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Papers - TAMAKI Toru

Division display >> ／ All the affair displays 1 - 35 of about 35

Can masking background and object reduce static bias for zero-shot action recognition? Reviewed

Takumi Fukuzawa, Kensho Hara, Hirokatsu Kataoka, Toru Tamaki

The 31th International Conference on MultiMedia Modeling (MMM2025) 2025.01

Authorship：Last author Language：English Publishing type：Research paper (international conference proceedings)

Online Pre-Training With Long-Form Videos Reviewed

Itsuki Kato, Kodai Kamiya, Toru Tamaki

Proc. of 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE 2024) 2024.10

Authorship：Last author Language：Japanese Publishing type：Research paper (international conference proceedings)

DOI： 10.48550/arXiv.2408.15651

Other Link： https://doi.org/10.48550/arXiv.2408.15651

Shift and matching queries for video semantic segmentation

Tsubasa Mizuno, Toru Tamaki

arXiv 1 - 12 2024.10

Authorship：Last author Language：English Publishing type：Research paper (conference, symposium, etc.)

DOI： 10.48550/arXiv.2410.07635

Other Link： https://arxiv.org/abs/2410.07635

Query matching for spatio-temporal action detection with query-based object detector

Shimon Hori, Kazuki Omi, Toru Tamaki

arXiv 1 - 5 2024.09

Authorship：Last author Language：English Publishing type：Research paper (conference, symposium, etc.)

DOI： 10.48550/arXiv.2409.18408

Other Link： https://arxiv.org/abs/2409.18408

Fine-grained length controllable video captioning with ordinal embeddings

Tomoya Nitta, Takumi Fukuzawa, Toru Tamaki

arXiv 1 - 29 2024.08

Authorship：Last author Language：English Publishing type：Research paper (other academic)

DOI： 10.48550/arXiv.2408.15447

Other Link： https://arxiv.org/abs/2408.15447

セグメンテーションと画像変換を用いた動作認識のためのデータ拡張 Invited

杉浦大輝, 玉木徹

画像ラボ 35 ( 6 ) 7 - 15 2024.06

Authorship：Last author Language：Japanese Publishing type：Research paper (scientific journal)

Other Link： https://www.nikko-pb.co.jp/products/detail.php?product_id=5773

S3Aug: Segmentation, Sampling, and Shift for Action Recognition Reviewed

Taiki Sugiura, Toru Tamaki

Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP2024) 2 71 - 79 2024.02

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (international conference proceedings)

Action recognition is a well-established area of research in computer vision. In this paper, we propose S3Aug, a video data augmenatation for action recognition. Unlike conventional video data augmentation methods that involve cutting and pasting regions from two videos, the proposed method generates new videos from a single training video through segmentation and label-to-image transformation. Furthermore, the proposed method modifies certain categories of label images by sampling to generate a variety of videos, and shifts intermediate features to enhance the temporal coherency between frames of the generate videos. Experimental results on the UCF101, HMDB51, and Mimetics datasets demonstrate the effectiveness of the proposed method, paricularlly for out-of-context videos of the Mimetics dataset.

DOI： 10.5220/0012310400003660

Other Link： https://www.scitepress.org/Link.aspx?doi=10.5220/0012310400003660

Multi-model learning by sequential reading of untrimmed videos for action recognition Reviewed

Kodai Kamiya, Toru Tamaki

Proc. of The International Workshop on Frontiers of Computer Vision (IW-FCV2024) 2024.02

Authorship：Last author Language：English Publishing type：Research paper (international conference proceedings)

DOI： 10.48550/arXiv.2401.14675

Other Link： https://doi.org/10.48550/arXiv.2401.14675

Toru Tamaki, Daisuke Kayatama, Yongfei Wu, Tetsushi Koide, Shigeto Yoshida, Shin Morimoto, Yuki Okamoto, Shiro Oka, Shinji Tanaka

Visualization Algorithms of Colorectal NBI Endoscopy Images for Computer-aided Diagnosis

Proc. of The 8th International Symposium on Biomedical Engineering & International Workshop on Nanodevice Technologies 2023 2023.11

Authorship：Lead author Language：English Publishing type：Research paper (conference, symposium, etc.)

A Two-Stage Real Time Diagnosis System for Lesion Recognition in Colon NBI Endoscopy

Yongfei Wu, Daisuke Katayama, Tetsushi Koide, Toru Tamaki, Shigeto Yoshida, Shin Morimoto, Yuki Okamoto, Shiro Oka, Shinji Tanaka, Masayuki Odagawa, Toshihiko Sugihara

Proc. of The 8th International Symposium on Biomedical Engineering & International Workshop on Nanodevice Technologies 2023 2023.11

Language：English Publishing type：Research paper (conference, symposium, etc.)

