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Semi-automatic Technology Roadmapping Composing Method for Multiple Science, Technology, and Innovation Data Incorporation [chapter]

Yi Zhang, Hongshu Chen, Donghua Zhu
2016 Innovation, Technology, and Knowledge Management  
The understanding gained will assist in description of computer science macro-trends for R&D decision makers.  ...  Since its first engagement with industry decades ago, Technology Roadmapping (TRM) is taking a more and more important role for technical intelligence in current R&D planning and innovation tracking.  ...  We then apply Term Clumping steps for feature extraction, the process of which is given in Table 1 . in 2013 as a sample; also, we apply Pruning to DII first, and then, Fuzzy Matching. * #T = Number of  ... 
doi:10.1007/978-3-319-39056-7_12 fatcat:2zfhh4ju4fb65hchzgb6etuehu

Small Sample Learning in Big Data Era [article]

Jun Shu, Zongben Xu, Deyu Meng
2018 arXiv   pre-print
As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years.  ...  This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures.  ...  ( a )Figure 12 : a12 Siamese Networks for Chromosome Classification (b) Matching Network for Low Data Drug Discovery Examples of metric learning in SSL.  ... 
arXiv:1808.04572v3 fatcat:lqqzzrmgfnfb3izctvdzgopuny

Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification

Jingya Wang, Xiatian Zhu, Shaogang Gong, Wei Li
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Specifically, we introduce an Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) for simultaneously learning an attribute-semantic and identitydiscriminative feature representation space transferrable  ...  to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain (i.e. unsupervised learning in the target domain).  ...  Acknowledgements This work was partially supported by the China Scholarship Council, Vision Semantics Ltd, Royal Society Newton Advanced Fellowship Programme (NA150459), and In-novateUK Industrial Challenge  ... 
doi:10.1109/cvpr.2018.00242 dblp:conf/cvpr/WangZGL18 fatcat:6t46zlmqendb5i2fbuorvsrcw4

Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification [article]

Jingya Wang, Xiatian Zhu, Shaogang Gong, Wei Li
2018 arXiv   pre-print
Specifically, we introduce an Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) for simultaneously learning an attribute-semantic and identitydiscriminative feature representation space transferrable  ...  to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain (i.e. unsupervised learning in the target domain).  ...  Acknowledgements This work was partially supported by the China Scholarship Council, Vision Semantics Ltd, Royal Society Newton Advanced Fellowship Programme (NA150459), and In-novateUK Industrial Challenge  ... 
arXiv:1803.09786v1 fatcat:zgdruvntp5f7feds5czgfvh7wa

Multi-View Transformer for 3D Visual Grounding [article]

Shijia Huang, Yilun Chen, Jiaya Jia, Liwei Wang
2022 arXiv   pre-print
In this paper, we propose a Multi-View Transformer (MVT) for 3D visual grounding.  ...  The multi-view space enables the network to learn a more robust multi-modal representation for 3D visual grounding and eliminates the dependence on specific views.  ...  In the second stage, the task is modeled as a matching problem where 3D visual features are fused with the text features of the query language description to predict the matching scores.  ... 
arXiv:2204.02174v1 fatcat:2nmvi5o4gjex5dqi7b7fawtayu

Mining for Information Discovery on the Web: Overview and Illustrative Research [chapter]

Hwanjo Yu, AnHai Doan, Jiawei Han
2004 Intelligent Technologies for Information Analysis  
The Web has become a fertile ground for numerous research activities in mining. In this chapter we discuss research on finding targeted information on the Web.  ...  We conclude by briefly discussing novel research opportunities in the area of mining for information discovery on the Web.  ...  Acknowledgments: We thank Ying Lu and Yoonkyong Lee for obtaining the datasets and conducting the experiments in the PROM project, and for valuable comments on parts of this chapter.  ... 
doi:10.1007/978-3-662-07952-2_7 fatcat:kyp6jwkrl5dsvpl2f7l6b34j3a

A survey of joint intent detection and slot-filling models in natural language understanding [article]

H. Weld, X. Huang, S. Long, J. Poon, S. C. Han
2021 arXiv   pre-print
We observe three milestones in this research so far: Intent detection to identify the speaker's intention, slot filling to label each word token in the speech/text, and finally, joint intent classification  ...  Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks have been deemed to proceed independently.  ...  SLU starts with automatic speech recognition (ASR), the task of taking the sound waves or images of expressed language, and transcribing to text.  ... 
arXiv:2101.08091v3 fatcat:ai6w2imilrfupf4m5fm2rjtzxi

