pwc-archive/papers-with-abstracts huggingface dataset:
An End-to-End Audio Classification System based on Raw Waveforms and Mix-Training Strategy
Authors: Jiaxu Chen, Jing Hao, Kai Chen, Di Xie, Shicai Yang, ShiLiang Pu
Date: 2019-11-21 00:00:00
Tasks: Audio Classification, Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Audio classification can distinguish different kinds of sounds, which is helpful for intelligent applications in daily life. However, it remains a challenging task since the sound events in an audio clip is probably multiple, even overlapping. This paper introduces an end-to-end audio classification system based on raw waveforms and mix-training strategy. Compared to human-designed features which have been widely used in existing research, raw waveforms contain more complete information and are more appropriate for multi-label classification. Taking raw waveforms as input, our network consists of two variants of ResNet structure which can learn a discriminative representation. To explore the information in intermediate layers, a multi-level prediction with attention structure is applied in our model. Furthermore, we design a mix-training strategy to break the performance limitation caused by the amount of training data. Experiments show that the mean average precision of the proposed audio classification system on Audio Set dataset is 37.2%. Without using extra training data, our system exceeds the state-of-the-art multi-level attention model.
Empirical Autopsy of Deep Video Captioning Frameworks
Authors: Nayyer Aafaq, Naveed Akhtar, Wei Liu, Ajmal Mian
Date: 2019-11-21 00:00:00
Tasks: Decoder, Language Modelling, Video Captioning, Word Embeddings

Contemporary deep learning based video captioning follows encoder-decoder framework. In encoder, visual features are extracted with 2D/3D Convolutional Neural Networks (CNNs) and a transformed version of those features is passed to the decoder. The decoder uses word embeddings and a language model to map visual features to natural language captions. Due to its composite nature, the encoder-decoder pipeline provides the freedom of multiple choices for each of its components, e.g the choices of CNNs models, feature transformations, word embeddings, and language models etc. Component selection can have drastic effects on the overall video captioning performance. However, current literature is void of any systematic investigation in this regard. This article fills this gap by providing the first thorough empirical analysis of the role that each major component plays in a contemporary video captioning pipeline. We perform extensive experiments by varying the constituent components of the video captioning framework, and quantify the performance gains that are possible by mere component selection. We use the popular MSVD dataset as the test-bed, and demonstrate that substantial performance gains are possible by careful selection of the constituent components without major changes to the pipeline itself. These results are expected to provide guiding principles for future research in the fast growing direction of video captioning.
Convolutional Mixture Density Recurrent Neural Network for Predicting User Location with WiFi Fingerprints
Authors: Weizhu Qian, Fabrice Lauri, Franck Gechter
Date: 2019-11-21 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

Predicting smartphone users activity using WiFi fingerprints has been a popular approach for indoor positioning in recent years. However, such a high dimensional time-series prediction problem can be very tricky to solve. To address this issue, we propose a novel deep learning model, the convolutional mixture density recurrent neural network (CMDRNN), which combines the strengths of convolutional neural networks, recurrent neural networks and mixture density networks. In our model, the CNN sub-model is employed to detect the feature of the high dimensional input, the RNN sub-model is utilized to capture the time dependency and the MDN sub-model is for predicting the final output. For validation, we conduct the experiments on the real-world dataset and the obtained results illustrate the effectiveness of our method.
Emotion Recognition for Vietnamese Social Media Text
Authors: Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
Date: 2019-11-21 00:00:00
Tasks: Emotion Recognition, Sentiment Analysis

Emotion recognition or emotion prediction is a higher approach or a special case of sentiment analysis. In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of analysis in which the results are depicted in more expressions like sadness, enjoyment, anger, disgust, fear, and surprise. Emotion recognition plays a critical role in measuring the brand value of a product by recognizing specific emotions of customers' comments. In this study, we have achieved two targets. First and foremost, we built a standard Vietnamese Social Media Emotion Corpus (UIT-VSMEC) with exactly 6,927 emotion-annotated sentences, contributing to emotion recognition research in Vietnamese which is a low-resource language in natural language processing (NLP). Secondly, we assessed and measured machine learning and deep neural network models on our UIT-VSMEC corpus. As a result, the CNN model achieved the highest performance with the weighted F1-score of 59.74%. Our corpus is available at our research website.
Voice-Face Cross-modal Matching and Retrieval: A Benchmark
Authors: Chuyuan Xiong, Deyuan Zhang, Tao Liu, Xiaoyong Du
Date: 2019-11-21 00:00:00
Tasks: Retrieval, Triplet

