pwc-archive/papers-with-abstracts huggingface dataset:
Lane detection in complex scenes based on end-to-end neural network
Authors: Wenbo Liu, Fei Yan, Kuan Tang, Jiyong Zhang, Tao Deng
Date: 2020-10-26 00:00:00
Tasks: Decision Making, Lane Detection, Semantic Segmentation

The lane detection is a key problem to solve the division of derivable areas in unmanned driving, and the detection accuracy of lane lines plays an important role in the decision-making of vehicle driving. Scenes faced by vehicles in daily driving are relatively complex. Bright light, insufficient light, and crowded vehicles will bring varying degrees of difficulty to lane detection. So we combine the advantages of spatial convolution in spatial information processing and the efficiency of ERFNet in semantic segmentation, propose an end-to-end network to lane detection in a variety of complex scenes. And we design the information exchange block by combining spatial convolution and dilated convolution, which plays a great role in understanding detailed information. Finally, our network was tested on the CULane database and its F1-measure with IOU threshold of 0.5 can reach 71.9%.
Residual Recurrent CRNN for End-to-End Optical Music Recognition on Monophonic Scores
Authors: Aozhi Liu, Lipei Zhang, Yaqi Mei, Baoqiang Han, Zifeng Cai, Zhaohua Zhu, Jing Xiao
Date: 2020-10-26 00:00:00
Tasks: Decoder

One of the challenges of the Optical Music Recognition task is to transcript the symbols of the camera-captured images into digital music notations. Previous end-to-end model which was developed as a Convolutional Recurrent Neural Network does not explore sufficient contextual information from full scales and there is still a large room for improvement. We propose an innovative framework that combines a block of Residual Recurrent Convolutional Neural Network with a recurrent Encoder-Decoder network to map a sequence of monophonic music symbols corresponding to the notations present in the image. The Residual Recurrent Convolutional block can improve the ability of the model to enrich the context information. The experiment results are benchmarked against a publicly available dataset called CAMERA-PRIMUS, which demonstrates that our approach surpass the state-of-the-art end-to-end method using Convolutional Recurrent Neural Network.
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
Authors: Yucheng Wang, Bowen Yu, Yueyang Zhang, Tingwen Liu, Hongsong Zhu, Limin Sun
Date: 2020-10-26 00:00:00
Proceeding: COLING 2020 8
Tasks: Relation, Relation Extraction

Extracting entities and relations from unstructured text has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in identifying overlapping relations with shared entities. Prior works show that joint learning can result in a noticeable performance gain. However, they usually involve sequential interrelated steps and suffer from the problem of exposure bias. At training time, they predict with the ground truth conditions while at inference it has to make extraction from scratch. This discrepancy leads to error accumulation. To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias. TPLinker formulates joint extraction as a token pair linking problem and introduces a novel handshaking tagging scheme that aligns the boundary tokens of entity pairs under each relation type. Experiment results show that TPLinker performs significantly better on overlapping and multiple relation extraction, and achieves state-of-the-art performance on two public datasets.
Flexible Piecewise Curves Estimation for Photo Enhancement
Authors: Chongyi Li, Chunle Guo, Qiming Ai, Shangchen Zhou, Chen Change Loy
Date: 2020-10-26 00:00:00
Tasks: GPU

