Page 17 - TECH MAGAZINE CSE

AN Technical Magazine
VESHAN Maharaja Agrasen Institute of Technology

Techniques in Explainable Video Summarization
Attention Mechanisms: Used in models like Transformers to highlight what parts of the
video the model attended to.
Feature Attribution: Analyzes how different features (e.g., color, objects, movements)
contribute to summarization.
Saliency Detection: Identifies visually important parts of frames.
Post-hoc Explanations: Methods like SHAP and LIME are used to explain the model’s
choices after summarization.

Benefits of XAI in Video Summarization
Improved User Trust: Users can see why certain clips are chosen, boosting trust in the
system.

Better Customization: Users can adjust criteria based on their needs.
Enhanced Debugging: Developers can identify and fix model biases or errors.
Regulatory Compliance: XAI helps meet transparency requirements in AI regulations.

Challenges
1.High Complexity: Video data involves multiple modalities (visual, audio, temporal),
making it hard to explain.
2.Real-time Processing: Balancing explainability with computational efficiency, especially
for large videos.
3.Subjective Interpretations: Different users may interpret explanations differently.

Future Directions
The future of XAI in video summarization includes:
Multimodal Explainability: Combining visual, audio, and text features for more
comprehensive explanations.
Generative AI: Using AI to produce natural language explanations.
Real-time Tools: Developing interactive tools to allow users to modify summaries in
real time.

In conclusion, XAI in video summarization makes AI decisions transparent, increasing trust
and customization while ensuring accountability. This technology is crucial for applications
like surveillance and media processing, enhancing both user experience and system
reliability.