Cross-modal associations between voice and face from a person can be learnt algorithmically, which can benefit a lot of applications. The problem can be defined as voice-face matching and retrieval tasks. Much research attention has been paid on these tasks recently. However, this research is still in the early stage. Test schemes based on random tuple mining tend to have low test confidence. Generalization ability of models can not be evaluated by small scale datasets. Performance metrics on various tasks are scarce. A benchmark for this problem needs to be established. In this paper, first, a framework based on comprehensive studies is proposed for voice-face matching and retrieval. It achieves state-of-the-art performance with various performance metrics on different tasks and with high test confidence on large scale datasets, which can be taken as a baseline for the follow-up research. In this framework, a voice anchored L2-Norm constrained metric space is proposed, and cross-modal embeddings are learned with CNN-based networks and triplet loss in the metric space. The embedding learning process can be more effective and efficient with this strategy. Different network structures of the framework and the cross language transfer abilities of the model are also analyzed. Second, a voice-face dataset (with 1.15M face data and 0.29M audio data) from Chinese speakers is constructed, and a convenient and quality controllable dataset collection tool is developed. The dataset and source code of the paper will be published together with this paper.
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
Authors: Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang
Date: 2019-11-21 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Bayesian Optimization, Neural Architecture Search

Neural Architecture Search (NAS) has shown great potentials in finding better neural network designs. Sample-based NAS is the most reliable approach which aims at exploring the search space and evaluating the most promising architectures. However, it is computationally very costly. As a remedy, the one-shot approach has emerged as a popular technique for accelerating NAS using weight-sharing. However, due to the weight-sharing of vastly different networks, the one-shot approach is less reliable than the sample-based approach. In this work, we propose BONAS (Bayesian Optimized Neural Architecture Search), a sample-based NAS framework which is accelerated using weight-sharing to evaluate multiple related architectures simultaneously. Specifically, we apply Graph Convolutional Network predictor as a surrogate model for Bayesian Optimization to select multiple related candidate models in each iteration. We then apply weight-sharing to train multiple candidate models simultaneously. This approach not only accelerates the traditional sample-based approach significantly, but also keeps its reliability. This is because weight-sharing among related architectures are more reliable than those in the one-shot approach. Extensive experiments are conducted to verify the effectiveness of our method over many competing algorithms.
Incorporating Textual Evidence in Visual Storytelling
Authors: Tianyi Li, Sujian Li
Date: 2019-11-21 00:00:00
Proceeding: WS 2019 11
Tasks: Object Recognition, Story Generation, Visual Storytelling

Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation. Motivated by human storytelling process which recalls stories for familiar images, we exploit textual evidence from similar images to help generate coherent and meaningful stories. To pick the images which may provide textual experience, we propose a two-step ranking method based on image object recognition techniques. To utilize textual information, we design an extended Seq2Seq model with two-channel encoder and attention. Experiments on the VIST dataset show that our method outperforms state-of-the-art baseline models without heavy engineering.
Generating Diverse Translation by Manipulating Multi-Head Attention
Authors: Zewei Sun, Shu-Jian Huang, Hao-Ran Wei, Xin-yu Dai, Jia-Jun Chen
Date: 2019-11-21 00:00:00
Tasks: Data Augmentation, Decoder, Diversity, Machine Translation, Response Generation, Translation, Word Translation

Transformer model has been widely used on machine translation tasks and obtained state-of-the-art results. In this paper, we report an interesting phenomenon in its encoder-decoder multi-head attention: different attention heads of the final decoder layer align to different word translation candidates. We empirically verify this discovery and propose a method to generate diverse translations by manipulating heads. Furthermore, we make use of these diverse translations with the back-translation technique for better data augmentation. Experiment results show that our method generates diverse translations without severe drop in translation quality. Experiments also show that back-translation with these diverse translations could bring significant improvement on performance on translation tasks. An auxiliary experiment of conversation response generation task proves the effect of diversity as well.
Heart Segmentation From MRI Scans Using Convolutional Neural Network
Authors: Shakeel Muhammad Ibrahim, Muhammad Sohail Ibrahim, Muhammad Usman, Imran Naseem, Muhammad Moinuddin
Date: 2019-11-21 00:00:00
Tasks: Decoder, Heart Segmentation, Segmentation

