Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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Computational measurement of perceived pointiness from pronunciation Reviewed Open Access

Chihaya Matsuhira, Marc A. Kastner, Takahiro Komamizu, Ichiro Ide, Takatsugu Hirayama, Yasutomo Kawanishi, Keisuke Doman, Daisuke Deguchi

Multimedia Tools and Applications 2023.8

Refined Objectification for Improving End-to-End Driving Model Explanation Persuasibility Reviewed

Chenkai Zhang, Daisuke Deguchi, Hiroshi Murase

Proceedings of 2023 IEEE Intelligent Vehicles Symposium (IV2023) 2023.6

Estimation of targets' locations and attention degrees by spatio-temporal integration of audiences' facial orientations Reviewed

Kazuma Takeda, Yasutomo Kawanishi, Takatsugu Hirayama, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Kunio Kashino

IEICE Transactions on Information and Systems Vol. J106-A ( 3 ) page： 58 - 69 2023.3

End-to-End gaze grounding from a person pictured from behind Reviewed

Hayato Yumiya, Daisuke Deguchi, Yasutomo Kawanishi, Hiroshi Murase

Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Vol. 5 page： 898 - 905 2023.2

Subjective Baggage-Weight Estimation from Gait: Can You Estimate How Heavy the Person Feels? Reviewed

Masaya Mizuno, Tomohiro Fujita, Yasutomo Kawanishi, Daisuke Deguchi, Hiroshi Murase

Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) 2023 Vol. 5 page： 567 - 574 2023.2

A Preliminary Study on View Independent Panoptic Scene Change Detection Reviewed

Jiaxin Li, Yasutomo Kawanishi, Daisuke Deguchi, Hiroshi Murase

Proceedings of International Workshop on Advanced Image Technology (IWAIT) 2023 2023.1

More Persuasive Explanation Method for End-to-End Driving Models Reviewed Open Access

Chenkai Zhang, Daisuke Deguchi, Yuki Okafuji, Hiroshi Murase

IEEE Access Vol. 11 page： 4270 - 4282 2023.1

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

With the rapid development of autonomous driving technology, a variety of high-performance end-to-end driving models (E2EDMs) are being proposed. In order to understand the computational methods of E2EDMs, pixel-level explanations methods are used to obtain the explanations of the E2EDMs. However, little attention has been paid to the excellence of the explanations of E2EDMs. Therefore, in order to build trustworthy E2EDMs, we focus on improving the persuasibility of the explanations of E2EDMs. We propose an object-level explanation method (main approach) for E2EDMs, which masks the objects in the image and then treats the change in the prediction result as the importance of the objects, then we explain the E2EDM by the importance of each object. To further validate the effectiveness of object-level explanations, we propose another approach (validation approach), which trains E2EDMs with object information as input and generates the importance of objects using general explanation methods. Both approaches generate object-level explanations, in order to compare these object-level explanations with traditional pixel-level explanations, we propose experimental methods to measure the persuasibility of explanations of E2EDMs through a subjective and objective method. The subjective method evaluates persuasibility based on the extent to which participants think the importance of features indicated by the explanations is correct. The objective method evaluates the persuasibility based on the human annotation similarity between provided with only the important part of images and provided with the complete images. The experimental results show that the object-level explanations are more persuasive than the traditional pixel-level explanations.

DOI： 10.1109/ACCESS.2023.3235739

Open Access

Analysis of Gaze while Viewing Videos at Various Playback Speeds Reviewed

Hideaki Takahira, Yasutomo Kawanishi, Takatsugu Hirayama, Hiroshi Murase, Daisuke Deguchi

Proceedings of the International Display Workshops Vol. 29 page： 702 - 705 2022.12

Detection of Birds in a 3D Environment Referring to Audio-Visual Information Reviewed

Yasutomo Kawanishi, Ichiro Ide, Baidong Chu, Chihaya Matsuhira, Marc A. Kastner, Takahiro Komamizu, Daisuke Deguchi

Proceedings of the 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance page： 1 - 7 2022.11

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

We propose a method to detect birds in a 3D environment referring to both audio information observed from a microphone array and visual information observed from a panorama camera. In general, in panorama images, birds appear relatively too small to be detected accurately even with the state-of-the-art deep learning models. Thus, the proposed method takes a two step approach where the birds are first roughly located referring to audio information by Sound Source Localization (SSL), and then image detection is applied within its vicinity. Through evaluation on a dataset annotated with bounding boxes surrounding the birds, we show that the proposed method improves detection performance of birds that appear in relatively small sizes in the image, in both accuracy and processing speed.

DOI： 10.1109/AVSS56176.2022.9959510

Detection of distant eye-contact using spatio-temporal pedestrian skeletons Reviewed

Ryusei Hata, Daisuke Deguchi, Takatsugu Hirayama, Yasutomo Kawanishi, Hiroshi Murase

2022.9

Analysis of Gaze Point Distribution while Viewing Surround-Sound Videos Reviewed Open Access

Hideaki Takahira, Miho Shinohara, Yusuke Nosaka, Masaya Yokouchi, Mitsuho Yamada, Takatsugu Hirayama, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

The Transactions of Human Interface Society Vol. 24 ( 3 ) page： 195 - 204 2022.8

Two-Stage Input-Output Hidden Markov Model for Car-Pedestrian Interaction Behavior Modeling Reviewed Open Access

Fumito Shinmura, Naoki Akai, Takatsugu Hirayama, HaiLong Liu, Yasutomo Kawanishi, Daisuke Deguchi, Hiroshi Murase