A Lesion Recognition System Using Single FCN for Indicating Detailed Inference Results in Colon NBI Endoscopy

Yongfei Wu, Daisuke Katayama, Tetsushi Koide, Toru Tamaki, Shigeto Yoshida, Shin Morimoto, Yuki Okamoto, Shiro Oka, Shinji Tanaka, Masayuki Odagawa, Toshihiko Sugihara

Proc. of The 8th International Symposium on Biomedical Engineering & International Workshop on Nanodevice Technologies 2023 2023.11

Authorship：Lead author Language：English Publishing type：Research paper (conference, symposium, etc.)

Joint learning of images and videos with a single Vision Transformer Reviewed International journal

Shuki Shimizu, Toru Tamaki

Proc. of 18th International Conference on Machine Vision and Applications (MVA) 1 - 6 2023.08

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (international conference proceedings)

In this study, we propose a method for jointly learning of images and videos using a single model. In general, images and videos are often trained by separate models. We propose in this paper a method that takes a batch of images as input to Vision Transformer (IV-ViT), and also a set of video frames with temporal aggregation by late fusion. Experimental results on two image datasets and two action recognition datasets are presented.

DOI： 10.23919/MVA57639.2023.10215661

Other Link： https://ieeexplore.ieee.org/document/10215661/authors#authors

効率的な動作認識のためのシフトによる時間的な相互アテンションを用いたVision Transformer

橋口凌大, 玉木徹

画像ラボ 34 ( 5 ) 9 - 16 2023.05

Authorship：Last author,　Corresponding author Language：Japanese Publishing type：Research paper (bulletin of university, research institution)

効率的な動作認識のために時間的な相互アテンション機構を導入したマルチヘッド自己・相互アテンション(Multi-head Self/Cross-Attention、MSCA)を提案する。これは追加の計算量がなく効率的であり、ViTを時間的に拡張するために適した構造となっている。Kineitcs400を用いた実験により提案手法の有効性と、従来手法に対する優位性を示す。

Other Link： https://www.nikko-pb.co.jp/products/detail.php?product_id=5529

Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition Reviewed

Tomoya Nitta, Tsubasa Hirakawa, Hironobu Fujiyoshi, Toru Tamaki

IEICE Transactions on Information and Systems E106-D ( 3 ) 391 - 400 2023.03

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：The Institute of Electronics, Information and Communication Engineers

In this paper we propose an extension of the Attention Branch Network (ABN) by using instance segmentation for generating sharper attention maps for action recognition. Methods for visual explanation such as Grad-CAM usually generate blurry maps which are not intuitive for humans to understand, particularly in recognizing actions of people in videos. Our proposed method, Object-ABN, tackles this issue by introducing a new mask loss that makes the generated attention maps close to the instance segmentation result. Further the Prototype Conformity (PC) loss and multiple attention maps are introduced to enhance the sharpness of the maps and improve the performance of classification. Experimental results with UCF101 and SSv2 shows that the generated maps by the proposed method are much clearer qualitatively and quantitatively than those of the original ABN.

DOI： 10.1587/transinf.2022EDP7138

Other Link： https://www.jstage.jst.go.jp/article/transinf/E106.D/3/E106.D_2022EDP7138/_article

ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for Action Recognition Reviewed International journal

Jun Kimata, Tomoya Nitta, Toru Tamaki

ACM MM 2022 Asia (MMAsia '22) 2022.12

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (international conference proceedings)

DOI： 10.1145/3551626.3564941

Other Link： https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1145%2F3551626.3564941&sa=D&sntz=1&usg=AOvVaw2jqzXXsG8MZbwSm67eCcjm

Temporal Cross-attention for Action Recognition Reviewed International journal

Ryota Hashiguchi, Toru Tamaki

2022.12

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (international conference proceedings)

Feature shifts have been shown to be useful for action recognition with CNN-based models since Temporal Shift Module (TSM) was proposed. It is based on frame-wise feature extraction with late fusion, and layer features are shifted along the time direction for the temporal interaction. TokenShift, a recent model based on Vision Transformer (ViT), also uses the temporal feature shift mechanism, which, however, does not fully exploit the structure of Multi-head Self-Attention (MSA) in ViT. In this paper, we propose Multi-head Self/Cross-Attention (MSCA), which fully utilizes the attention structure. TokenShift is based on a frame-wise ViT with features temporally shifted with successive frames (at time t+1 and t-1). In contrast, the proposed MSCA replaces MSA in the frame-wise ViT, and some MSA heads attend to successive frames instead of the current frame. The computation cost is the same as the frame-wise ViT and TokenShift as it simply changes the target to which the attention is taken. There is a choice about which of key, query, and value are taken from the successive frames, then we experimentally compared these variants with Kinetics400. We also investigate other variants in which the proposed MSCA is used along the patch dimension of ViT, instead of the head dimension. Experimental results show that a variant, MSCA-KV, shows the best performance and is better than TokenShift by 0.1% and then ViT by 1.2%.

