Neural Information Processing 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part I
The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020. Due to COVID-19 pandemic the conference was held virtually.The 187 full papers presented w...
Saved in:
| Main Authors | , , , , , |
|---|---|
| Format | eBook Conference Proceeding |
| Language | English |
| Published |
Cham
Springer International Publishing AG
2020
Springer International Publishing |
| Edition | 1 |
| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020. Due to COVID-19 pandemic the conference was held virtually.The 187 full papers presented were carefully reviewed and selected from 618 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The first volume, LNCS 12532, is organized in topical sections on human-computer interaction; image processing and computer vision; natural language processing. |
|---|---|
| AbstractList | The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP 2020, held in Bangkok, Thailand, in November 2020. Due to COVID-19 pandemic the conference was held virtually.The 187 full papers presented were carefully reviewed and selected from 618 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The first volume, LNCS 12532, is organized in topical sections on human-computer interaction; image processing and computer vision; natural language processing. |
| Author | King, Irwin Kwok, James T Yang, Haiqin Leung, Andrew Chi Sing Chan, Jonathan H Pasupa, Kitsuchart |
| Author_xml | – sequence: 1 fullname: Yang, Haiqin – sequence: 2 fullname: Pasupa, Kitsuchart – sequence: 3 fullname: Leung, Andrew Chi Sing – sequence: 4 fullname: Kwok, James T – sequence: 5 fullname: Chan, Jonathan H – sequence: 6 fullname: King, Irwin |
| BookMark | eNpVkE1PwzAMhgMMxDb2Azgg7YY4hCVxmo8jTAMmTcABcY3S1YWx0kDTwd8nWznAxbZeP69le0B6daiRkFPOLjljemK1oUAZMKrAbOMeGSUNkrIT1D7pc8U5BZD24G9PWNYj_VQLarWEIzLgQkhQGXBzTEYxvjHGhBRSS9snZ_e4aXw1ntdlaN59uwr1-LEJS4xxVb-ckMPSVxFHv3lInm9mT9M7uni4nU-vFtRzIaWmaI1mhRKISudel4iSo-ZLZiDLBXjDMs1lkVuZaWNzk8pCWKOs8YXJSwNDctEN9nGN3_E1VG10XxXmIayj-3d4YicdGz-atCM2rqM4c9vXbWkHLvFuZ3Bbx3nn-GjC5wZj63aDl1i36XY3u54qmXgu4Ad2uWWN |
| ContentType | eBook Conference Proceeding |
| Copyright | Springer Nature Switzerland AG 2020 |
| Copyright_xml | – notice: Springer Nature Switzerland AG 2020 |
| DEWEY | 006.32 |
| DOI | 10.1007/978-3-030-63830-6 |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISBN | 9783030638306 3030638308 |
| EISSN | 1611-3349 |
| Edition | 1 1st Edition 2020 |
| Editor | King, Irwin Yang, Haiqin Leung, Andrew Chi-Sing Chan, Jonathan H. Pasupa, Kitsuchart Kwok, James T. |
| Editor_xml | – sequence: 1 givenname: Haiqin orcidid: 0000-0001-5453-476X surname: Yang fullname: Yang, Haiqin email: hqyang@ieee.org – sequence: 2 givenname: Kitsuchart orcidid: 0000-0001-8359-9888 surname: Pasupa fullname: Pasupa, Kitsuchart email: kitsuchart@it.kmitl.ac.th – sequence: 3 givenname: Andrew Chi-Sing orcidid: 0000-0003-0962-6723 surname: Leung fullname: Leung, Andrew Chi-Sing email: eeleungc@cityu.edu.hk – sequence: 4 givenname: James T. orcidid: 0000-0002-4828-8248 surname: Kwok fullname: Kwok, James T. email: jamesk@cse.ust.hk – sequence: 5 givenname: Jonathan H. orcidid: 0000-0002-2384-0462 surname: Chan fullname: Chan, Jonathan H. email: jonathan@sit.kmutt.ac.th – sequence: 6 givenname: Irwin orcidid: 0000-0001-8106-6447 surname: King fullname: King, Irwin email: king@cse.cuhk.edu.hk |
| ExternalDocumentID | 9783030638306 496057 EBC6403012 |
