The field of computer vision is witnessing a significant shift towards incorporating causal inference techniques to improve the reliability and generalizability of models. Researchers are now focusing on developing methods that can explicitly address confounding factors, such as style bias, to learn more robust and causal representations. This direction is expected to have a profound impact on various applications, including domain generalization, scene graph generation, and anomaly detection. Notable advancements include the development of frameworks that can adaptively cluster style distributions, perform causal interventions during feature extraction, and learn causal video normality. Furthermore, causal adjustment modules are being proposed to debias scene graph generation models, and representation learning methods are being designed to account for both feature and structural distribution shifts. These innovative approaches are poised to advance the field of computer vision and enable more accurate and reliable models. Noteworthy papers include:

• Casual Inference via Style Bias Deconfounding for Domain Generalization, which introduces a novel causal inference-based framework to address style bias in domain generalization.
• A Causal Adjustment Module for Debiasing Scene Graph Generation, which proposes a causal adjustment module to estimate the modeled causal structure and correct biased model predictions.
• CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos, which argues that there exist causal factors that can adequately generalize the prototypical patterns of regular events and presents significant deviations when anomalous instances occur.