Object State Change Anticipation in Procedural Videos: The OSCA Task, Benchmark, and Baseline

Area of Science:

• Computer Vision
• Artificial Intelligence
• Human-Computer Interaction

Background:

• Robust video understanding requires anticipating object state changes during activities.
• Assistive, Augmented Reality (AR), and robotics systems need real-world predictive capabilities.

Purpose of the Study:

• Introduce Object State Change Anticipation (OSCA) for predicting imminent object state changes before the next action in egocentric videos.
• Present Ego4D-OSCA, a benchmark dataset with pre-/post-state labels, inverse state relations, and a 'no state change' category.
• Propose a baseline model for OSCA that combines egocentric visual context with structured linguistic action/state history.

Main Methods:

• Developed the Ego4D-OSCA benchmark dataset.
• Proposed a novel OSCA baseline model fusing visual context and linguistic history.
• Established comprehensive evaluation protocols and conducted extensive experiments, including modality and temporal context ablations.
• Investigated robustness to class imbalance and long-tail distributions.

Main Results:

• Integrating structured action/state history significantly improves anticipation compared to visual-only methods.
• Modeling inverse state relations enhances generalization across objects and contexts.
• OSCA prediction is complementary to action anticipation and next-active object prediction, offering a distinct capability.

Conclusions:

• The proposed OSCA approach and Ego4D-OSCA benchmark advance the field of predictive AI for real-world applications.
• Structured historical information is key for accurate object state change anticipation.
• OSCA addresses a unique predictive task crucial for intelligent systems.