Other Link： https://openaccess.thecvf.com/menu_other.html

Model-agnostic Multi-Domain Learning with Domain-Specific Adapters for Action Recognition Reviewed International journal

Kazuki Omi, Jun Kimata, Toru Tamaki

IEICE Transactions on Information and Systems E105-D ( 12 ) 2022.12

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：IEICE

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 net- work. 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.

DOI： 10.1587/transinf.2022EDP7058

Other Link： https://search.ieice.org/bin/summary_advpub.php?id=2022EDP7058&category=D&lang=E&abst=

動作行動認識の最前線:手法，タスク，データセット Invited

玉木徹

画像応用技術専門委員会研究会報告 34 ( 4 ) 1 - 20 2022.11

Authorship：Lead author,　Last author,　Corresponding author Language：Japanese Publishing type：Research paper (conference, symposium, etc.)

Other Link： http://www.tc-iaip.org/research/

Performance Evaluation of Action Recognition Models on Low Quality Videos Reviewed International journal

Aoi Otani, Ryota Hashiguchi, Kazuki Omi, Norishige Fukushima, Toru Tamaki

IEEE Access 10 94898 - 94907 2022.09

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：IEEE

In the design of action recognition models, the quality of videos is an important issue; however, the trade-off between the quality and performance is often ignored. In general, action recognition models are trained on high-quality videos, hence it is not known how the model performance degrades when tested on low-quality videos, and how much the quality of training videos affects the performance. The issue of video quality is important, however, it has not been studied so far. The goal of this study is to show the trade-off between the performance and the quality of training and test videos by quantitative performance evaluation of several action recognition models for transcoded videos in different qualities. First, we show how the video quality affects the performance of pre-trained models. We transcode the original validation videos of Kinetics400 by changing quality control parameters of JPEG (compression strength) and H.264/AVC (CRF). Then we use the transcoded videos to validate the pre-trained models. Second, we show how the models perform when trained on transcoded videos. We transcode the original training videos of Kinetics400 by changing the quality parameters of JPEG and H.264/AVC. Then we train the models on the transcoded training videos and validate them with the original and transcoded validation videos. Experimental results with JPEG transcoding show that there is no severe performance degradation (up to −1.5%) for compression strength smaller than 70 where no quality degradation is visually observed, and for larger than 80 the performance degrades linearly with respect to the quality index. Experiments with H.264/AVC transcoding show that there is no significant performance loss (up to −1%) with CRF30 while the total size of video files is reduced to 30%. In summary, the video quality doesn’t have a large impact on the performance of action recognition models unless the quality degradation is severe and visible. This enables us to transcode the tr...

DOI： 10.1109/ACCESS.2022.3204755

Other Link： https://ieeexplore.ieee.org/document/9878331

Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition International journal

Tomoya Nitta, Tsubasa Hirakawa, Hironobu Fujiyoshi, Toru Tamaki

2022.07

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (other academic)

In this paper we propose an extension of the Attention Branch Network (ABN) by using instance segmentation for generating sharper attention maps for action recognition. Methods for visual explanation such as Grad-CAM usually generate blurry maps which are not intuitive for humans to understand, particularly in recognizing actions of people in videos. Our proposed method, Object-ABN, tackles this issue by introducing a new mask loss that makes the generated attention maps close to the instance segmentation result. Further the PC loss and multiple attention maps are introduced to enhance the sharpness of the maps and improve the performance of classification. Experimental results with UCF101 and SSv2 shows that the generated maps by the proposed method are much clearer qualitatively and quantitatively than those of the original ABN.

DOI： 10.48550/arXiv.2207.13306

Other Link： https://doi.org/10.48550/arXiv.2207.13306

On the Performance Evaluation of Action Recognition Models on Transcoded Low Quality Videos International journal

Aoi Otani, Ryota Hashiguchi, Kazuki Omi, Norishige Fukushima, Toru Tamaki

2022.04

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (other academic)

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.

DOI： 10.48550/arXiv.2204.09166

Other Link： https://doi.org/10.48550/arXiv.2204.09166

Model-agnostic Multi-Domain Learning with Domain-Specific Adapters for Action Recognition International journal

Kazuki Omi, Toru Tamaki

2022.04

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (other academic)

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.

DOI： 10.48550/arXiv.2204.07270

Other Link： https://doi.org/10.48550/arXiv.2204.07270

Vision Transformer with Cross-attention by Temporal Shift for Efficient Action Recognition International journal

Ryota Hashiguchi, Toru Tamaki

2022.04

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (other academic)

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.