| GroupedDBID | 38. AABBV ABZKH ACGCR AEDXK AEJLV AEJNW AEKFX ALMA_UNASSIGNED_HOLDINGS APEJL AVCSZ AZTDL BBABE CYNQG CZZ DACMV ESBCR I4C IEZ OAOFD OPOMJ SBO TPJZQ TSXQS Z7R Z7U Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z84 Z88 -DT -GH -~X 1SB 29L 2HA 2HV 5QI 875 AASHB ABMNI ACGFS ADCXD AEFIE EJD F5P FEDTE HVGLF LAS LDH P2P RIG RNI RSU SVGTG VI1 ~02 Z87 |
| ID | FETCH-LOGICAL-a12447-e9870d62ee67ba7fee41e71c0835b23a805714db945789b84dbd298698ad8bf83 |
| ISBN | 9783030638290 3030638294 |
| ISSN | 0302-9743 |
| IngestDate | Fri Nov 08 04:27:41 EST 2024 Wed Oct 30 02:36:44 EDT 2024 Tue Mar 12 00:35:45 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| LCCallNum_Ident | Q337.5 |
| Language | English |
| LinkModel | OpenURL |
| MeetingName | International Conference on Neural Information Processing |
| MergedId | FETCHMERGED-LOGICAL-a12447-e9870d62ee67ba7fee41e71c0835b23a805714db945789b84dbd298698ad8bf83 |
| OCLC | 1224365318 |
| PQID | EBC6403012 |
| PageCount | 834 |
| ParticipantIDs | askewsholts_vlebooks_9783030638306 springer_books_10_1007_978_3_030_63830_6 proquest_ebookcentral_EBC6403012 |
| PublicationCentury | 2000 |
| PublicationDate | 2020 2020-11-18 |
| PublicationDateYYYYMMDD | 2020-01-01 2020-11-18 |
| PublicationDate_xml | – year: 2020 text: 2020 |
| PublicationDecade | 2020 |
| PublicationPlace | Cham |
| PublicationPlace_xml | – name: Cham |
| PublicationSeriesSubtitle | Theoretical Computer Science and General Issues |
| PublicationSeriesTitle | Lecture Notes in Computer Science |
| PublicationSeriesTitleAlternate | Lect.Notes Computer |
| PublicationYear | 2020 |
| Publisher | Springer International Publishing AG Springer International Publishing |
| Publisher_xml | – name: Springer International Publishing AG – name: Springer International Publishing |
| RelatedPersons | Hartmanis, Juris Gao, Wen Bertino, Elisa Woeginger, Gerhard Goos, Gerhard Steffen, Bernhard Yung, Moti |
| RelatedPersons_xml | – sequence: 1 givenname: Gerhard surname: Goos fullname: Goos, Gerhard – sequence: 2 givenname: Juris surname: Hartmanis fullname: Hartmanis, Juris – sequence: 3 givenname: Elisa surname: Bertino fullname: Bertino, Elisa – sequence: 4 givenname: Wen surname: Gao fullname: Gao, Wen – sequence: 5 givenname: Bernhard orcidid: 0000-0001-9619-1558 surname: Steffen fullname: Steffen, Bernhard – sequence: 6 givenname: Gerhard orcidid: 0000-0001-8816-2693 surname: Woeginger fullname: Woeginger, Gerhard – sequence: 7 givenname: Moti surname: Yung fullname: Yung, Moti |
| SSID | ssj0002424749 ssj0002792 |
| Score | 2.5375557 |
| Snippet | The three-volume set of LNCS 12532, 12533, and 12534 constitutes the proceedings of the 27th International Conference on Neural Information Processing, ICONIP... |
| SourceID | askewsholts springer proquest |
| SourceType | Aggregation Database Publisher |
| SubjectTerms | Computer Applications Computer Science Information Systems and Communication Service Machine Learning Neural networks (Computer science)-Congresses Pattern Recognition |
| Subtitle | 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part I |
