Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
SIP |
2024-03-21 14:25 |
Ibaraki |
Center for Computational Sciences, University of Tsukuba |
Bunch of Tricks for Improving Shuttlecock Detection from Badminton Videos Muhammad Abdul Haq (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
Accurate identification of the shuttlecock is necessary for video analysis in badminton matches, but, it remains difficu... [more] |
SIP2024-4 pp.8-11 |
IEICE-ITS, IEICE-IE, ME, AIT, MMS [detail] |
2024-02-19 13:45 |
Hokkaido |
Hokkaido Univ. |
Efficient Human Pose and Shape Estimation using Decomposed Manhattan Self-Attention Yushan Wang, Botao Zhang (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
HMR2.0, a high performance human pose and shape estimation algorithm, leverages ViT as its backbone and uses pretrained ... [more] |
MMS2024-9 ME2024-25 AIT2024-9 pp.44-48 |
IEICE-ITS, IEICE-IE, ME, AIT, MMS [detail] |
2024-02-19 15:45 |
Hokkaido |
Hokkaido Univ. |
Exploring Source-free Domain Adaption in Multiple Object Tracking Shu Xinqi (Tokyo Metropolitan Uni.), Tarashima Shuhei (NTT Com), Tagawa Norio (Tokyo Metropolitan Uni.) |
In MOT, the task involves detecting and tracking objects across continuous frames, presenting challenges such as occlusi... [more] |
MMS2024-16 ME2024-32 AIT2024-16 pp.84-87 |
MMS, ME, AIT, IEICE-IE, IEICE-ITS [detail] |
2023-02-21 15:30 |
Hokkaido |
Hokkaido Univ. |
Evaluating The Effectiveness of Data Augmentation for Learning TrackNetV2 Yushan Wang (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
Data augmentation has been widely used in a variety of deep learning tasks, mostly with a positive impact on the results... [more] |
MMS2023-16 ME2023-36 AIT2023-16 pp.81-84 |
MMS, ME, AIT, IEICE-IE, IEICE-ITS [detail] |
2023-02-21 15:45 |
Hokkaido |
Hokkaido Univ. |
A Residual U-Net Architecture for Shuttlecock Detection Muhammad Abdul Haq (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
Detection of fast-moving shuttlecocks is essential for badminton video analysis. Several methods based on deep learning ... [more] |
MMS2023-17 ME2023-37 AIT2023-17 pp.85-88 |
AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-21 11:30 |
Online |
online |
A Study of Temporal Information Embedding in Group Activity Recognition Models Yuta Kumakura, Norio Tagawa (Tokyo Metropolitan Univ.), Shuhei Tarashima (NTT Communications Corp.) |
In the previous studies on group activity recognition, various models have been proposed to relate the appearance and be... [more] |
MMS2022-5 ME2022-30 AIT2022-5 pp.25-29 |
AIT, ME, MMS, IEICE-IE, IEICE-ITS [detail] |
2022-02-22 17:15 |
Online |
online |
A Study on Tracklet Appearance Feature Extraction for Multi-Target Multi-Camera Tracking Tomohiro Saito (Tokyo Metropolitan Univ.), Shuhei Trachima (NTT communications Corp.), Norio Tagawa (Tokyo Metropolitan Univ.) |
[more] |
MMS2022-35 ME2022-60 AIT2022-35 pp.383-387 |
ME, IEICE-EMM, IEICE-IE, IEICE-LOIS, IEE-CMN [detail] |
2017-09-05 15:25 |
Kyoto |
Kyoto Univ. (Clock Tower Centennial Hall) |
Visual Pattern Mining via Partitioning Correspondence Graphs Shuhei Tarashima, Takayuki Kurozumi, Tetsuya Kinebuchi (NTT) |
[more] |
ME2017-89 pp.91-95 |