Causal Counterfactual Inference Network for Video Object State Changes in Open-World Scenarios

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Object state changes (OSCs) are crucial for video understanding, but current methods struggle with dataset bias and poor generalization in open-world scenarios.
• Existing approaches often rely on irrelevant features due to background-causal scene imbalance and fail to generalize to unseen objects.

Purpose of the Study:

• To address the challenges of dataset bias and poor generalization in open-world object state change recognition.
• To develop a novel neural network that leverages causal and counterfactual inference for improved video understanding.

Main Methods:

• Introduced a structural causal model (SCM) to formally define the OSC task and its challenges.
• Proposed CCI-Net, a causal counterfactual inference-based neural network.
• Implemented a backdoor scene classifier (BSC) for confounder elimination and a counterfactual module (CM) for enhanced generalization.

Main Results:

• CCI-Net effectively mitigates spurious correlations by eliminating confounders through backdoor adjustment.
• The counterfactual module enhances generalization to unseen objects and their state changes.
• Experimental results show significant improvements in precision and generalization compared to existing methods on benchmark datasets.

Conclusions:

• CCI-Net offers a robust solution for object state change recognition in open-world video understanding.
• The integration of causal and counterfactual inference is key to achieving better generalization and precision.
• This work advances the field by providing a generalized approach to understanding state transitions in videos.