論文 - 玉木 徹
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On the Performance Evaluation of Action Recognition Models on Transcoded Low Quality Videos 国際誌
Aoi Otani, Ryota Hashiguchi, Kazuki Omi, Norishige Fukushima, Toru Tamaki
2022年04月
担当区分：最終著者, 責任著者 記述言語：英語 掲載種別：研究論文（その他学術会議資料等）
In the design of action recognition models, the quality of videos in the dataset is an important issue, however the trade-off between the quality and performance is often ignored. In general, action recognition models are trained and tested on high-quality videos, but in actual situations where action recognition models are deployed, sometimes it might not be assumed that the input videos are of high quality. In this study, we report qualitative evaluations of action recognition models for the quality degradation associated with transcoding by JPEG and H.264/AVC. Experimental results are shown for evaluating the performance of pre-trained models on the transcoded validation videos of Kinetics400. The models are also trained on the transcoded training videos. From these results, we quantitatively show the degree of degradation of the model performance with respect to the degradation of the video quality.
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Model-agnostic Multi-Domain Learning with Domain-Specific Adapters for Action Recognition 国際誌
Kazuki Omi, Toru Tamaki
2022年04月
担当区分：最終著者, 責任著者 記述言語：英語 掲載種別：研究論文（その他学術会議資料等）
In this paper, we propose a multi-domain learning model for action recognition. The proposed method inserts domain-specific adapters between layers of domain-independent layers of a backbone network. Unlike a multi-head network that switches classification heads only, our model switches not only the heads, but also the adapters for facilitating to learn feature representations universal to multiple domains. Unlike prior works, the proposed method is model-agnostic and doesn't assume model structures unlike prior works. Experimental results on three popular action recognition datasets (HMDB51, UCF101, and Kinetics-400) demonstrate that the proposed method is more effective than a multi-head architecture and more efficient than separately training models for each domain.
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Vision Transformer with Cross-attention by Temporal Shift for Efficient Action Recognition 国際誌
Ryota Hashiguchi, Toru Tamaki
2022年04月
担当区分：最終著者, 責任著者 記述言語：英語 掲載種別：研究論文（その他学術会議資料等）
We propose Multi-head Self/Cross-Attention (MSCA), which introduces a temporal cross-attention mechanism for action recognition, based on the structure of the Multi-head Self-Attention (MSA) mechanism of the Vision Transformer (ViT). Simply applying ViT to each frame of a video frame can capture frame features, but cannot model temporal features. However, simply modeling temporal information with CNN or Transfomer is computationally expensive. TSM that perform feature shifting assume a CNN and cannot take advantage of the ViT structure. The proposed model captures temporal information by shifting the Query, Key, and Value in the calculation of MSA of ViT. This is efficient without additional coinformationmputational effort and is a suitable structure for extending ViT over temporal. Experiments on Kineitcs400 show the effectiveness of the proposed method and its superiority over previous methods.
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ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for Action Recognition 国際誌
Jun Kimata, Tomoya Nitta, Toru Tamaki
2022年04月
担当区分：最終著者, 責任著者 記述言語：英語 掲載種別：研究論文（その他学術会議資料等）
In this paper, we propose a data augmentation method for action recognition using instance segmentation. Although many data augmentation methods have been proposed for image recognition, few methods have been proposed for action recognition. Our proposed method, ObjectMix, extracts each object region from two videos using instance segmentation and combines them to create new videos. Experiments on two action recognition datasets, UCF101 and HMDB51, demonstrate the effectiveness of the proposed method and show its superiority over VideoMix, a prior work.
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On the Instability of Unsupervised Domain Adaptation with ADDA 査読あり 国際誌
Kazuki Omi and Toru Tamaki
International Workshop on Advanced Image Technology (IWAIT2022) 2022年01月
担当区分：最終著者, 責任著者 記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
DOI： 10.1117/12.2625953
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Estimating the number of Table Tennis Rallies in a Match Video 査読あり 国際誌
Shoma Kato, Akira Kito, Toru Tamaki and Hiroaki Sawano
International Workshop on Advanced Image Technology (IWAIT2022) 2022年01月
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Classification with CNN features and SVM on Embedded DSP Core for Colorectal Magnified NBI Endoscopic Video Image 査読あり 国際誌
Masayuki Odagawa, Takumi Okamoto, Tetsushi Koide, Toru Tamaki, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E105-A ( 1 ) 25 - 34 2022年01月
記述言語：英語 掲載種別：研究論文（学術雑誌）
DOI： 10.1587/transfun.2021EAP1036
その他リンク： https://www.jstage.jst.go.jp/article/transfun/E105.A/1/E105.A_2021EAP1036/_article
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Development of multi-class computer-aided diagnostic systems using the NICE/JNET classifications for colorectal lesions 査読あり 国際誌
