Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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

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

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

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

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

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

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

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

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

Best next-viewpoint recommendation by selecting minimum pose ambiguity for category-level object pose estimation Reviewed

Nik Mohd Zarifie Hashim, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Journal of the Japan Society for Precision Engineering Vol. 87 ( 5 ) page： 440 - 446 2021.5

Odometry estimation from sparse LiDAR point cloud constrained by image feature correspondence Reviewed

Masayuki Shimizu, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Journal of the Japan Society for Precision Engineering Vol. 87 ( 5 ) page： 447 - 454 2021.5

Proposal of a Light-Field Descriptor Considering Light-Ray Direction Reviewed

Masayuki Shimizu, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Journal of the Japan Society for Precision Engineering Vol. 87 ( 2 ) page： 197 - 205 2021.2

ColAtt-Net: In Reducing the Ambiguity of Pedestrian Orientations on Attribute-Aware Semantic Segmentation Task Reviewed

Mahmud Dwi Sulistiyo, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Takatsugu Hirayama, Hiroshi Murase

IEEJ Transactions on Electrical and Electronic Engineering Vol. 16 ( 2 ) page： 295 - 306 2021.2

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

Semantic segmentation has become one of the trending topics in the world of computer vision and deep learning. Recently, due to an increasing demand to solve a semantic segmentation task simultaneously with attribute recognition of objects, a new task named attribute-aware semantic segmentation has been introduced. Since the task requires to handle pixel-wise object class estimation with its attributes such as a pedestrian’s body orientation, previous works had difficulties to handle ambiguous attributes such as body orientations in object-level, especially when segmenting the pedestrians with their attributes correctly. This paper proposes the ColAtt-Net that is an attribute-aware semantic segmentation model augmented by a column-wise mask branch to predict the pedestrians’ orientations in the horizontal perspective of the input image. We firmly assume that the pedestrians captured by a car-mounted camera are distributed horizontally so that for each column of the input image, the pedestrian pixels can be labeled with one orientation uniformly. In the proposed method, we split the output of the base semantic segmentation model into two branches; one branch for segmenting the object categories, while the other one, as the novel column-wise attribute branch, is to map the recognition of pedestrian’s orientations that are distributed horizontally. This method successfully enhances the performance of attribute-aware semantic segmentation by reducing the ambiguity on segmenting the pedestrian’s orientation. Improvements on the pedestrian orientation segmentation are confidently shown by the proposed method in the experimental results, both in quantitative and qualitative views. This paper also discusses how the improved performance becomes an advantage in the autonomous driving system.

DOI： https://doi.org/10.1002/tee.23296

LFIR2Pose: Pose Estimation from an Extremely Low-Resolution FIR Image Sequence Reviewed

Saki Iwata, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa

Proceedings of the 25th International Conference on Pattern Recognition 2021.1

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

In this paper, we propose a method for human pose estimation from a Low-resolution Far-InfraRed (LFIR) image sequence captured by a 16 × 16 FIR sensor array. Human body estimation from such a single LFIR image is a hard task. For training the estimation model, annotation of the human pose to the images is also a difficult task for human. Thus, we propose the LFIR2Pose model which accepts a sequence of LFIR images and outputs the human pose of the last frame, and also propose an automatic annotation system for the model training. Additionally, considering that the scale of human body motion is largely different among body parts, we also propose a loss function focusing on the difference. Through an experiment, we evaluated the human pose estimation accuracy using an original data set, and confirmed that human pose can be estimated accurately from an LFIR image sequence.

OMEga-GAN: Object Manifold Embedding GAN for Image Generation by Disentangling Parameters into Pose and Shape Manifolds Reviewed

Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of the 25th International Conference on Pattern Recognition page： 7945 - 7952 2021.1

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

In this paper, we propose Object Manifold Embedding GAN (Ω-GAN) to generate images of variously shaped and arbitrarily posed objects from a noise variable sampled from a distribution defined over the pose and the shape manifolds in a vector space. We introduce Parametric Manifold Sampling to sample noise variables from a distribution over the pose manifold to conditionally generate object images in arbitrary poses by tuning the pose parameter. We also introduce Object Identity Loss for clearly disentangling the pose and shape parameters, which allows us to maintain the shape of the object instance when only the pose parameter is changed. Through evaluation, we confirmed that the proposed Ω-GAN could generate variously shaped object images in arbitrary poses by changing the pose and shape parameters independently. We also introduce an application of the proposed method for object pose estimation, through which we confirmed that the object poses in the generated images are accurate.

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Papers - Deguchi Daisuke