Novel Semantic Prompting for Zero-Shot Action Recognition

Salman Iqbal; Waheed Rehman

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arXiv · 2603.08289

Novel Semantic Prompting for Zero-Shot Action Recognition

Salman Iqbal·Waheed Rehman

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Abstract

Zero-shot action recognition relies on transferring knowledge from vision-language models to unseen actions using semantic descriptions. While recent methods focus on temporal modeling or architectural adaptations to handle video data, we argue that semantic prompting alone provides a strong and underexplored signal for zero-shot action understanding. We introduce SP-CLIP, a lightweight framework that augments frozen vision-language models with structured semantic prompts describing actions at multiple levels of abstraction, such as intent, motion, and object interaction. Without modifying the visual encoder or learning additional parameters, SP-CLIP aligns video representations with enriched textual semantics through prompt aggregation and consistency scoring. Experiments across standard benchmarks show that semantic prompting substantially improves zero-shot action recognition, particularly for fine-grained and compositional actions, while preserving the efficiency and generalization of pretrained models.

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Salman Iqbal, Waheed Rehman. 2026-03-09. Novel Semantic Prompting for Zero-Shot Action Recognition. https://arxiv.org/abs/2603.08289

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