Multimodal Fusion Model for Video Description Synchronization
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Solution Overview
Problem
Existing video description systems face challenges in synchronizing the sequence of video features with the sequence of words in descriptions, leading to clutter and incomplete event representation, as objects and actions may appear in a different order than they are described.
Innovation Solution
A multimodal fusion system that generates content vectors from input data including image, motion, and audio signals, using feature extractors, weight estimation, and attention mechanisms to synchronize and selectively use features for accurate word sequence generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all video features are used to generate video descriptions, then comprehensive event coverage is improved, but description accuracy deteriorates due to clutter from irrelevant features
Solution Approach 1:
The patent extracts and separates relevant features from irrelevant ones by introducing modality-specific attention mechanisms that selectively attend to features corresponding to each word being generated, while filtering out clutter from unrelated features through dynamic weighting
Solution Approach 2:
The patent applies different attention weights to different modalities and features based on their local relevance to each specific word being generated, allowing the system to focus on locally relevant features (image, motion, audio) while ignoring globally present but locally irrelevant features
2Device complexity
If video features are processed in sequential order, then processing simplicity is improved, but synchronization with natural language description deteriorates due to mismatched feature and word sequences
Solution Approach 1:
The patent introduces dynamic attention weights that adaptively adjust the importance of different video features at each generation step, allowing the system to dynamically synchronize feature selection with the natural language description sequence rather than relying on fixed sequential processing
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary between the video features and the word generation process, which mediates the synchronization by selectively gating which features influence each word based on temporal and semantic alignment
3Reliability
If multiple modalities are fused to improve description quality, then event representation is improved, but computational complexity deteriorates
Solution Approach 1:
The patent segments the fusion process into modality-specific attention mechanisms that independently process each modality (image, motion, audio) and then combine their contributions, reducing computational complexity compared to processing all features uniformly while maintaining comprehensive event representation
Data Source
AI summary
A system for generating a word sequence includes one or more processors in connection with a memory and one or more storage devices storing instructions causing operations that include receiving first and second input vectors, extracting first and second feature vectors, estimating a first set of weights and a second set of weights, calculating a first content vector from the first set of weights and the first feature vectors, and calculating a second content vector, transforming the first content vector into a first modal content vector having a predetermined dimension and transforming the second content vector into a second modal content vector having the predetermined dimension, estimating a set of modal attention weights, generating a weighted content vector having the predetermined dimension from the set of modal attention weights and the first and second modal content vectors, and generating a predicted word using the sequence generator.