Novel Semantic Prompting for Zero-Shot Action Recognition

• URL: http://arxiv.org/abs/2603.08289v1
• Date: Mon, 09 Mar 2026 12:07:55 GMT
• Title: Novel Semantic Prompting for Zero-Shot Action Recognition
• Abstract summary: Zero-shot action recognition relies on transferring knowledge from vision-language models to unseen actions using semantic descriptions.<n>We introduce SP-CLIP, a lightweight framework that augments frozen vision-language models with structured semantic prompts describing actions at multiple levels of abstraction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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