@inproceedings{
xu2024probabilistic,
title={Probabilistic Distillation Transformer: Modelling Uncertainties for Visual Abductive Reasoning},
author={Wanru Xu and Zhenjiang Miao and Yi Tian and Yigang Cen and Lili Wan and Ma Xiaole},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=7TYTiE7CAZ}
}
Visual abduction reasoning aims to find the most plausible explanation for incomplete observations, and suffers from inherent uncertainties and ambiguities, which mainly stem from the latent causal relations, incomplete observations, and the reasoning itself.
To address this, we propose a probabilistic model named Uncertainty-Guided Probabilistic Distillation Transformer (UPD-Trans) to model uncertainties  for Visual Abductive Reasoning.
In order to  better discover the correct cause-effect chain, we  model all the potential causal relations into a unified reasoning framework, thus both  the direct relations and  latent relations are considered.
In order to reduce the effect of the stochasticity and uncertainty for reasoning:
1) we extend the deterministic Transformer to a probabilistic Transformer by considering those uncertain factors as Gaussian random variables and explicitly modeling their distribution;
2)  we introduce a distillation mechanism between the posterior branch with complete observations  and the  prior branch with incomplete observations to transfer posterior knowledge.
Evaluation results on the benchmark datasets, consistently demonstrate the commendable performance of our UPD-Trans, with significant improvements  after latent relation modeling and uncertainty modeling.
Probabilistic Distillation Transformer: Modelling Uncertainties for Visual Abductive Reasoning
Wanru Xu Zhenjiang Miao Yi Tian Yigang Cen Lili Wan Ma Xiaole