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Evidence record 6076 · automatically gathered

Frequency-Enhanced Diffusion Models: Curriculum-Guided Semantic Alignment for Zero-Shot Skeleton Action Recognition

Human action recognition is pivotal in computer vision, with applications ranging from surveillance to human-robot interaction. Despite the effectiveness of supervised skeleton-based methods, their reliance on exhaustive annotation limits generalization to novel actions. Zero-Shot Skeleton Action Recognition (ZSAR) emerges as a promising paradigm, yet it faces challenges due to the spectral bias of diffusion models, which oversmooth high-frequency dynamics. Here, we propose Frequency-Aware Diffu

Record details

Published: 10 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment · Privacy · Agents & autonomy
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (10 April 2026), “Frequency-Enhanced Diffusion Models: Curriculum-Guided Semantic Alignment for Zero-Shot Skeleton Action Recognition,” evidence record 6076, https://ethics.ai/record/6076 (originally published by arXiv).

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