A lightweight weak semantic framework for cinematographic shot classification.

Area of Science:

• Computer Vision
• Film Studies
• Artificial Intelligence

Background:

• Film shots are fundamental units conveying directorial intent and visual language.
• Understanding shot types aids in analyzing cinematic structure and meaning.
• Existing methods for shot classification may not fully leverage multimodal data or low-level features.

Purpose of the Study:

• To improve the accuracy of cinematographic shot type classification.
• To investigate the effectiveness of multimodal video inputs for shot analysis.
• To establish a connection between shot classification and spatiotemporal semantic features.

Main Methods:

• Proposed a Lightweight Weak Semantic Relevance Framework (LWSRNet) for shot type classification.
• Developed a Linear Modalities Fusion (LMF) module for integrating diverse video inputs.
• Utilized a Weak Semantic 3D-CNN based Feature Extraction Backbone (WSFE Module) for movement and scale analysis.

Main Results:

• Experimental results validate that multimodal inputs enhance shot classification accuracy.
• The proposed LWSRNet framework outperforms previous methods on benchmark datasets.
• The framework achieves high accuracy with reduced computational complexity and fewer parameters.

Conclusions:

• Multimodal video analysis and spatiotemporal features are crucial for accurate shot type classification.
• The LWSRNet framework offers an efficient and effective solution for cinematographic analysis.
• The FullShots dataset provides valuable resources for future research in computational filmmaking.