Deep learning has revolutionized the field of artificial intelligence. Based on the statistical correlations uncovered by deep learning-based methods, computer vision technology has contributed to tremendous growth in areas such as 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 beginning to refine deep learning-based 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，计算机视觉技术为自动驾驶和机器人等领域的巨大发展做出了贡献。然而，这种相关性作为深度学习的基础并不稳定，且易受不可控因素影响。在缺乏先验知识引导的情况下，统计相关性极易转化为伪相关性并引发混杂因素。为此，研究者开始运用因果理论优化深度学习方法。因果理论能够建模不受数据偏差影响的内在因果结构，有效规避伪相关性。本文旨在全面综述语义分割、目标检测和图像描述等典型视觉及视觉-语言任务中的现有因果方法，总结因果性的优势与构建因果范式的途径，并提出未来发展路线图，包括推动因果理论发展及其在复杂场景与系统中的深化应用。