Deep learning has revolutionized the field of artificial intelligence. Based on the statistical correlations uncovered by deep learning-based methods, computer vision has contributed to tremendous growth in areas like autonomous driving and robotics. Despite being the basis of deep learning, such correlation is not stable and is susceptible to uncontrolled factors. In the absence of the guidance of prior knowledge, statistical correlations can easily turn into spurious correlations and cause confounders. As a result, researchers are now trying to enhance deep learning methods with causal theory. Causal theory models the intrinsic causal structure unaffected by data bias and is effective in avoiding spurious correlations. This paper aims to comprehensively review the existing causal methods in typical vision and vision-language tasks such as semantic segmentation, object detection, and image captioning. The advantages of causality and the approaches for building causal paradigms will be summarized. Future roadmaps are also proposed, including facilitating the development of causal theory and its application in other complex scenes and systems.

翻译：深度学习已彻底改变了人工智能领域。基于深度学习方法揭示的统计相关性，计算机视觉在自动驾驶和机器人等领域取得了巨大进展。然而，尽管统计相关性是深度学习的基础，它并不稳定，且易受不可控因素的影响。在缺乏先验知识指导的情况下，统计相关性容易转变为虚假相关性，并引发混杂因子。因此，研究者正试图利用因果理论来增强深度学习方法。因果理论建模了不受数据偏差影响的内在因果结构，能有效避免虚假相关性。本文旨在全面综述现有因果方法在语义分割、目标检测和图像描述等典型视觉及视觉-语言任务中的应用。我们将总结因果性的优势及构建因果范式的方法，并展望未来路线图，包括促进因果理论的发展及其在复杂场景与系统中的应用。