Co-tokenization for Video and Text Fusion
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing multi-modal inputs that include both video frames and text sequences using neural networks, particularly in tasks such as visual question-answering, where the integration of video and text information is computationally intensive and resource-consuming.
Innovation Solution
The system employs co-tokenization to generate a fixed-size set of tokens representing both video and text features, which are then processed using a decoder neural network to produce network outputs. This approach includes iterative refinement of tokens, updating the relatively small number of tokens rather than the larger number of visual features, to improve representation quality while maintaining computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video and text features are processed separately using traditional methods, then each modality can be processed independently, but the fusion process becomes computationally intensive and consumes excessive resources
Solution Approach 1:
The patent combines video and text processing into a unified co-tokenization framework where both modalities are processed together through shared transformer layers, eliminating the need for separate processing pipelines and reducing overall computational overhead
Solution Approach 2:
The transformer-based architecture serves multiple functions simultaneously: it performs tokenization, feature extraction, and modal fusion in a single unified model that can handle both video and text inputs through the same computational structure
2Loss of information
If a large number of visual features from video frames are directly used in the neural network, then detailed visual information is preserved, but the computational complexity and resource consumption increase significantly
Solution Approach 1:
The patent extracts only the most relevant visual features from video frames by selecting key frames and extracting dominant visual elements, then represents these extracted features as a compact set of tokens that retain essential visual information while reducing dimensionality
Solution Approach 2:
The patent transforms visual features from high-dimensional continuous space into discrete token representations with fixed vocabulary sizes, changing the parameter representation from continuous floats to discrete indices that are more computationally efficient while preserving semantic meaning
3Manufacturing precision
If iterative refinement is performed on the full set of visual features, then representation quality improves, but the computational cost becomes prohibitively high
Solution Approach 1:
The patent segments the refinement process to operate only on the selected token representations rather than all visual features, dividing the computational work into two stages: initial feature selection and subsequent token refinement, which together achieve high representation quality at lower cost
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing videos and text using co-tokenization.