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Evidence record 4402 · automatically gathered

STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition

Few-shot action recognition (FSAR) requires models to generalize to novel action categories from only a handful of annotated samples. Despite progress with vision-language models, existing approaches still suffer from semantic-temporal misalignment, where static textual prompts fail to capture decisive visual cues that appear sparsely across sequences, and from inadequate modeling of multi-scale temporal dynamics, as short-term discriminative cues and long-range dependencies are often either ove

Record details

Published: 13 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (13 May 2026), “STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition,” evidence record 4402, https://ethics.ai/record/4402 (originally published by arXiv).

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