Neural Network Alignment of Video and Text Sequences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for aligning heterogeneous sequential data, such as video and text, face challenges due to the complexity of one-to-many and one-to-none correspondences, reliance on first-order Markov assumptions, and separation of similarity metric learning and alignment stages, which limits contextual information utilization.
Innovation Solution
An end-to-end neural architecture using long short-term memory (LSTM) chains and recurrent neural networks captures decision context, allowing for non-Markov alignment decisions and optimizing similarity metrics specifically for the alignment task, incorporating previous matched content and decisions to inform future alignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional two-stage methods are used for aligning video and text data, then the alignment process is simpler and more modular, but the alignment accuracy is limited due to separation of similarity metric learning and alignment stages
Solution Approach 1:
The patent merges the similarity metric learning stage and the alignment stage into a single unified neural network model. This allows the model to jointly optimize both tasks, enabling the alignment process to directly utilize learned similarity metrics while capturing contextual information from both video and text sequences simultaneously, thereby improving alignment accuracy without requiring separate processing stages
2Loss of information
If first-order Markov assumptions are used for alignment decisions, then the computational complexity is reduced, but the contextual information utilization is limited
Solution Approach 1:
The patent employs dynamic recurrent neural network processing that adapts to the sequential nature of video and text data. The model dynamically processes sequences by maintaining hidden states that capture contextual information from previous elements, allowing it to utilize long-range dependencies and contextual patterns without being constrained by fixed-order Markov assumptions, thus reducing information loss while managing computational complexity through efficient RNN architecture
Data Source
AI summary
Systems, methods and computer program products related to aligning heterogeneous sequential data are disclosed. Video data in a media presentation and textual data corresponding to content of the media presentation are received. An action related to aligning the video data and the textual data is determined using an alignment neural network, such that the video data and the textual data are at least partially aligned following the action. The alignment neural network includes a first fully connected layer that receives as input the video data, the textual data, and data relating to a previously determined action by the alignment neural network related to aligning the video data and the textual data. The determined action related to aligning the video data and the textual data is performed.