Analysis of Human Activity Recognition by Diffusion Models
Year: 2024
Type: conference-paper
Abstract: This paper proposes a classifier based on a diffusion model for human activity recognition. To this end, we introduce three architectures: (1) the representation-conditional diffusion transformer, (2) the first classifier, RepcondFormer, and (3) the second classifier, RepcondClassifier. Experimental results show that RepcondFormer outperforms in three datasets, while RepcondClassifier performs better in one dataset when fine-tuned.
Authors: Kosuke Ukita, Tsuyoshi Okita
Institution: Kyushu Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Universitas Brawijaya
Award: G5589212785
OpenAlex ID: W4402722128