the encoder-decoder framework, the Transformer, to a sequence modeling formulation using self-attention. In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc. Experiments on real-world datasets demonstrate that the proposed method out-performs the baseline on various metrics. KOBE can achieve an improvement of 9.7% over state-of-the-arts in terms of BLEU. We also present several case studies as the anecdotal evidence to further prove the effectiveness of the proposed approach. The framework has been deployed in Taobao, the largest online e-commerce platform in China.