Yuki Okamoto, Shigeto Yoshida, Seiji Izakura, Daisuke Katayama, Ryuichi Michida, Tetsushi Koide, Toru Tamaki, Yuki Kamigaichi, Hirosato Tamari, Yasutsugu Shimohara, Tomoyuki Nishimura, Katsuaki Inagaki, Hidenori Tanaka, Ken Yamashita, Kyoku Sumimoto, Shiro Oka, Shinji Tanaka
Journal of Gastroenterology and Hepatology 37 ( 1 ) 104 - 110 2022年01月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：Wiley
DOI： 10.1111/jgh.15682
その他リンク： https://onlinelibrary.wiley.com/doi/10.1111/jgh.15682
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Feasibility Study for Computer-Aided Diagnosis System with Navigation Function of Clear Region for Real-Time Endoscopic Video Image on Customizable Embedded DSP Cores 査読あり 国際誌
Masayuki Odagawa, Tetsushi Koide, Toru Tamaki, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 1 ( E105-A ) 58 - 62 2022年01月
記述言語：英語 掲載種別：研究論文（学術雑誌）
DOI： 10.1587/transfun.2021EAL2044
その他リンク： https://www.jstage.jst.go.jp/article/transfun/E105.A/1/E105.A_2021EAL2044/_article
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Localization of Flying Bats from Multichannel Audio Signals by Estimating Location Map with Convolutional Neural Networks 査読あり
Kazuki Fujimori, Bisser Raytchev, Kazufumi Kaneda, Yasufumi Yamada, Yu Teshima, Emyo Fujioka, Shizuko Hiryu, and Toru Tamaki
Journal of Robotics and Mechatronics 33 ( 3 ) 515 - 525 2021年06月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：Fuji Technology Press Ltd
We propose a method that uses ultrasound audio signals from a multichannel microphone array to estimate the positions of flying bats. The proposed model uses a deep convolutional neural network that takes multichannel signals as input and outputs the probability maps of the locations of bats. We present experimental results using two ultrasound audio clips of different bat species and show numerical simulations with synthetically generated sounds.
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A Hardware Implementation on Customizable Embedded DSP Core for Colorectal Tumor Classification with Endoscopic Video toward Real-Time Computer-Aided Diagnosis System 査読あり
Masayuki ODAGAWA, Takumi OKAMOTO, Tetsushi KOIDE, Toru TAMAKI, Bisser RAYTCHEV, Kazufumi KANEDA, Shigeto YOSHIDA, Hiroshi MIENO, Shinji TANAKA, Takayuki SUGAWARA, Hiroshi TOISHI, Masayuki TSUJI, Nobuo TAMBA
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E104-A ( 4 ) 691 - 701 2021年04月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：IEICE
In this paper, we present a hardware implementation of a colorectal cancer diagnosis support system using a colorectal endoscopic video image on customizable embedded DSP. In an endoscopic video image, color shift, blurring or reflection of light occurs in a lesion area, which affects the discrimination result by a computer. Therefore, in order to identify lesions with high robustness and stable classification to these images specific to video frame, we implement a computer-aided diagnosis (CAD) system for colorectal endoscopic images with Narrow Band Imaging (NBI) magnification with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification. Since CNN and SVM need to perform many multiplication and accumulation (MAC) operations, we implement the proposed hardware system on a customizable embedded DSP, which can realize at high speed MAC operations and parallel processing with Very Long Instruction Word (VLIW). Before implementing to the customizable embedded DSP, we profile and analyze processing cycles of the CAD system and optimize the bottlenecks. We show the effectiveness of the real-time diagnosis support system on the embedded system for endoscopic video images. The prototyped system demonstrated real-time processing on video frame rate (over 30fps @ 200MHz) and more than 90% accuracy.
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表面下散乱を考慮した蛍光現象のスペクトラルレンダリング 査読あり
釘田尚弥, 金田和文, ライチェフビセル, 玉木徹
芸術科学会論文誌 20 ( 1 ) 30 - 39 2021年03月
記述言語：日本語 掲載種別：研究論文（学術雑誌）
波長依存性の高い蛍光現象を表現するためには光のスペクトルを考慮してレンダリングを行う必要がある．さらに，蛍光物質を含有した半透明媒質では表面下散乱を考慮した蛍光現象のレンダリングが必要となる．本論文では大域照明環境下における表面下散乱を考慮した蛍光現象のスペクトラルレンダリング手法を提案する．提案手法では，蛍光現象の物理的特性に基づきPPPM(確率的漸進的フォトンマッピング)法を用いてレンダリングを行う．光の成分を蛍光，単散乱光，多重散乱光の３成分に分けてフォトンマップに格納することにより，表面下での光の散乱・吸収を考慮した蛍光現象を表示する．計算効率化と表面下からの光の出射点を確率的に決定するために新たにフォトンパワーテーブルを導入する．提案手法を用いて蛍光物質を含有した半透明媒質をレンダリングし，その有用性を示す．
その他リンク： https://www.art-science.org/journal/v20n1/v20n1pp30/artsci-v20n1pp30.pdf
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Rephrasing visual questions by specifying the entropy of the answer distribution 査読あり
Kento Terao, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Shin’Ichi Satoh
IEICE TRANSACTIONS on Information and Systems E103-D ( 11 ) 2362 - 2370 2020年11月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：IEICE
DOI： 10.1587/transinf.2020EDP7089
その他リンク： https://search.ieice.org/bin/summary.php?id=e103-d_11_2362&category=D&year=2020&lang=E&abst=
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An Entropy Clustering Approach for Assessing Visual Question Difficulty 査読あり 国際誌
Kento Terao, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Shin'ichi Satoh
IEEE Access 8 180633 - 180645 2020年09月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：IEEE