Journal of Information Processing Vol. 63 ( 8 ) page： 1371 - 1382 2022.8

Intuitive Gait Modeling using Mimetic-Words for Gait Description and Generation Reviewed

Hirotaka Kato, Takatsugu Hirayama, Keisuke Doman, Ichiro Ide, Yasutomo Kawanishi, Takahiro Komamizu, Daisuke Deguchi, Hiroshi Murase

page： 240 - 245 2022.8

Context-Aware Contribution Estimation for Feature Aggregation in Video Face Recognition Reviewed Open Access

Meng Zhang, Rujie Liu, Daisuke Deguchi, Hiroshi Murase

IEEE Access Vol. 10 page： 79301 - 79310 2022.7

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

The difficulties in video-based face recognition, such as dramatic pose variations and low quality, can be alleviated by leveraging the rich complementary information between the frames. However, limited by the mini-batch training strategy, the current deep learning methods only utilizes the frames in each batch during training, which ignore the context of the entire video. In this paper, we propose a context-aware feature aggregation scheme to aggregate complementary information between different frames. Firstly, a two-branch structure is designed as the C ontext-aware feature A ggregation N etwork ( CAN ). Secondly, a context-aware training strategy using a context bank is proposed, which alleviates the limitation of mini-batch samples by using the context of the entire video or several images belonging to the same ID and thus achieves global contribution estimation result. Comparative studies on benchmark datasets, such as IJB-C, YouTube Face (YTF), PaSC and COX, confirm that the proposed approach can achieve state-of-the-art level. Meanwhile, qualitative analysis on Multi-PIE dataset indicates that the contribution learned by the CAN is reasonable and beneficial to video face recognition.

DOI： 10.1109/ACCESS.2022.3193787

Open Access

SfM-Student: Semantic Segmentation of Train Front-View Images with SfM Data Augmentation Reviewed

Yuki Furitsu, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase, Hiroki Mukoujima, Nozomi Nagamine

IEICE Transactions on Information and Systems Vol. J105-A ( 4 ) page： 48 - 57 2022.4

Safety Margin Prediction for Pedestrians Using Risk Potential Function Considering Pedestrian Attributes and Driver Skill Reviewed

Yamato Maekawa, Takatsugu Hirayama, Yasutomo Kawanishi, Daisuke Deguchi, HaiLong Liu, Ichiro Ide, Hiroshi Murase

IEICE Transactions on Information and Systems Vol. J105-A ( 3 ) page： 38 - 42 2022.3

Masked face recognition with mask transfer and self-attention under the COVID-19 pandemic Reviewed Open Access

Zhang Meng, Rujie Liu, Daisuke Deguchi, Hiroshi Murase

IEEE Access Vol. 10 page： 20527 - 20538 2022.2

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

Face masks bring a new challenge to face recognition systems especially against the background of the COVID-19 pandemic. In this paper, a method mitigating the negative effects of mask defects on face recognition is proposed. Firstly, a low-cost, accurate method of masked face synthesis, i.e. mask transfer, is proposed for data augmentation. Secondly, an attention-aware masked face recognition (AMaskNet) is proposed to improve the performance of masked face recognition, which includes two modules: a feature extractor and a contribution estimator. Therein, the contribution estimator is employed to learn the contribution of the feature elements, thus achieving refined feature representation by simple matrix multiplications. Meanwhile, the end-to-end training strategy is utilized to optimize the entire model. Finally, a mask-aware similarity matching strategy(MS) is taken to improve the performance in the inference stage. The experiments show that the proposed method consistently outperforms on three masked face recognition datasets: RMFRD [1], COX [2] and Public-IvS [3]. Meanwhile, qualitative analysis experiments using CAM [4] indicate that the contribution learned by AMaskNet is more conducive to masked face recognition.

DOI： 10.1109/ACCESS.2022.3150345

Open Access

Active Learning for Human Pose Estimation based on Temporal Pose Continuity Reviewed

Taro Mori, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase, Tetsuo Inoshita

Proceedings of International Workshop on Advanced Image Technology (IWAIT) 2022 2022.1

Soft-boundary label relaxation with class placement constraints for semantic segmentation of the railway environment Reviewed

Yuki Furitsu, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase, Hiroki Mukojima, Nozomi Nagamine

Pattern Recognition Letters Vol. 150 page： 258 - 264 2021.10

Pointedness of an Image: Measuring How Pointy an Image is Perceived Reviewed

Chihaya Matsuhira, Marc A. Kastner, Ichiro Ide, Yasutomo Kawanishi, Takatsugu Hirayama, Keisuke Doman, Daisuke Deguchi, Hiroshi Murase

Proceedings of HCII 2021 Vol. 37 page： 137 - 144 2021.7

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

For computers to understand human perception, metrics that can capture human perception well are important. However, there are few metrics that characterize the visual perception of humans towards images. Therefore, in this paper, we propose a novel concept and a metric of pointedness of an image, which describes how pointy an image is perceived. The algorithm is inspired by the Features from Accelerated Segment Test (FAST) algorithm for corner detection which looks on the number of continuous neighboring darker pixels surrounding each pixel. We assume that this number would be proportional to the perceived pointedness in the region around the pixel. We evaluated our method towards how well it could capture the human perception of images. To compare the method with similar metrics that describe shapes, we prepared silhouette images of both artificial shapes and natural objects. The results showed that the proposed method gave nearly equivalent perceptual performance to other metrics and also worked in a larger variety of images.

DOI： https://doi.org/10.1007/978-3-030-78635-9_20

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Papers - Deguchi Daisuke