  In this paper, we propose a Disentangled Counterfactual Learning~(DCL)
approach for physical audiovisual commonsense reasoning. The task aims to infer
objects' physics commonsense based on both video and audio input, with the main
challenge is how to imitate the reasoning ability of humans. Most of the
current methods fail to take full advantage of different characteristics in
multi-modal data, and lacking causal reasoning ability in models impedes the
progress of implicit physical knowledge inferring. To address these issues, our
proposed DCL method decouples videos into static (time-invariant) and dynamic
(time-varying) factors in the latent space by the disentangled sequential
encoder, which adopts a variational autoencoder (VAE) to maximize the mutual
information with a contrastive loss function. Furthermore, we introduce a
counterfactual learning module to augment the model's reasoning ability by
modeling physical knowledge relationships among different objects under
counterfactual intervention. Our proposed method is a plug-and-play module that
can be incorporated into any baseline. In experiments, we show that our
proposed method improves baseline methods and achieves state-of-the-art
performance. Our source code is available at https://github.com/Andy20178/DCL.