| TableOfContents | 3.2 Comparison with State-of-the-Art Method -- 3.3 Ablation Studies -- 4 Conclusion -- References -- An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences -- 1 Introduction -- 2 State of the Art -- 3 Materials and Methods -- 3.1 Material -- 3.2 Model Implementation -- 4 Results -- 4.1 Mixing All -- 4.2 Comparison with UNET3D -- 4.3 Deep Feature Extraction and Interpretation Analysis -- 5 Conclusion -- References -- Analysis of Texture Representation in Convolution Neural Network Using Wavelet Based Joint Statistics -- 1 Introduction -- 2 Methods and Materials -- 2.1 Model Description of VGG16 -- 2.2 Overview of the PSS -- 2.3 Image Dataset -- 3 Experiments and Results -- 3.1 Experiment with LASSO Regression -- 3.2 Analysis of the Synthesized Image with VGG -- 3.3 Results -- 4 Conclusion and Discussion -- References -- Auto-Classifier: A Robust Defect Detector Based on an AutoML Head -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Convolutional Neural Architectures -- 3.2 Auto-Classifier -- 4 Experiments -- 4.1 DAGM2007 -- 4.2 Results and Discussion -- 5 Conclusions -- References -- Automating Inspection of Moveable Lane Barrier for Auckland Harbour Bridge Traffic Safety -- 1 Introduction -- 2 Background -- 2.1 Deep Learning and Object Detection -- 2.2 SqueezeNet Evaluation and Architecture -- 3 Methodology -- 3.1 Design Decisions and Rationale of the Study -- 3.2 Extending Minority Class and Data Pre-processing -- 3.3 Dataset Distribution -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Bionic Vision Descriptor for Image Retrieval -- 1 Introduction -- 2 Bionic Vision Descriptor -- 2.1 Motivation -- 2.2 Original Bionic Vision Descriptor -- 2.3 Extensional Bionic Vision Descriptor -- 2.4 Feature Selection and Extraction -- 3 Experimental Results -- 4 Conclusion -- References 4.1 Comparison of Different Methods -- 4.2 Ablation Study -- 5 Conclusion -- References -- Deep Patch-Based Human Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Surface Mapping -- 2.2 Deep Learning on Human Segmentation -- 3 Method -- 3.1 Overview -- 3.2 Surface Mapping -- 3.3 Neural Network and Implementation -- 4 Experimental Results -- 4.1 Dataset Configuration -- 4.2 Evaluation Metric -- 4.3 Visual and Quantitative Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Deep Residual Local Feature Learning for Speech Emotion Recognition -- 1 Introduction -- 2 Literature Reviews -- 3 The Proposed Model -- 3.1 Raw Data Preparation -- 3.2 Voice Activity Detection -- 3.3 Bias Frame Cleaning -- 3.4 Feature Extraction -- 3.5 Deep Learning -- 4 Experiments and Discussion -- 5 Conclusion -- References -- Densely Multi-path Network for Single Image Super-Resolution -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Network Framework -- 3.2 Densely Multi-path Block -- 3.3 Reconstruction Block -- 4 Experiments -- 4.1 Datasets and Metrics -- 4.2 Training Details -- 4.3 Comparisons with State-of-the-arts -- 5 Conclusions and Future Works -- References -- Denstity Level Aware Network for Crowd Counting -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Architecture -- 3.2 Density Level Estimator -- 3.3 Loss Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Performance Comparison -- 4.4 Ablation Study -- 5 Conclusion -- References -- Difficulty Within Deep Learning Object-Recognition Due to Object Variance -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Datasets of Variances for Various NN Architectures -- 3.2 Distribution of Data with Sensitivity Analysis -- 4 Experiments -- 5 Conclusion -- References -- Drawing Dreams -- 1 Introduction -- 2 Related Work -- 3 Method 3.1 Information Extraction Image Processing and Computer Vision -- A Feature Fusion Network for Multi-modal Mesoscale Eddy Detection -- 1 Introduction -- 2 Related Work -- 2.1 Non-deep Learning Algorithms -- 2.2 Deep Learning Algorithms -- 3 Methodology -- 3.1 FusionNet -- 3.2 