An Evaluation of Large Pre-Trained Models for Gesture Recognition using Synthetic Videos

• URL: http://arxiv.org/abs/2410.02152v1
• Date: Thu, 3 Oct 2024 02:31:14 GMT
• Title: An Evaluation of Large Pre-Trained Models for Gesture Recognition using Synthetic Videos
• Authors: Arun Reddy, Ketul Shah, Corban Rivera, William Paul, Celso M. De Melo, Rama Chellappa,
• Abstract summary: We explore the possibility of using synthetically generated data for video-based gesture recognition with large pre-trained models. We use various state-of-the-art video encoders to extract features for use in k-nearest neighbors classification. We find that using synthetic training videos yields significantly lower classification accuracy on real test videos compared to using a relatively small number of real training videos.
• Score: 32.257816070522885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 of each gesture. In our experiments with the RoCoG-v2 dataset, we find that using synthetic training videos yields significantly lower classification accuracy on real test videos compared to using a relatively small number of real training videos. We also observe that video backbones that were fine-tuned on classification tasks serve as superior feature extractors, and that the choice of fine-tuning data has a substantial impact on k-nearest neighbors performance. Lastly, we find that zero-shot text-based classification performs poorly on the gesture recognition task, as gestures are not easily described through natural language.

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