Multi-Modal Video Attention for Temporal Coherence and Microgenres
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Solution Overview
Problem
Existing recommendation systems fail to utilize temporal coherence and cross-modal interactions effectively, leading to inaccuracies in video classification and recommendation models.
Innovation Solution
Incorporating temporal coherence and cross-modal non-linear guided attention into machine learning models using convolutional neural networks to process video data, learning dependencies and correlations across different modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing video classification models use attention-based architectures (attention-RNN or transformer), then they can process temporal features, but they fail to effectively utilize temporal coherence which is a fundamental property of videos
Solution Approach 1:
The patent introduces an intermediary mechanism (attention module with temporal coherence constraints) that mediates between the raw video features and the classification output. This intermediary enforces temporal coherence by ensuring that attention weights for temporally coherent regions are consistent, thereby resolving the contradiction between utilizing temporal coherence and maintaining model complexity.
Solution Approach 2:
The patent modifies the attention computation parameters by incorporating temporal coherence constraints into the attention mechanism. This changes the parameter space of the attention module to explicitly account for temporal relationships, allowing the model to effectively utilize temporal coherence without requiring a complete architectural overhaul.
2Reliability
If existing multi-modal video classification architectures are used, then they can process multiple modalities, but they do not influence the attention computation of one modality with another
Solution Approach 1:
The patent merges the attention computation processes of different modalities by introducing cross-modal attention mechanisms. This allows the attention computation of one modality to be influenced by another, combining the information from multiple modalities in the attention weighting process to improve classification accuracy.
Solution Approach 2:
The patent introduces cross-modal attention modules as intermediaries that facilitate interaction between different modalities. These intermediary modules compute attention weights that are influenced by multiple modalities and use them to refine the feature representations, thereby enabling effective cross-modal influence without excessive architectural complexity.
3Measurement precision
If recommendation systems use traditional models, then they can distribute online content, but they fail to achieve hyper-personalization through microgenre classification
Solution Approach 1:
The patent segments the video content classification into microgenres by introducing fine-grained classification categories. This segmentation allows for more precise content classification and hyper-personalization recommendations, enabling the system to distinguish between subtle differences in content types while maintaining efficient recommendation delivery.
Data Source
AI summary
A method includes accessing video data that includes at least two different modalities. The method also includes using a convolutional neural network layer to incorporate temporal coherence into a machine learning model architecture configured to process the video data. The method further includes learning dependency among the at least two different modalities in an attention space of the machine learning model architecture. In addition, the method includes predicting one or more correlations among the at least two different modalities.