Causal Intervention and Semantic Knowledge for Object Relationships

This study investigates how causal intervention and semantic features can improve image categorization and context inference. Classifying items in images and understanding their contextual relationships are common computer vision difficulties. We propose a convolutional network trained using the causal intervention method to overcome these restrictions. This integration aims to improve relational inference, image classification, model interpretability, accuracy, and context awareness. The framework's efficacy is tested using CrisisMMD, a natural catastrophe image dataset.