3 citations · 5 across the 4 of their papers we have counts for
4 papers · 1 filter
Skeleton-based Zero-Shot Spatio-Temporal Action Localization via Weakly-Supervised Pretraining
Koshiro Nagano, Fumiaki Sato, Ryo Hachiuma +2
We propose a novel pretraining strategy for skeleton-based zero-shot spatio-temporal action localization to estimate unseen actions for person instances while overcoming high annot…
Learning from Synthetic Data via Provenance-Based Input Gradient Guidance
Koshiro Nagano, Ryo Fujii, Ryo Hachiuma +3
Learning methods using synthetic data have attracted attention as an effective approach for increasing the diversity of training data while reducing collection costs, thereby impro…
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling
Ryo Hachiuma, Fumiaki Sato, Taiki Sekii
This paper simultaneously addresses three limitations associated with conventional skeleton-based action recognition; skeleton detection and tracking errors, poor variety of the ta…
Prompt-Guided Zero-Shot Anomaly Action Recognition using Pretrained Deep Skeleton Features
Fumiaki Sato, Ryo Hachiuma, Taiki Sekii
This study investigates unsupervised anomaly action recognition, which identifies video-level abnormal-human-behavior events in an unsupervised manner without abnormal samples, and…