This paper presents a new method, called FlexiCurve, for photo enhancement. Unlike most existing methods that perform image-to-image mapping, which requires expensive pixel-wise reconstruction, FlexiCurve takes an input image and estimates global curves to adjust the image. The adjustment curves are specially designed for performing piecewise mapping, taking nonlinear adjustment and differentiability into account. To cope with challenging and diverse illumination properties in real-world images, FlexiCurve is formulated as a multi-task framework to produce diverse estimations and the associated confidence maps. These estimations are adaptively fused to improve local enhancements of different regions. Thanks to the image-to-curve formulation, for an image with a size of 512*512*3, FlexiCurve only needs a lightweight network (150K trainable parameters) and it has a fast inference speed (83FPS on a single NVIDIA 2080Ti GPU). The proposed method improves efficiency without compromising the enhancement quality and losing details in the original image. The method is also appealing as it is not limited to paired training data, thus it can flexibly learn rich enhancement styles from unpaired data. Extensive experiments demonstrate that our method achieves state-of-the-art performance on photo enhancement quantitively and qualitatively.
Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network
Authors: Niranjan Deshpande, Dominique Vaufreydaz, Anne Spalanzani
Date: 2020-10-26 00:00:00
Tasks: Autonomous Driving, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Decision making for autonomous driving in urban environments is challenging due to the complexity of the road structure and the uncertainty in the behavior of diverse road users. Traditional methods consist of manually designed rules as the driving policy, which require expert domain knowledge, are difficult to generalize and might give sub-optimal results as the environment gets complex. Whereas, using reinforcement learning, optimal driving policy could be learned and improved automatically through several interactions with the environment. However, current research in the field of reinforcement learning for autonomous driving is mainly focused on highway setup with little to no emphasis on urban environments. In this work, a deep reinforcement learning based decision-making approach for high-level driving behavior is proposed for urban environments in the presence of pedestrians. For this, the use of Deep Recurrent Q-Network (DRQN) is explored, a method combining state-of-the art Deep Q-Network (DQN) with a long term short term memory (LSTM) layer helping the agent gain a memory of the environment. A 3-D state representation is designed as the input combined with a well defined reward function to train the agent for learning an appropriate behavior policy in a real-world like urban simulator. The proposed method is evaluated for dense urban scenarios and compared with a rule-based approach and results show that the proposed DRQN based driving behavior decision maker outperforms the rule-based approach.
The sample complexity of level set approximation
Authors: François Bachoc, Tommaso Cesari, Sébastien Gerchinovitz
Date: 2020-10-26 00:00:00

We study the problem of approximating the level set of an unknown function by sequentially querying its values. We introduce a family of algorithms called Bisect and Approximate through which we reduce the level set approximation problem to a local function approximation problem. We then show how this approach leads to rate-optimal sample complexity guarantees for H{\"o}lder functions, and we investigate how such rates improve when additional smoothness or other structural assumptions hold true.
Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction
Authors: Amir Pouran Ben Veyseh, Tuan Ngo Nguyen, Thien Huu Nguyen
Date: 2020-10-26 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Event Argument Extraction, Inductive Bias, Sentence

The goal of Event Argument Extraction (EAE) is to find the role of each entity mention for a given event trigger word. It has been shown in the previous works that the syntactic structures of the sentences are helpful for the deep learning models for EAE. However, a major problem in such prior works is that they fail to exploit the semantic structures of the sentences to induce effective representations for EAE. Consequently, in this work, we propose a novel model for EAE that exploits both syntactic and semantic structures of the sentences with the Graph Transformer Networks (GTNs) to learn more effective sentence structures for EAE. In addition, we introduce a novel inductive bias based on information bottleneck to improve generalization of the EAE models. Extensive experiments are performed to demonstrate the benefits of the proposed model, leading to state-of-the-art performance for EAE on standard datasets.
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation
Authors: Amir Pouran Ben Veyseh, Nasim Nour, Franck Dernoncourt, Quan Hung Tran, Dejing Dou, Thien Huu Nguyen
Date: 2020-10-26 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment polarity of a sentence toward a specific aspect. Recently, it has been shown that dependency trees can be integrated into deep learning models to produce the state-of-the-art performance for ABSA. However, these models tend to compute the hidden/representation vectors without considering the aspect terms and fail to benefit from the overall contextual importance scores of the words that can be obtained from the dependency tree for ABSA. In this work, we propose a novel graph-based deep learning model to overcome these two issues of the prior work on ABSA. In our model, gate vectors are generated from the representation vectors of the aspect terms to customize the hidden vectors of the graph-based models toward the aspect terms. In addition, we propose a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA. The proposed model achieves the state-of-the-art performance on three benchmark datasets.
A Novel Classification Approach for Credit Scoring based on Gaussian Mixture Models
Authors: Hamidreza Arian, Seyed Mohammad Sina Seyfi, Azin Sharifi
Date: 2020-10-26 00:00:00
Tasks: General Classification

