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Dispensed transformer network for unsupervised domain adaptation

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arXiv 2021年

作者： Li, Yunxiang Li, Jingxiong Dan, Ruilong Wang, Shuai Jin, Kai Zeng, Guodong Wang, Jun Pan, Xiangji Zhang, Qianni Zhou, Huiyu Jin, Qun Wang, Li Wang, Yaqi College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Artificial Intelligence and Biomedical Image Analysis Lab Westlake University Hangzhou China School of Mechanical Electrical and Information Engineering Shandong University Weihai China Department of Ophthalmology Hospital of Zhejiang University Hangzhou China sitem Center for Translational Medicine and Biomedical Entrepreneurship University of Bern Bern Switzerland School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom Department of Human Informatics and Cognitive Sciences Faculty of Human Sciences Waseda University Tokyo Japan Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill United States College of Media Engineering Communication University of Zhejiang Hangzhou China

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. However, it is costly to perform data annotation that provides ground truth labels for training the supervised algorithms, and the high variance of data that comes from different domains tends to severely degrade system performance over cross-site or cross-modality datasets. To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. First, a dispensed residual transformer block is designed, which realizes global attention by dispensed interleaving operation and deals with the excessive computational cost and GPU memory usage of the Transformer. Second, a multi-scale consistency regularization is proposed to alleviate the loss of details in the low-resolution output for better feature alignment. Finally, a feature ranking discriminator is introduced to automatically assign different weights to domain-gap features to lessen the feature distribution distance, reducing the performance shift of two domains. The proposed method is evaluated on large fluorescein angiography (FA) retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate that our proposed network achieves the best performance in comparison with several state-of-the-art techniques. © 2021, CC BY.

关键词： Supervised learning

Towards AR-assisted visualisation and guidance for imaging of dental decay

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Towards AR-assisted visualisation and guidance for imaging o...

作者： Zhou, Yaxuan Yoo, Paul Feng, Yingru Sankar, Aditya Sadr, Alireza Seibel, Eric J. Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Human Photonics Lab Department of Mechanical Engineering University of Washington SeattleWA98195 United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA98195 United States School of Dentistry University of Washington SeattleWA98195 United States

Untreated dental decay is the most prevalent dental problem in the world, affecting up to 2.4 billion people and leading to a significant economic and social burden. Early detection can greatly mitigate irreversible effects of dental decay, avoiding the need for expensive restorative treatment that forever disrupts the enamel protective layer of teeth. However, two key challenges exist that make early decay management difficult: unreliable detection and lack of quantitative monitoring during treatment. New optically based imaging through the enamel provides the dentist a safe means to detect, locate, and monitor the healing process. This work explores the use of an augmented reality (AR) headset to improve the workflow of early decay therapy and monitoring. The proposed workflow includes two novel AR-enabled features: (i) in situ visualisation of pre-operative optically based dental images and (ii) augmented guidance for repetitive imaging during therapy monitoring. The workflow is designed to minimise distraction, mitigate hand–eye coordination problems, and help guide monitoring of early decay during therapy in both clinical and mobile environments. The results from quantitative evaluations as well as a formative qualitative user study uncover the potentials of the proposed system and indicate that AR can serve as a promising tool in tooth decay management. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Augmented reality

Explicit contextual semantics for text comprehension 33

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Explicit contextual semantics for text comprehension

33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019

作者： Zhang, Zhuosheng Wu, Yuwei Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China College of Zhiyuan Shanghai Jiao Tong University China

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. Copyright © 2019 Zhuosheng Zhang, Yuwei Wu, Zuchao Li and Hai Zhao.

关键词： Semantics

Composing Answer from Multi-spans for Reading Comprehension

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arXiv 2020年

作者： Zhang, Zhuosheng Zhang, Yiqing Zhao, Hai Zhou, Xi Zhou, Xiang Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China CloudWalk Technology Shanghai China

This paper presents a novel method to generate answers for non-extraction machine reading comprehension (MRC) tasks whose answers cannot be simply extracted as one span from the given passages. Using a pointer network-style extractive decoder for such type of MRC may result in unsatisfactory performance when the ground-truth answers are given by human annotators or highly re-paraphrased from parts of the passages. On the other hand, using generative decoder cannot well guarantee the resulted answers with well-formed syntax and semantics when encountering long sentences. Therefore, to alleviate the obvious drawbacks of both sides, we propose an answer making-up method from extracted multi-spans that are learned by our model as highly confident n-gram candidates in the given passage. That is, the returned answers are composed of discontinuous multi-spans but not just one consecutive span in the given passages anymore. The proposed method is simple but effective: empirical experiments on MS MARCO show that the proposed method has a better performance on accurately generating long answers, and substantially outperforms two competitive typical one-span and Seq2Seq baseline decoders. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