Deep Learning for SAR Ship Detection: Past, Present and Future

Jianwei Li, Congan Xu, Hang Su, Long Gao, Taoyang Wang
2022 Remote Sensing  
In the future part, we list the problem and direction of this field. We can find that, in the past five years, the AP50 has boosted from 78.8% in 2017 to 97.8 % in 2022 on SSDD.  ...  After the revival of deep learning in computer vision in 2012, SAR ship detection comes into the deep learning era too.  ...  [95] proposed a feature balance and matching network, which uses the Feature fusion. Chen et al.  ... 
doi:10.3390/rs14112712 fatcat:dbd6a4ugwjc65pook3wpcuj52a

Towards Robust Pattern Recognition: A Review [article]

Xu-Yao Zhang, Cheng-Lin Liu, Ching Y. Suen
2020 arXiv   pre-print
directions for robust pattern recognition.  ...  The accuracies for many pattern recognition tasks have increased rapidly year by year, achieving or even outperforming human performance.  ...  automatically learned and optimized fusion structure.  ... 
arXiv:2006.06976v1 fatcat:mn35i7bmhngl5hxr3vukdcmmde

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [article]

Sicheng Zhao, Xiangyu Yue, Shanghang Zhang, Bo Li, Han Zhao, Bichen Wu, Ravi Krishna, Joseph E. Gonzalez, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia, Kurt Keutzer
2020 arXiv   pre-print
In this paper, we review the latest single-source deep unsupervised domain adaptation methods focused on visual tasks and discuss new perspectives for future research.  ...  Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks.  ...  [69] recently proposed Transferrable Prototypical Networks, which perform domain alignment such that prototypes for each class in the source and target domains are close in the embedding space and the  ... 
arXiv:2009.00155v3 fatcat:yqkew4n4q5gtbjosozufw37ome

Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction [article]

Danushka Bollegala, Ryuichi Kiryo, Kosuke Tsujino, Haruki Yukawa
2020 arXiv   pre-print
Moreover, we find that smoothed inverse frequency (SIF) to be an accurate method to create word embeddings from subword embeddings for multilingual semantic similarity prediction tasks.  ...  In this paper, we empirically compare the two approaches using semantic similarity measurement as an evaluation task across a diverse set of languages.  ...  Given that LIT methods have been popularly used in NMT, where decoder vocabularies are typically less than 100K, it is encouraging to see that this benefit is transferrable to other NLP tasks such as semantic  ... 
arXiv:2002.11004v1 fatcat:2eplhu2a6zg23ejxyakak3o2de

A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining [article]

Chenguang Zhu, Ruochen Xu, Michael Zeng, Xuedong Huang
2020 arXiv   pre-print
Meanwhile, there are a handful of deep neural models for text summarization and dialogue systems.  ...  In this paper, we propose a novel abstractive summary network that adapts to the meeting scenario.  ...  Acknowledgement We thank William Hinthorn for proof-reading this paper. We thank the anonymous reviewers for their valuable comments.  ... 
arXiv:2004.02016v4 fatcat:yjbxv5h4lzapvaax2holwqp6ka

Book of Abstracts of the Digital Humanities in the Nordic Countries 5th conference. Riga, 20–23 October 2020 [article]

Sanita Reinsone, Anda Baklāne, Jānis Daugavietis
2020 Zenodo  
Book of Abstracts DHN, Rīga 2020 Book of Abstracts of the Digital Humanities in the Nordic Countries 5th conference.  ...  Acknowledgements We thank the Fritz Thyssen Foundation for their funding for the research project Distant Viewing.  ...  We thank the Staatsbibliothek Berlin for providing access to the Wegehaupt collection.  ... 
doi:10.5281/zenodo.4107117 fatcat:6ongky6p5rab7gvtawnjmp2ofm

Artificial Intelligence for the Metaverse: A Survey [article]

Thien Huynh-The and Quoc-Viet Pham and Xuan-Qui Pham and Thanh Thi Nguyen and Zhu Han and Dong-Seong Kim
2022 arXiv   pre-print
Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.  ...  We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking  ...  Various CNNs and LSTM networks with simple structures and advanced-designed architectures. [31] Generate short text in image captioning and long text in virtual question answer.  ... 
arXiv:2202.10336v1 fatcat:35isd745dbaqfnpzthnmbaosue

A question-entailment approach to question answering

Asma Ben Abacha, Dina Demner-Fushman
2019 BMC Bioinformatics  
First, we compare logistic regression and deep learning methods for RQE using different kinds of datasets including textual inference, question similarity, and entailment in both the open and clinical  ...  Conclusions: The evaluation results support the relevance of question entailment for QA and highlight the effectiveness of combining IR and RQE for future QA efforts.  ...  Shooshan (NLM/NIH) for her help with the judgment of the retrieved answers, and Ellen Voorhees (NIST) for her help with the TREC LiveQA evaluation.  ... 
doi:10.1186/s12859-019-3119-4 fatcat:ztrn5jaiwjdizmicuscn466ghq
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