Title: Enhancing Temporal Action Segmentation with Causal Abstraction Segmentation Refiner (CAS

Integrating deep learning and causal discovery has increased the
interpretability of Temporal Action Segmentation (TAS) tasks. However,
frame-level causal relationships exist many complicated noises outside the
segment-level, making it infeasible to directly express macro action semantics.
Thus, we propose Causal Abstraction Segmentation Refiner (CASR), which can
refine TAS results from various models by enhancing video causality in
marginalizing frame-level casual relationships. Specifically, we define the
equivalent frame-level casual model and segment-level causal model, so that the
causal adjacency matrix constructed from marginalized frame-level causal
relationships has the ability to represent the segmnet-level causal
relationships. CASR works out by reducing the difference in the causal
adjacency matrix between we constructed and pre-segmentation results of
backbone models. In addition, we propose a novel evaluation metric Causal Edit
Distance (CED) to evaluate the causal interpretability. Extensive experimental
results on mainstream datasets indicate that CASR significantly surpasses
existing various methods in action segmentation performance, as well as in
causal explainability and generalization.

Enhancing Temporal Action Segmentation with Causal Abstraction Segmentation Refiner (CASR)

In recent years, the integration of deep learning and causal discovery has greatly improved the interpretability of Temporal Action Segmentation (TAS) tasks. However, a significant challenge remains in expressing macro action semantics due to the presence of frame-level causal relationships with complicated noises outside the segment-level.

To address this challenge, we propose a novel framework called Causal Abstraction Segmentation Refiner (CASR). CASR aims to refine TAS results from various models by enhancing video causality in marginalizing frame-level causal relationships. By defining equivalent frame-level causal models and segment-level causal models, CASR constructs a causal adjacency matrix that represents the segment-level causal relationships.

The key idea behind CASR is to minimize the difference between the causal adjacency matrix constructed from marginalized frame-level causal relationships and the pre-segmentation results of backbone models. This refinement process ensures that the refined TAS results capture a more accurate representation of the underlying causal relationships within the video.

In addition to introducing CASR, we also propose a new evaluation metric called Causal Edit Distance (CED) to assess the causal interpretability of TAS results. CED provides a quantitative measure of how well the refined TAS results align with the ground truth causal structure of the video.

The multi-disciplinary nature of CASR is evident in its integration of concepts from deep learning, causal discovery, and multimedia information systems. By combining these fields, CASR provides a comprehensive approach to enhancing TAS performance and interpretability.

In the broader field of multimedia information systems, TAS plays a crucial role in applications such as video surveillance, human-computer interaction, and content analysis. The ability to accurately segment and interpret actions within a video can improve tasks such as activity recognition, event detection, and anomaly detection.

Furthermore, CASR’s approach to enhancing video causality has implications for other areas of multimedia technology, such as animations, artificial reality, augmented reality, and virtual realities. By refining TAS results and improving causal interpretability, CASR can contribute to the development of more realistic and immersive multimedia experiences.

Key Takeaways:

1. The integration of deep learning and causal discovery has improved the interpretability of Temporal Action Segmentation (TAS) tasks.
2. CASR is a framework that refines TAS results by enhancing video causality through marginalizing frame-level causal relationships.
3. CASR constructs a causal adjacency matrix to represent segment-level causal relationships.
4. The difference between the constructed causal adjacency matrix and pre-segmentation results is minimized to refine TAS results.
5. CED is a new evaluation metric introduced by CASR to assess causal interpretability.
6. CASR’s multi-disciplinary nature relates to the wider field of multimedia information systems, as it can improve applications like video surveillance and content analysis.
7. CASR’s enhancement of video causality has implications for animations, artificial reality, augmented reality, and virtual realities.

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