Heart is one of the vital organs of human body. A minor dysfunction of heart even for a short time interval can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation of the organ of interest. However, for the segmentation of heart using MRI, only few methods have been proposed each with its own merits and demerits. For further advancement in this area of research, we analyze automated heart segmentation methods for magnetic resonance images. The analysis are based on deep learning methods that processes a full MR scan in a slice by slice fashion to predict desired mask for heart region. We design two encoder decoder type fully convolutional neural network models
LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
Authors: Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua, Duc Thanh Nguyen, Gemma Roig, Sai-Kit Yeung
Date: 2019-11-21 00:00:00
Tasks: 3D Point Cloud Matching, Depth Estimation, Retrieval

In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network that maps 2D and 3D input into a shared latent space representation. We show that such local cross-domain descriptors in the shared embedding are more discriminative than those obtained from individual training in 2D and 3D domains. To facilitate the training process, we built a new dataset by collecting $\approx 1.4$ millions of 2D-3D correspondences with various lighting conditions and settings from publicly available RGB-D scenes. Our descriptor is evaluated in three main experiments: 2D-3D matching, cross-domain retrieval, and sparse-to-dense depth estimation. Experimental results confirm the robustness of our approach as well as its competitive performance not only in solving cross-domain tasks but also in being able to generalize to solve sole 2D and 3D tasks. Our dataset and code are released publicly at \url{https://hkust-vgd.github.io/lcd}.
Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition
Authors: Liu Yafeng, Chen Tian, Liu Zhongyu, Zhang Lei, Hu Yanjun, Ding Enjie
Date: 2019-11-21 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Time Series Analysis

Human actions recognition has attracted more and more people's attention. Many technology have been developed to express human action's features, such as image, skeleton-based, and channel state information(CSI). Among them, on account of CSI's easy to be equipped and undemanding for light, and it has gained more and more attention in some special scene. However, the relationship between CSI signal and human actions is very complex, and some preliminary work must be done to make CSI features easy to understand for computer. Nowadays, many work departed CSI-based features' action dealing into two parts. One part is for features extraction and dimension reduce, and the other part is for time series problems. Some of them even omitted one of the two part work. Therefore, the accuracies of current recognition systems are far from satisfactory. In this paper, we propose a new deep learning based approach, i.e. C3D network and C3D network with attention mechanism, for human actions recognition using CSI signals. This kind of network can make feature extraction from spatial convolution and temporal convolution simultaneously, and through this network the two part of CSI-based human actions recognition mentioned above can be realized at the same time. The entire algorithm structure is simplified. The experimental results show that our proposed C3D network is able to achieve the best recognition performance for all activities when compared with some benchmark approaches.
Data Proxy Generation for Fast and Efficient Neural Architecture Search
Authors: Minje Park
Date: 2019-11-21 00:00:00
Tasks: Neural Architecture Search

Due to the recent advances on Neural Architecture Search (NAS), it gains popularity in designing best networks for specific tasks. Although it shows promising results on many benchmarks and competitions, NAS still suffers from its demanding computation cost for searching high dimensional architectural design space, and this problem becomes even worse when we want to use a large-scale dataset. If we can make a reliable data proxy for NAS, the efficiency of NAS approaches increase accordingly. Our basic observation for making a data proxy is that each example in a specific dataset has a different impact on NAS process and most of examples are redundant from a relative accuracy ranking perspective, which we should preserve when making a data proxy. We propose a systematic approach to measure the importance of each example from this relative accuracy ranking point of view, and make a reliable data proxy based on the statistics of training and testing examples. Our experiment shows that we can preserve the almost same relative accuracy ranking between all possible network configurations even with 10-20$\times$ smaller data proxy.
Minimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation
Authors: Chenze Shao, Jinchao Zhang, Yang Feng, Fandong Meng, Jie zhou
Date: 2019-11-21 00:00:00
Tasks: Machine Translation, Sentence, Translation

Non-Autoregressive Neural Machine Translation (NAT) achieves significant decoding speedup through generating target words independently and simultaneously. However, in the context of non-autoregressive translation, the word-level cross-entropy loss cannot model the target-side sequential dependency properly, leading to its weak correlation with the translation quality. As a result, NAT tends to generate influent translations with over-translation and under-translation errors. In this paper, we propose to train NAT to minimize the Bag-of-Ngrams (BoN) difference between the model output and the reference sentence. The bag-of-ngrams training objective is differentiable and can be efficiently calculated, which encourages NAT to capture the target-side sequential dependency and correlates well with the translation quality. We validate our approach on three translation tasks and show that our approach largely outperforms the NAT baseline by about 5.0 BLEU scores on WMT14 En$\leftrightarrow$De and about 2.5 BLEU scores on WMT16 En$\leftrightarrow$Ro.
An Empirical Study of Sections in Classifying Disease Outbreak Reports
Authors: Son Doan, Mike Conway, Nigel Collier
Date: 2019-11-21 00:00:00
Tasks: Articles, General Classification, Sentence, text-classification, Text Classification

