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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RISING (3rd) |
2023-10-31 13:00 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Few Shot Learning-Driven Traffic Forecast for 5G VNF Scaling Qianqian Pan, Akihiro Nakao (The Univ. of Tokyo) |
Virtual network functions (VNFs) make 5G networks more feasible to the diverse and heterogeneous communication environme... [more] | |
| DE, IPSJ-DBS, IPSJ-IFAT [detail] | 2023-09-22 09:00 |
Fukuoka | Kitakyushu International Conference Center |  DE2023-18 |
(To be available after the conference date) [more] | DE2023-18 pp.42-47 |
| NLC | 2023-09-07 16:20 |
Osaka | Osaka Metropolitan University. Nakamozu Campus. (Primary: On-site, Secondary: Online) |
Estimation of sentence boundaries in texts on business performance Kaito Takano, Kei Nakagawa (NAM), Hiroyuki Sakai (Seikei Univ.) NLC2023-13 |
In order to maintain a healthy market for financial instruments, listed companies are required to disclose corporate inf... [more] | NLC2023-13 pp.69-74 |
| NLC, IPSJ-NL | 2023-03-18 11:05 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
Estimating Named Entity Label Representation for Generative Low-Resource NER Yuya Sawada (NAIST), Hiroki Teranishi (RIKEN AIP), Hiroki Ouchi (NAIST), Yuji Matsumoto (RIKEN AIP), Taro Watanabe (NAIST) NLC2022-22 |
Named entity recognition (NER) system needs to identify the entities of novel entity types with fewer examples. Few-shot... [more] | NLC2022-22 pp.16-21 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 11:05 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
A Study of Word Lip-Reading using Meta Learning Michinari Kodama, Takeshi Saitoh (kyutech) PRMU2022-77 IBISML2022-84 |
Lip-reading technology, which estimates utterance content using only visual information, is a kind of supervised learnin... [more] | PRMU2022-77 IBISML2022-84 pp.102-106 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 09:20 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
A Study of Few-shot NeRF by Pseudo-Feature Vectors Evaluation for Unknown Viewpoints Daiju Kanaoka (Kyutech/RIKEN), Motoharu Sonogashira (RIKEN), Hakaru Tamukoh (Kyutech/Neumorph Center), Yasutomo Kawanishi (RIKEN) PRMU2022-101 IBISML2022-108 |
Neural Radiance Fields (NeRF) is a powerful method for novel view synthesis. However, NeRF requires a large number of ... [more] |
PRMU2022-101 IBISML2022-108 pp.220-225 |
| SIS | 2022-03-03 14:45 |
Online | Online | Few-Shot Music Artist Classification Tianshuai Yu, Yoshimasa Tsuruoka (Tokyo Univ.) SIS2021-36 |
Music artist classification is known as a task in the field of Music Information Retrieval(MIR). Recently, due to the im... [more] | SIS2021-36 pp.32-37 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-02 15:35 |
Okinawa | (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Interpolation of head-related transfer function from small amount of observation data using deep learning based on spherical wavefunction expansion Yuki Ito, Tomohiko Nakamura, Shoichi Koyama, Hiroshi Saruwatari (UTokyo) EA2021-90 SIP2021-117 SP2021-75 |
In binaural synthesis, listeners' individual head-related transfer functions (HRTFs) are necessary for highly-immersive ... [more] | EA2021-90 SIP2021-117 SP2021-75 pp.163-170 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 14:25 |
Online | Online | A Note on Visual Sentiment Prediction Based on Few-shot Learning using Knowledge Distillation Yingrui Ye, Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
The prediction of visual sentiment can be useful to understand users' behaviors. Emotion theories underlying the sentime... [more] | |
| PRMU | 2020-12-17 14:40 |
Online | Online | Belonging Network
-- Few-shot One-class Image Classification for Classes with Various Distributions -- Takumi Ohkuma, Hideki Nakayama (UT) PRMU2020-44 |
Few-shot one-class image classification is the task of recognizing a particular class while rejecting test images that d... [more] | PRMU2020-44 pp.36-41 |
| PRMU | 2020-12-17 16:20 |
Online | Online | [Short Paper]
Few-Shot Incremental Learning by Unifying with Variational Autoencoder Keita Takayama, Kuniaki Uto, Koichi Shinoda (TokyoTech) PRMU2020-48 |
We propose a few-shot incremental learning method using a variational autoencoder for deep learning. In incremental lear... [more] | PRMU2020-48 pp.58-62 |
| PRMU | 2020-12-18 14:55 |
Online | Online | Regularization Using Knowledge Distillation in Learning Small Datasets Ryota Higashi, Toshikazu Wada (Wakayama Univ.) PRMU2020-61 |
Knowledge distillation is a method mainly used for compressing deep learning models, but it has recently gained attentio... [more] | PRMU2020-61 pp.133-138 |
| ICM | 2020-07-17 09:25 |
Online | Online | Study on which data we should label in a few-shot learning for service identification over encrypted web services Shouta Yoshida, Yutaka Eguchi, Kohei Shiomoto (TCU) ICM2020-14 |
It is very important to monitor and control the communication traffic to cope with the increasing communication traffic... [more] |
ICM2020-14 pp.37-42 |
| PRMU, IPSJ-CVIM | 2020-03-16 11:00 |
Kyoto | (Cancelled but technical report was issued) |
[Short Paper]
Few-shot Character Image Generation with Deep Metric Learning Haruka Aoki, Koki Tsubota, Hikaru Ikuta, Kiyoharu Aizawa (Tokyo Univ.) PRMU2019-66 |
(To be available after the conference date) [more] | PRMU2019-66 pp.11-12 |
| SeMI, RCS, NS, SR, RCC (Joint) |
2019-07-11 15:25 |
Osaka | I-Site Nanba（Osaka） | Few-shot Learning based on Prototypical Network to Understand Area Service Level in LTE Networks Shogo Aoki (Waseda Univ.), Kohei Shiomoto (TCU), Chin Lam Eng, Sebastian Backstad (Ericsson Japan) RCC2019-42 NS2019-78 RCS2019-135 SR2019-54 SeMI2019-51 |
In case a base station in mobile network malfunction, it is crucial to classify a service degradation event and identify... [more] | RCC2019-42 NS2019-78 RCS2019-135 SR2019-54 SeMI2019-51 pp.151-156(RCC), pp.177-182(NS), pp.173-178(RCS), pp.183-188(SR), pp.165-170(SeMI) |
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