The Loss Function -- 4 Experiments -- 4.1 The Multi-modal Dataset -- 4.2 Experimental Results -- 5 Conclusion -- References -- A Hybrid Self-Attention Model for Pedestrians Detection -- 1 Introduction -- 2 Related Work -- 2.1 Pedestrian Detection -- 2.2 Attention Mechanism -- 3 Proposed Method -- 3.1 Revisiting the CSP Detector -- 3.2 Channel Attention -- 3.3 Spatial Attention -- 3.4 Hybrid Attention Fusion Strategy -- 4 Experiments -- 4.1 Dataset and Evaluation Metrics -- 4.2 Ablation Study -- 4.3 Comparison with State of the Arts -- 5 Conclusion -- References -- DF-PLSTM-FCN: A Method for Unmanned Driving Based on Dual-Fusions and Parallel LSTM-FCN -- 1 Introduction -- 2 Related Work -- 3 DF-PLSTM-FCN -- 3.1 Driving Model -- 3.2 Network Structure -- 3.3 Feature Fusion -- 3.4 Decision Fusion -- 3.5 Model Evaluation -- 4 Experiment -- 4.1 Dataset -- 4.2 Parameter Setting -- 4.3 Experiment Analysis -- 4.4 Evaluation Index -- 5 Conclusion -- References -- A Modified Joint Geometrical and Statistical Alignment Approach for Low-Resolution Face Recognition -- 1 Introduction -- 2 Related Work -- 3 Framework for Visual Domain Adaptation -- 3.1 Problem Description -- 3.2 Model Formulation -- 3.3 New Objective Function -- 4 Experiments -- 4.1 Benchmark Datasets -- 4.2 Experimental Results -- 4.3 Experimental Analysis -- 4.4 Parameter Sensitivity Test -- 5 Conclusion -- References -- A Part Fusion Model for Action Recognition in Still Images -- 1 Introduction -- 2 Method -- 2.1 The Guided Attention Module -- 2.2 Two-Level Classification Networks -- 3 Experiments -- 3.1 Experimental Setup Intro -- Preface -- Organization -- Contents - Part I -- Human-Computer Interaction -- A Genetic Feature Selection Based Two-Stream Neural Network for Anger Veracity Recognition -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Network Architecture -- 2.3 Two-Stream Architecture -- 2.4 Data Pre-processing and Feature Selection -- 3 Experiments and Discussions -- 3.1 Experiment Settings -- 3.2 Baseline Model -- 3.3 Experiments on GFS and Two-Stream Architecture -- 3.4 Discussion -- 4 Conclusion and Future Work -- References -- An Efficient Joint Training Framework for Robust Small-Footprint Keyword Spotting -- 1 Introduction -- 2 System Description -- 2.1 Masking-Based Speech Enhancement Method -- 2.2 Feature Transformation Block -- 2.3 Keyword Spotting System -- 3 Experiments and Results -- 3.1 Experimental Settings -- 3.2 Results -- 4 Conclusions -- References -- Hierarchical Interactive Matching Network for Multi-turn Response Selection in Retrieval-Based Chatbots -- 1 Introduction -- 2 Related Work -- 3 Hierarchical Interactive Matching Network -- 3.1 Task Description -- 3.2 Model Overview -- 3.3 Multi-level Attention Representation -- 3.4 Two-Level Hierarchical Interactive Matching -- 3.5 Aggregation -- 4 Experiments -- 4.1 Dataset -- 4.2 Evaluation Metric -- 4.3 Baseline Models -- 4.4 Experiment Settings -- 4.5 Experiment Results -- 4.6 Discussions -- 5 Conclusion -- References -- Investigation of Effectively Synthesizing Code-Switched Speech Using Highly Imbalanced Mix-Lingual Data -- 1 Introduction -- 2 Related Work -- 2.1 Data Sets for the CS TTS -- 2.2 Text Representation for CS TTS -- 3 Proposed Method -- 3.1 General Framework -- 3.2 CS Front-End -- 3.3 Synthesis Module -- 4 Data Description -- 5 Experiments -- 5.1 Input Representations -- 5.2 Experimental Setup -- 5.3 Experimental Results -- 6 Conclusion -- References Brain Tumor Segmentation from Multi-spectral