Uncertainty-Guided Probabilistic Transformer for Complex Action Recognition

A complex action consists of a sequence of atomic actions that interact with each other over a relatively long period of time. This paper introduces a probabilistic model named Uncertainty-Guided Probabilistic Transformer (UGPT) for complex action recognition. The self-attention mechanism of a Transformer is used to capture the complex and long-term dynamics of the complex actions. By explicitly modeling the distribution of the attention scores, we extend the deterministic Transformer to a probabilistic Transformer in order to quantify the uncertainty of the prediction. The model prediction uncertainty is used to improve both training and inference. Specifically, we propose a novel training strategy by introducing a majority model and a minority model based on the epistemic uncertainty. During the inference, the prediction is jointly made by both models through a dynamic fusion strategy. Our method is validated on the benchmark datasets, including Breakfast Actions, MultiTHUMOS, and Charades. The experiment results show that our model achieves the state-of-the-art performance under both sufficient and insufficient data.