Synthetic Video Sample Scaling for Few-Shot Gesture Recognition in Large Pre-Trained Models Across Encoders

Abstract

Abstract: In this work, we explore the possibility of using synthetically generated data for video-based gesture recognition with large pre-trained models. We consider whether these models have sufficiently robust and expressive representation spaces to enable "training-free" classification. Specifically, we utilize various state-of-the-art video encoders to extract features for use in k-nearest neighbors classification, where the training data points are derived from synthetic videos only. We compare these results with another training-free approach – zero-shot classification using text descriptions o

Research Question

What is the impact of varying the number of synthetic video samples per gesture class on the few-shot learning performance of large pre-trained models, as measured by classification accuracy and robustness across different video encoders?

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