MR Image Data Using Random Forest Classifier -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Pre-processing -- 2.3 Decision Making -- 2.4 Post-processing -- 2.5 Evaluation Criteria -- 3 Results and Discussion -- 4 Conclusions -- References -- CAU-net: A Novel Convolutional Neural Network for Coronary Artery Segmentation in Digital Substraction Angiography -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Feature Fusion Module -- 3.2 Pyramid Feature Concatenation -- 3.3 SE-Block -- 3.4 Loss Function -- 4 Dataset -- 5 Experiments -- 5.1 Implementation Details -- 5.2 Evaluation Metrics -- 5.3 Ablation Experiments -- 5.4 Comparing with Other Methods -- 6 Conclusion -- References -- Combining Filter Bank and KSH for Image Retrieval in Bone Scintigraphy -- 1 Introduction -- 2 Methodology -- 2.1 Texture Feature Extraction with Filter Bank -- 2.2 Supervised Retrieval with Kernels -- 3 Experiments -- 3.1 Dataset and Settings -- 3.2 Quantitative Comparison -- 3.3 Subjective Comparison -- 4 Conclusion -- References -- Contrastive Learning with Hallucinating Data for Long-Tailed Face Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Face Recognition -- 2.2 Contrastive Learning -- 3 Method -- 3.1 Framework -- 3.2 Data Hallucinating -- 3.3 Contrastive Learning -- 3.4 Training and Inference -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Performance on Constrained Datasets -- 4.3 Performance on Unconstrained Datasets -- 4.4 Ablation Studies -- 4.5 Visualization Results -- 5 Conclusion -- References -- Deep Cascade Wavelet Network for Compressed Sensing-MRI -- 1 Introduction -- 2 Methods -- 2.1 Problem Formulation -- 2.2 Overall Structure -- 2.3 Deep Wavelet Block -- 2.4 CA Layer -- 2.5 DC Layer -- 3 Experiments -- 3.1 Dataset -- 3.2 Network Training and Evaluation -- 4 Results |
| Title | Neural Information Processing |
| URI | https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=6403012 http://link.springer.com/10.1007/978-3-030-63830-6 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9783030638306 |
| Volume | 12532 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwED5BWejCW5SXIsSAhILaxPFjYABUVFWAGACxWXbiLKAi0cDAr-ccx0laGIDFihLr4tzn3MPnOwMcUdQyeZ_r0E6nkKQmxV9K9UORGDuh0KIv06NvbunogYyfkqfmFIQyu6TQp-nnj3kl_0EV7yGuNkv2D8jWRPEGXiO-2CLC2M4Zvz_qGVtYoyyVUWcg-n3_vp8dhZmeXVeBgtvXotx_deLPcvC_dtv3j_pzvr9f-5tbPWwWsGacxdi6B7ENnLZkTIwCEV0KJ2OMk4HUVjaMXSXRWkhGiVuH_CZy27sskFyIr7Bto1_qXX8EvaWELcIiE1EHls6H4-vHej3Mpqkw6wpWWtQWNnQRIDdAm5fjP8AVdGx9kA9XVxWDZwbSha6aPqPGQG1STGccibnYd2lS3K_CZpNsGdzV8K7Bgpmsw4pHKKgQWoduq3TkBuw78IMW-EED_iY8Xg3vL0dhddRFqKyBxUIjUHBmNDKGMq1YbgwZGDZIrYWso1hx5NyAZFoQFLFCc7zMIsGp4CrjOufxFnQmrxOzDUGSCcMUM0qJjGTYO1c5o5oJTngUp0kPDlsMkR8vZVh-KhuOYtODwPNJls-rvcJyeHFJifWjox4ce_5JR8GXwEZKMpbYSZbEJN35fdddWG5m-h50ird3s492X6EPqinzBcCnSsw |
| link.rule.ids | 307,310,783,787,789,792,4057,27937 |
| linkProvider | Library Specific Holdings |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Neural+Information+Processing&rft.series=Lecture+Notes+in+Computer+Science&rft.date=2020-01-01&rft.pub=Springer+International+Publishing&rft.isbn=9783030638290&rft.issn=0302-9743&rft.eissn=1611-3349&rft.volume=12532&rft_id=info:doi/10.1007%2F978-3-030-63830-6&rft.externalDocID=496057 |
| thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fvle.dmmserver.com%2Fmedia%2F640%2F97830306%2F9783030638306.jpg |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fmedia.springernature.com%2Fw306%2Fspringer-static%2Fcover-hires%2Fbook%2F978-3-030-63830-6 |