DOI： 10.48550/arXiv.2204.00452

Other Link： https://doi.org/10.48550/arXiv.2204.00452

ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for Action Recognition International journal

Jun Kimata, Tomoya Nitta, Toru Tamaki

2022.04

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (other academic)

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.

DOI： 10.48550/arXiv.2204.00239

Other Link： https://doi.org/10.48550/arXiv.2204.00239

On the Instability of Unsupervised Domain Adaptation with ADDA Reviewed International journal

Kazuki Omi and Toru Tamaki

International Workshop on Advanced Image Technology (IWAIT2022) 2022.01

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (international conference proceedings)

DOI： 10.1117/12.2625953

Other Link： https://doi.org/10.1117/12.2625953

Estimating the number of Table Tennis Rallies in a Match Video Reviewed International journal

Shoma Kato, Akira Kito, Toru Tamaki and Hiroaki Sawano

International Workshop on Advanced Image Technology (IWAIT2022) 2022.01

Language：English Publishing type：Research paper (international conference proceedings)

DOI： 10.1117/12.2625945

Other Link： https://doi.org/10.1117/12.2625945

Classification with CNN features and SVM on Embedded DSP Core for Colorectal Magnified NBI Endoscopic Video Image Reviewed International journal

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 2022.01

Language：English Publishing type：Research paper (scientific journal)

DOI： 10.1587/transfun.2021EAP1036

Other Link： https://www.jstage.jst.go.jp/article/transfun/E105.A/1/E105.A_2021EAP1036/_article

Development of multi-class computer-aided diagnostic systems using the NICE/JNET classifications for colorectal lesions Reviewed International journal

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 2022.01

Language：English Publishing type：Research paper (scientific journal) Publisher：Wiley

DOI： 10.1111/jgh.15682

Other Link： https://onlinelibrary.wiley.com/doi/10.1111/jgh.15682

Feasibility Study for Computer-Aided Diagnosis System with Navigation Function of Clear Region for Real-Time Endoscopic Video Image on Customizable Embedded DSP Cores Reviewed International journal

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 2022.01

Language：English Publishing type：Research paper (scientific journal)

DOI： 10.1587/transfun.2021EAL2044

Other Link： https://www.jstage.jst.go.jp/article/transfun/E105.A/1/E105.A_2021EAL2044/_article

Localization of Flying Bats from Multichannel Audio Signals by Estimating Location Map with Convolutional Neural Networks Reviewed

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 2021.06

Language：English Publishing type：Research paper (scientific journal) Publisher：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.

DOI： 10.20965/jrm.2021.p0515

Other Link： https://www.fujipress.jp/jrm/rb/robot003300030515/

A Hardware Implementation on Customizable Embedded DSP Core for Colorectal Tumor Classification with Endoscopic Video toward Real-Time Computer-Aided Diagnosis System Reviewed

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 2021.04

Language：English Publishing type：Research paper (scientific journal) Publisher：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.

DOI： 10.1587/transfun.2020EAP1069

Spectral Rendering of Fluorescence on Translucent Materials Reviewed

Masaya Kugita, Kazufumi Kaneda, Bisser Raytchev, Toru Tamaki

20 ( 1 ) 30 - 39 2021.03

Language：Japanese Publishing type：Research paper (scientific journal)

To render fluorescence, a wavelength dependent phenomena, we need to take into account the spectral distribution of light. Moreover, for translucent fluorescent medium we need to consider subsurface scattering. We propose a spectral rendering method to render fluorescence on translucent materials under global illumination environment. The proposed method is based on the physical properties of the fluorescence phenomena and rendered in a Probabilistic Progressive Photon Mapping method. By separating the power of photons into 3 elements (fluorescence, single scattering, multiple scattering), our method realizes fluorescence taking into account the scattering and absorption of light under the surface. We also introduce the Photon Power Table used for calculating the illuminance efficiently and deciding the outgoing point of light probabilistically. Finally, we show the usefulness of our method by demonstrating the rendered image.

Other Link： https://www.art-science.org/journal/v20n1/v20n1pp30/artsci-v20n1pp30.pdf

Rephrasing visual questions by specifying the entropy of the answer distribution Reviewed

Kento Terao, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Shin’Ichi Satoh

IEICE TRANSACTIONS on Information and Systems E103-D ( 11 ) 2362 - 2370 2020.11

Language：English Publishing type：Research paper (scientific journal) Publisher：IEICE

DOI： 10.1587/transinf.2020EDP7089

arXiv

Other Link： https://search.ieice.org/bin/summary.php?id=e103-d_11_2362&category=D&year=2020&lang=E&abst=

An Entropy Clustering Approach for Assessing Visual Question Difficulty Reviewed International journal

Kento Terao, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Shin'ichi Satoh

IEEE Access 8 180633 - 180645 2020.09

Language：English Publishing type：Research paper (scientific journal) Publisher：IEEE