Human action anticipation aims at predicting what people will do in the future based on past observations. In this paper, we introduce Uncertainty-aware Action Decoupling Transformer (UADT) for action anticipation. Unlike existing methods that directly predict action in a verb-noun pair format, we decouple the action anticipation task into verb and noun anticipations separately. The objective is to make the two decoupled tasks assist each other and eventually im-prove the action anticipation task. Specifically, we propose a two-stream Transformer-based architecture which is composed of a verb-to-noun model and a noun-to-verb model. The verb-to-noun model leverages the verb information to improve the noun prediction and the other way around. We extend the model in a probabilistic manner and quantify the predictive uncertainty of each decoupled task to select features. In this way, the noun prediction leverages the most in-formative and redundancy-free verb features and verb pre-diction works similarly. Finally, the two streams are combined dynamically based on their uncertainties to make the joint action anticipation. We demonstrate the efficacy of our method by achieving state-of-the-art performance on action anticipation benchmarks including EPIC-KITCHENS, EGTEA Gaze+, and 50-Salads.

In this paper, we introduce physics-augmented autoencoder (PAA) framework for 3D skeleton-based human gait recognition. Specifically, we construct the autoencoder with a graph-convolution-based encoder and a physics-based decoder. The encoder takes the skeleton sequence as input and produces the generalized positions and forces of each joint, which are taken by the decoder to reconstruct the input skeleton based on the Lagrangian dynamics. In this way, the intermediate representations are physically plausible and discriminative. During the inference, the decoder is discared and a RNN-based classifier takes the output of the encoder for gait recognition. We evaluated our proposed method on three benchmark datasets including Gait3D, GREW, and KinectGait. Our method achieves state-of-the-art performance for 3D skeleton-based gait recognition. Furthermore, extensive ablation studies show that our method generalizes better and is more robust with small-scale training data by incorporating the physics knowledge. We also validated the physical plausibility of the intermediate representations by making force predictions on real data with physical annotations.

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 pre-diction. 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 re-sults show that our model achieves the state-of-the-art per-formance under both sufficient and insufficient data.