| Paper Abstract and Keywords |
| Presentation |
2024-02-19 13:45
Efficient Human Pose and Shape Estimation using Decomposed Manhattan Self-Attention
Yushan Wang, Botao Zhang (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
HMR2.0, a high performance human pose and shape estimation algorithm, leverages ViT as its backbone and uses pretrained weights that has learned spatial relationships, leads to high number of parameters and complexity. Our goal is to significantly reduce both parameters and model complexity while preserving the model's expressive capability to a considerable extent. We replace the ViT backbone with spatial decay matrix and proposed decomposed manhattan-attention based architecture, which characterized by its linear complexity. We mix the typical datasets for training with different weights as in HMR2.0, i.e., Human3.6M 0.1, MPI-INF3DHP 0.02, COCO 0.2, MPII 0.1, InstaVariety 0.2, AVA 0.19 and AI Challenger 0.19. We compare the parameters and FLOPs between HMR2.0 and our proposed Decomposed Manhattan Self-Attention based linear complexity structure. Experimental results show that we reduce FLOPs from 242.1G to 17.5G. In terms of qualitative comparison, the adoption of linear complexity led to inferior results compared to the HMR2.0. This outcome was anticipated as, in HMR2.0, to attain optimal results, pre-training weights based on ImageNet were initially employed. However, due to modifications of linear complexity in our network structure, the use of the original pre-trained weights became impractical, necessitating a complete restart of training from scratch. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Pose and Shape Estimation / ViT / HMR2.0 / Linear Complexity / / / / |
| Reference Info. |
ITE Tech. Rep., vol. 48, no. 6, ME2024-25, pp. 44-48, Feb. 2024. |
| Paper # |
ME2024-25 |
| Date of Issue |
2024-02-12 (MMS, ME, AIT) |
| ISSN |
Online edition: ISSN 2424-1970 |
| Download PDF |
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| Conference Information |
| Committee |
IEICE-ITS IEICE-IE ME AIT MMS |
| Conference Date |
2024-02-19 - 2024-02-20 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Hokkaido Univ. |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Image Processing, etc. |
| Paper Information |
| Registration To |
ME |
| Conference Code |
2024-02-ITS-IE-ME-AIT-MMS |
| Language |
English |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Efficient Human Pose and Shape Estimation using Decomposed Manhattan Self-Attention |
| Sub Title (in English) |
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| Keyword(1) |
Pose and Shape Estimation |
| Keyword(2) |
ViT |
| Keyword(3) |
HMR2.0 |
| Keyword(4) |
Linear Complexity |
| Keyword(5) |
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| 1st Author's Name |
Yushan Wang |
| 1st Author's Affiliation |
Tokyo Metropolitan University (TMU) |
| 2nd Author's Name |
Botao Zhang |
| 2nd Author's Affiliation |
Tokyo Metropolitan University (TMU) |
| 3rd Author's Name |
Shuhei Tarashima |
| 3rd Author's Affiliation |
NTT Communications Corporation (NTT Com) |
| 4th Author's Name |
Norio Tagawa |
| 4th Author's Affiliation |
Tokyo Metropolitan University (TMU) |
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| Speaker |
Author-1 |
| Date Time |
2024-02-19 13:45:00 |
| Presentation Time |
15 minutes |
| Registration for |
ME |
| Paper # |
MMS2024-9, ME2024-25, AIT2024-9 |
| Volume (vol) |
vol.48 |
| Number (no) |
no.6 |
| Page |
pp.44-48 |
| #Pages |
5 |
| Date of Issue |
2024-02-12 (MMS, ME, AIT) |
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