C-3PO: Cyclic-three-phase optimization for human-robot motion retargeting based on reinforcement learning

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arXiv 2019年

作者： Kim, Taewoo Lee, Joo-Haeng Department of Computer Software and Engineering Korea University of Science and Technology Daejeon Korea Republic of Human-Robot Interaction Research Group Electronics and Telecommunications Research Institute Daejeon Korea Republic of

— Motion retargeting between heterogeneous poly-morphs with different sizes and kinematic configurations requires a comprehensive knowledge of kinematics and inverse kinematics. Moreover, it is non-trivial to provide a kinematic independent general solution. In this study, we developed a cyclic three-phase optimization method based on deep reinforcement learning for human-robot motion retargeting. The motion retargeting and reward calculations were performed using refined data in a latent space by the cyclic and filtering paths of our method. In addition, the human-in-the-loop based three-phase approach provides a framework for the improvement of the motion retargeting policy by both quantitative and qualitative manners. Using the proposed C-3PO method, we were successfully able to learn the motion retargeting skill between the human skeleton and the real NAO, Pepper, Baxter and C-3PO robot motions. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

Cross-Domain Transfer Learning for Dependency Parsing 8th

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Cross-Domain Transfer Learning for Dependency Parsing

8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Li, Zuchao Zhou, Junru Zhao, Hai Wang, Rui Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institude Shanghai Jiao Tong University Shanghai China Kyoto Japan

ISBN: (纸本)9783030322359

In recent years, the research of dependency parsing focuses on improving the accuracy of in-domain data and has made remarkable progress. However, the real world is different from a single scenario dataset, filled with countless scenarios that are not covered by the dataset, namely, out-of-domain. As a result, parsers that perform well on the in-domain data often suffer significant performance degradation on the out-of-domain data. Therefore, in order to adapt the existing in-domain parsers with substantial performance to the new domain scenario, cross-domain transfer learning techniques are essential to solve the domain problem in parsing. In this paper, we examine two scenarios for cross-domain transfer learning: semi-supervised and unsupervised cross-domain transfer learning. Specifically, we adopt a pretrained language model BERT for training on the source domain (in-domain) data at subword level and introduce two tri-training variant methods for the two scenarios so as to achieve the goal of cross-domain transfer learning. The system based on this paper participated in NLPCC-2019-shared-task on cross-domain dependency parsing and won the first place on the "subtask3-un-open" and "subtask4-semi-open" subtasks, indicating the effectiveness of the approaches adopted. © 2019, Springer Nature Switzerland AG.

关键词： Computational linguistics

Filling the gap of utterance-aware and speaker-aware representation for multi-turn dialogue

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arXiv 2020年

作者： Liu, Longxiang Zhang, Zhuosheng Zhao, Hai Zhou, Xi Zhou, Xiang Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China CloudWalk Technology Shanghai China

A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. Thus utterance- and speaker-aware clues are supposed to be well captured in models. However, in the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely by taking the pairwise dialogue history and candidate response as a whole, the hierarchical information on either utterance interrelation or speaker roles coupled in such representations is not well addressed. In this work, we propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history. In detail, we decouple the contextualized word representations by masking mechanisms in Transformer-based PrLM, making each word only focus on the words in current utterance, other utterances, two speaker roles (i.e., utterances of sender and utterances of receiver), respectively. Experimental results show that our method boosts the strong ELECTRA baseline substantially in four public benchmark datasets, and achieves various new state-of-the-art performance over previous methods. A series of ablation studies are conducted to demonstrate the effectiveness of our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

TeachMe: Three-phase learning framework for robotic motion imitation based on interactive teaching and reinforcement learning

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TeachMe: Three-phase learning framework for robotic motion i...

IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Taewoo Kim Joo-Haeng Lee Department of Computer Software and Engineering Korea University of Science and Technology Daejeon Republic of Korea Human-Robot Interaction Research Group Electronics and Telecommunications Research Institute Daejeon Republic of Korea

The following topics are dealt with: human-robot interaction; mobile robots; learning (artificial intelligence); medical robotics; humanoid robots; robot vision; control engineering computing; service robots; telerobo...

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Lecture Notes in Computer Science (including subseries Lectu...

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Preface

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2021年 12856 LNCS卷 v页

作者： Hernández-Leo, Davinia Hishiyama, Reiko Zurita, Gustavo Weyers, Benjamin Nolte, Alexander Ogata, Hiroaki Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Department of Management Control and Information Systems University of Chile Santiago Chile Institute of Computer Science University of Tartu Tartu Estonia School of Creative Science Waseda University Tokyo Japan Human-Computer Interaction Group University of Trier Trier Germany Academic Center for Computing and Media Studies Kyoto University Kyoto Japan