Identifying articles that relate to infectious diseases is a necessary step for any automatic bio-surveillance system that monitors news articles from the Internet. Unlike scientific articles which are available in a strongly structured form, news articles are usually loosely structured. In this chapter, we investigate the importance of each section and the effect of section weighting on performance of text classification. The experimental results show that (1) classification models using the headline and leading sentence achieve a high performance in terms of F-score compared to other parts of the article; (2) all section with bag-of-word representation (full text) achieves the highest recall; and (3) section weighting information can help to improve accuracy.
Relation Network for Person Re-identification
Authors: Hyunjong Park, Bumsub Ham
Date: 2019-11-21 00:00:00
Tasks: Person Re-Identification, Relation, Relation Network

Person re-identification (reID) aims at retrieving an image of the person of interest from a set of images typically captured by multiple cameras. Recent reID methods have shown that exploiting local features describing body parts, together with a global feature of a person image itself, gives robust feature representations, even in the case of missing body parts. However, using the individual part-level features directly, without considering relations between body parts, confuses differentiating identities of different persons having similar attributes in corresponding parts. To address this issue, we propose a new relation network for person reID that considers relations between individual body parts and the rest of them. Our model makes a single part-level feature incorporate partial information of other body parts as well, supporting it to be more discriminative. We also introduce a global contrastive pooling (GCP) method to obtain a global feature of a person image. We propose to use contrastive features for GCP to complement conventional max and averaging pooling techniques. We show that our model outperforms the state of the art on the Market1501, DukeMTMC-reID and CUHK03 datasets, demonstrating the effectiveness of our approach on discriminative person representations.
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVM
Authors: Alberto Barbado, Óscar Corcho, Richard Benjamins
Date: 2019-11-21 00:00:00
Tasks: Anomaly Detection, Diversity, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Unsupervised Anomaly Detection

OneClass SVM is a popular method for unsupervised anomaly detection. As many other methods, it suffers from the black box problem: it is difficult to justify, in an intuitive and simple manner, why the decision frontier is identifying data points as anomalous or non anomalous. Such type of problem is being widely addressed for supervised models. However, it is still an uncharted area for unsupervised learning. In this paper, we evaluate several rule extraction techniques over OneClass SVM models, as well as present alternative designs for some of those algorithms. Together with that, we propose algorithms to compute metrics related with eXplainable Artificial Intelligence (XAI) regarding the "comprehensibility", "representativeness", "stability" and "diversity" of the extracted rules. We evaluate our proposals with different datasets, including real-world data coming from industry. With this, our proposal contributes to extend XAI techniques to unsupervised machine learning models.
Density Propagation with Characteristics-based Deep Learning
Authors: Tenavi Nakamura-Zimmerer, Daniele Venturi, Qi Gong, Wei Kang
Date: 2019-11-21 00:00:00
Tasks: Deep Learning

Uncertainty propagation in nonlinear dynamic systems remains an outstanding problem in scientific computing and control. Numerous approaches have been developed, but are limited in their capability to tackle problems with more than a few uncertain variables or require large amounts of simulation data. In this paper, we propose a data-driven method for approximating joint probability density functions (PDFs) of nonlinear dynamic systems with initial condition and parameter uncertainty. Our approach leverages on the power of deep learning to deal with high-dimensional inputs, but we overcome the need for huge quantities of training data by encoding PDF evolution equations directly into the optimization problem. We demonstrate the potential of the proposed method by applying it to evaluate the robustness of a feedback controller for a six-dimensional rigid body with parameter uncertainty.
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Authors: Yuxuan Song, Lantao Yu, Zhangjie Cao, Zhiming Zhou, Jian Shen, Shuo Shao, Wei-Nan Zhang, Yong Yu
Date: 2019-11-21 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available. To leverage and adapt the label information from source domain, most existing methods employ a feature extracting function and match the marginal distributions of source and target domains in a shared feature space. In this paper, from the perspective of information theory, we show that representation matching is actually an insufficient constraint on the feature space for obtaining a model with good generalization performance in target domain. We then propose variational bottleneck domain adaptation (VBDA), a new domain adaptation method which improves feature transferability by explicitly enforcing the feature extractor to ignore the task-irrelevant factors and focus on the information that is essential to the task of interest for both source and target domains. Extensive experimental results demonstrate that VBDA significantly outperforms state-of-the-art methods across three domain adaptation benchmark datasets.