Proceedings

Towards Robust Deterministic and Probabilistic Modeling for Predictive Learning

Xuesong Nie, Haoyuan Jin, Vijayakumar Bhagavatula, Xiaofeng Liu

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1747-1755.

https://doi.org/10.24963/ijcai.2025/195

Keywords

  • Computer Vision: CV: Video analysis and understanding
  • Computer Vision: CV: Motion and tracking
  • Machine Learning: ML: Self-supervised Learning

Abstract

Predictive modeling of unannotated spatiotemporal data presents inherent challenges, primarily due to the highly entangled visual dynamics in real-world scenes. To tackle these complexities, we introduce a novel insight through Disentangling Deterministic and Probabilistic (DDP) modeling. We note a key observation in spatiotemporal data where low-level details typically remain stable, whereas high-level motion frequently exhibits dynamic variations. The core motivation involves constructing two distinct pathways in the latent space: a deterministic path and a probabilistic path. The probabilistic path begins by defining the motion flow, which explicitly describes complex many-to-many motion patterns between patches, and models its probabilistic distribution using a motion diffuser. The deterministic path incorporates a spectral-aware enhancer to retain and amplify visual details in the frequency domain. These designs ensure visual consistency while also capturing intricate long-term motion dynamics. Extensive experiments demonstrate the superiority of DDP across diverse scenario evaluations.

Xuesong Nie, Haoyuan Jin, Vijayakumar Bhagavatula, and Xiaofeng Liu. 2025. Towards Robust Deterministic and Probabilistic Modeling for Predictive Learning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 1747–1755. https://doi.org/10.24963/ijcai.2025/195
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