Credit scoring is a rapidly expanding analytical technique used by banks and other financial institutions. Academic studies on credit scoring provide a range of classification techniques used to differentiate between good and bad borrowers. The main contribution of this paper is to introduce a new method for credit scoring based on Gaussian Mixture Models. Our algorithm classifies consumers into groups which are labeled as positive or negative. Labels are estimated according to the probability associated with each class. We apply our model with real world databases from Australia, Japan, and Germany. Numerical results show that not only our model's performance is comparable to others, but also its flexibility avoids over-fitting even in the absence of standard cross validation techniques. The framework developed by this paper can provide a computationally efficient and powerful tool for assessment of consumer default risk in related financial institutions.
Video-based Facial Expression Recognition using Graph Convolutional Networks
Authors: Daizong Liu, Hongting Zhang, Pan Zhou
Date: 2020-10-26 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Facial expression recognition (FER), aiming to classify the expression present in the facial image or video, has attracted a lot of research interests in the field of artificial intelligence and multimedia. In terms of video based FER task, it is sensible to capture the dynamic expression variation among the frames to recognize facial expression. However, existing methods directly utilize CNN-RNN or 3D CNN to extract the spatial-temporal features from different facial units, instead of concentrating on a certain region during expression variation capturing, which leads to limited performance in FER. In our paper, we introduce a Graph Convolutional Network (GCN) layer into a common CNN-RNN based model for video-based FER. First, the GCN layer is utilized to learn more significant facial expression features which concentrate on certain regions after sharing information between extracted CNN features of nodes. Then, a LSTM layer is applied to learn long-term dependencies among the GCN learned features to model the variation. In addition, a weight assignment mechanism is also designed to weight the output of different nodes for final classification by characterizing the expression intensities in each frame. To the best of our knowledge, it is the first time to use GCN in FER task. We evaluate our method on three widely-used datasets, CK+, Oulu-CASIA and MMI, and also one challenging wild dataset AFEW8.0, and the experimental results demonstrate that our method has superior performance to existing methods.
The training accuracy of two-layer neural networks: its estimation and understanding using random datasets
Authors: Shuyue Guan, Murray Loew
Date: 2020-10-26 00:00:00

Although the neural network (NN) technique plays an important role in machine learning, understanding the mechanism of NN models and the transparency of deep learning still require more basic research. In this study, we propose a novel theory based on space partitioning to estimate the approximate training accuracy for two-layer neural networks on random datasets without training. There appear to be no other studies that have proposed a method to estimate training accuracy without using input data and/or trained models. Our method estimates the training accuracy for two-layer fully-connected neural networks on two-class random datasets using only three arguments: the dimensionality of inputs (d), the number of inputs (N), and the number of neurons in the hidden layer (L). We have verified our method using real training accuracies in our experiments. The results indicate that the method will work for any dimension, and the proposed theory could extend also to estimate deeper NN models. The main purpose of this paper is to understand the mechanism of NN models by the approach of estimating training accuracy but not to analyze their generalization nor their performance in real-world applications. This study may provide a starting point for a new way for researchers to make progress on the difficult problem of understanding deep learning.
Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning
Authors: Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen
Date: 2020-10-26 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect-oriented  Opinion Extraction, Deep Learning, Sentence, Sentiment Analysis

Targeted opinion word extraction (TOWE) is a sub-task of aspect based sentiment analysis (ABSA) which aims to find the opinion words for a given aspect-term in a sentence. Despite their success for TOWE, the current deep learning models fail to exploit the syntactic information of the sentences that have been proved to be useful for TOWE in the prior research. In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words. We also introduce a novel regularization technique to improve the performance of the deep learning models based on the representation distinctions between the words in TOWE. The proposed model is extensively analyzed and achieves the state-of-the-art performance on four benchmark datasets.
Multimodal Topic Learning for Video Recommendation
Authors: Shi Pu, Yijiang He, Zheng Li, Mao Zheng
Date: 2020-10-26 00:00:00
Tasks: Computational Efficiency, Recommendation Systems

Facilitated by deep neural networks, video recommendation systems have made significant advances. Existing video recommendation systems directly exploit features from different modalities (e.g., user personal data, user behavior data, video titles, video tags, and visual contents) to input deep neural networks, while expecting the networks to online mine user-preferred topics implicitly from these features. However, the features lacking semantic topic information limits accurate recommendation generation. In addition, feature crosses using visual content features generate high dimensionality features that heavily downgrade the online computational efficiency of networks. In this paper, we explicitly separate topic generation from recommendation generation, propose a multimodal topic learning algorithm to exploit three modalities (i.e., tags, titles, and cover images) for generating video topics offline. The topics generated by the proposed algorithm serve as semantic topic features to facilitate preference scope determination and recommendation generation. Furthermore, we use the semantic topic features instead of visual content features to effectively reduce online computational cost. Our proposed algorithm has been deployed in the Kuaibao information streaming platform. Online and offline evaluation results show that our proposed algorithm performs favorably.
What is the best data augmentation for 3D brain tumor segmentation?
Authors: Marco Domenico Cirillo, David Abramian, Anders Eklund
Date: 2020-10-26 00:00:00
Tasks: Brain Tumor Segmentation, Data Augmentation, Segmentation, Tumor Segmentation

Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain. Despite this fact, data augmentation has in our opinion not been fully explored for brain tumor segmentation. In this project we apply different types of data augmentation (flipping, rotation, scaling, brightness adjustment, elastic deformation) when training a standard 3D U-Net, and demonstrate that augmentation significantly improves the network's performance in many cases. Our conclusion is that brightness augmentation and elastic deformation work best, and that combinations of different augmentation techniques do not provide further improvement compared to only using one augmentation technique. Our code is available at https://github.com/mdciri/3D-augmentation-techniques.
Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping
Authors: Minjia Zhang, Yuxiong He
Date: 2020-10-26 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Language Modeling, Language Modelling, Unsupervised Pre-training

Recently, Transformer-based language models have demonstrated remarkable performance across many NLP domains. However, the unsupervised pre-training step of these models suffers from unbearable overall computational expenses. Current methods for accelerating the pre-training either rely on massive parallelism with advanced hardware or are not applicable to language modeling. In this work, we propose a method based on progressive layer dropping that speeds the training of Transformer-based language models, not at the cost of excessive hardware resources but from model architecture change and training technique boosted efficiency. Extensive experiments on BERT show that the proposed method achieves a 24% time reduction on average per sample and allows the pre-training to be 2.5 times faster than the baseline to get a similar accuracy on downstream tasks. While being faster, our pre-trained models are equipped with strong knowledge transferability, achieving comparable and sometimes higher GLUE score than the baseline when pre-trained with the same number of samples.
Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds
Authors: Keisuke Kinoshita, Marc Delcroix, Naohiro Tawara
Date: 2020-10-26 00:00:00
Tasks: Clustering

Recent diarization technologies can be categorized into two approaches, i.e., clustering and end-to-end neural approaches, which have different pros and cons. The clustering-based approaches assign speaker labels to speech regions by clustering speaker embeddings such as x-vectors. While it can be seen as a current state-of-the-art approach that works for various challenging data with reasonable robustness and accuracy, it has a critical disadvantage that it cannot handle overlapped speech that is inevitable in natural conversational data. In contrast, the end-to-end neural diarization (EEND), which directly predicts diarization labels using a neural network, was devised to handle the overlapped speech. While the EEND, which can easily incorporate emerging deep-learning technologies, has started outperforming the x-vector clustering approach in some realistic database, it is difficult to make it work for `long' recordings (e.g., recordings longer than 10 minutes) because of, e.g., its huge memory consumption. Block-wise independent processing is also difficult because it poses an inter-block label permutation problem, i.e., an ambiguity of the speaker label assignments between blocks. In this paper, we propose a simple but effective hybrid diarization framework that works with overlapped speech and for long recordings containing an arbitrary number of speakers. It modifies the conventional EEND framework to simultaneously output global speaker embeddings so that speaker clustering can be performed across blocks to solve the permutation problem. With experiments based on simulated noisy reverberant 2-speaker meeting-like data, we show that the proposed framework works significantly better than the original EEND especially when the input data is long.
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy
Authors: Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon
Date: 2020-10-26 00:00:00
Tasks: Adversarial Robustness, Autonomous Driving, Fairness

Convolutional neural networks (CNNs) have made significant advancement, however, they are widely known to be vulnerable to adversarial attacks. Adversarial training is the most widely used technique for improving adversarial robustness to strong white-box attacks. Prior works have been evaluating and improving the model average robustness without class-wise evaluation. The average evaluation alone might provide a false sense of robustness. For example, the attacker can focus on attacking the vulnerable class, which can be dangerous, especially, when the vulnerable class is a critical one, such as "human" in autonomous driving. We propose an empirical study on the class-wise accuracy and robustness of adversarially trained models. We find that there exists inter-class discrepancy for accuracy and robustness even when the training dataset has an equal number of samples for each class. For example, in CIFAR10, "cat" is much more vulnerable than other classes. Moreover, this inter-class discrepancy also exists for normally trained models, while adversarial training tends to further increase the discrepancy. Our work aims to investigate the following questions: (a) is the phenomenon of inter-class discrepancy universal regardless of datasets, model architectures and optimization hyper-parameters? (b) If so, what can be possible explanations for the inter-class discrepancy? (c) Can the techniques proposed in the long tail classification be readily extended to adversarial training for addressing the inter-class discrepancy?