Sihang Zeng, Kai Tian, Kaiyan Zhang, Yuru Wang, Junqi Gao, Runze Liu, Sa Yang, Jingxuan Li, Xinwei Long, Jiaheng Ma, Biqing Qi, Bowen Zhou. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications. Existing causal discovery methods assume data sufficiency, which may not be the case in many real world datasets. As a result, many existing causal discovery methods can fail under limited data. In this work, we propose Bayesian-augmented frequentist independence tests to improve the performance of constraint-based causal discovery methods under insufficient data: 1) We firstly introduce a Bayesian method to estimate mutual information (MI), based on which we propose a robust MI based independence test; 2) Secondly, we consider the Bayesian estimation of hypothesis likelihood and incorporate it into a well-defined statistical test, resulting in a robust statistical testing based independence test. We apply proposed independence tests to constraint-based causal discovery methods and evaluate the performance on benchmark datasets with insufficient samples. Experiments show significant performance improvement in terms of both accuracy and efficiency over SOTA methods.

Road detection plays a fundamental role in the visual navigation system of autonomous vehicles. However, it's still challenging to achieve robust road detection in off-road scenarios due to their complicated road appearances and ambiguous road structures. Therefore, existing image-based road detection approaches usually fail to extract the right routes due to the lack of the effective fusion of the image and prior reference paths(road guidances generated via map annotations and GPS localization). Besides, the reference paths are not always reliable because of GPS localization errors and mapping errors. To achieve robust road detection in off-road scenarios, we propose a prior-correction-based road detection network named PR-ROAD via fusing the cross-model information provided by both the reference path and the input image. These two heterogeneous data, prior and image, are deeply fused by a cross-attention module and formulate contextual inter-dependencies. We conduct experiments in our collected rural, off-road and urban datasets. The experimental results demonstrate the effectiveness of the proposed method both on unstructured and structured roads.

Deep learning methods have been widely applied to automatic facial action unit (AU) intensity estimation and achieved the state-of-the-art performance. These methods, however, are mostly appearance-based and fail to exploit the underlying structural information among AUs. In this paper, we propose a novel dynamic probabilistic graph convolution (DPG) model to simultaneously exploit AU appearances, AU dynamics, and their semantic structural dependencies for AU intensity estimation. Firstly, we propose to use Bayesian Network to capture the inherent dependencies among AUs. Secondly, we introduce probabilistic graph convolution that allows to perform graph convolution on the distribution of Bayesian Network structure to extract AU structural features. Finally, we introduce a dynamic deep model based on LSTM to simultaneously combine AU appearance features, AU dynamic features, and AU structural features for AU intensity estimation. In experiments, our method achieves comparable and even better performance with the state-of-the-art methods on two benchmark facial AU intensity estimation databases, i.e., FERA 2015 and DISFA.

Facial expression and action units (AUs) represent two levels of descriptions of the facial behavior. Due to the underlying facial anatomy and the need to form a meaningful coherent expression, they are strongly correlated. This paper proposes to systematically capture their dependencies and incorporate them into a deep learning framework for joint facial expression recognition and action unit detection. Specifically, we first propose a constraint optimization method to encode the generic knowledge on expression-AUs probabilistic dependencies into a Bayesian Network (BN). The BN is then integrated into a deep learning framework as a weak supervision for an AU detection model. A data-driven facial expression recognition(FER) model is then constructed from data. Finally, the FER model and AU detection model are trained jointly to refine their learning. Evaluations on benchmark datasets demonstrate the effectiveness of the proposed knowledge integration in improving the performance of both the FER model and the AU detection model. The proposed AU detection model is demonstrated to be able to achieve competitive performance without AU annotations. Furthermore, the proposed Bayesian Network capturing the generic knowledge is demonstrated to generalize well to different datasets.