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

• URL: http://arxiv.org/abs/2605.13202v1
• Date: Wed, 13 May 2026 08:54:38 GMT
• Title: STAR: Semantic-Temporal Adaptive Representation Learning for Few-Shot Action Recognition
• Abstract summary: Few-shot action recognition (FSAR) requires models to generalize to novel action categories from only a handful of annotated samples.<n>Despite progress with vision-language models, existing approaches still suffer from semantic-temporal misalignment.<n>We propose Semantic Temporal Adaptive Representation Learning (STAR), a unified framework consisting of a semantic-alignment component and a temporal-aware component.
• Score: 23.546777614096424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 oversmoothed or fragmented. To address these challenges, we propose Semantic Temporal Adaptive Representation Learning (STAR), a unified framework, consisting of a semantic-alignment component and a temporal-aware component, effectively bridging the semantic and temporal gaps and transferring the sequence modeling capability of Mamba into the FSAR. The semantic alignment module introduces a Temporal Semantic Attention (TSA) mechanism, which performs frame-level cross-modal alignment with textual cues, ensuring fine-grained semantic-temporal consistency. The temporal-aware module incorporates a Semantic Temporal Prototype Refiner (STPR) that integrates semantic-guided Mamba blocks with multi-frequency temporal sampling and bidirectional state-space refinement, yielding semantically aligned prototypes with enhanced discriminative fidelity and temporal consistency. Furthermore, temporally dependent class descriptors derived from large language models (LLMs) provide long-range semantic guidance. Extensive experiments on five FSAR benchmarks demonstrate the consistent superiority of STAR over state-of-the-art methods. For instance, STAR achieves up to 8.1% and 6.7% gains on the SSv2-Full and SSv2-Small datasets under the 1-shot setting, and 7.3% on HMDB51, validating its effectiveness under limited supervision. The code is available at https://github.com/HongliLiu1/STAR-main.

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