Domain Gap Impact on Zero-Shot CLIP-Based Video Encoders in Gesture Recognition

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 goal: How does the domain gap between synthetic and real-world video data affect the zero-shot accuracy of CLIP-based video encoders in gesture recognition tasks?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.