Exploring Vision-Language Models for Open-Vocabulary Zero-Shot Action Segmentation

• URL: http://arxiv.org/abs/2602.21406v1
• Date: Tue, 24 Feb 2026 22:23:22 GMT
• Title: Exploring Vision-Language Models for Open-Vocabulary Zero-Shot Action Segmentation
• Authors: Asim Unmesh, Kaki Ramesh, Mayank Patel, Rahul Jain, Karthik Ramani,
• Abstract summary: Temporal ActionMatrix (TAS) requires dividing videos into action segments, yet the vast space of activities and alternative breakdowns makes collecting datasets infeasible.<n>We propose Open-Vocabulary Zero-Shot Temporal Action (OVTAS) by leveraging the strong zero-shot capabilities of Vision-Language Models (VLMs)<n>We present a systematic study across 14 diverse VLMs, providing the first broad analysis of their suitability for open-vocabulary action segmentation.
• Score: 12.112297992589314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal Action Segmentation (TAS) requires dividing videos into action segments, yet the vast space of activities and alternative breakdowns makes collecting comprehensive datasets infeasible. Existing methods remain limited to closed vocabularies and fixed label sets. In this work, we explore the largely unexplored problem of Open-Vocabulary Zero-Shot Temporal Action Segmentation (OVTAS) by leveraging the strong zero-shot capabilities of Vision-Language Models (VLMs). We introduce a training-free pipeline that follows a segmentation-by-classification design: Frame-Action Embedding Similarity (FAES) matches video frames to candidate action labels, and Similarity-Matrix Temporal Segmentation (SMTS) enforces temporal consistency. Beyond proposing OVTAS, we present a systematic study across 14 diverse VLMs, providing the first broad analysis of their suitability for open-vocabulary action segmentation. Experiments on standard benchmarks show that OVTAS achieves strong results without task-specific supervision, underscoring the potential of VLMs for structured temporal understanding.

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