Image-Audio-Text Feature Tensor Decomposition for Accurate Model Training
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Solution Overview
Problem
Conventional cross-modal high-dimensional visual representation reconstruction techniques often suffer from low efficiency and accuracy due to the incomplete learning of information from multiple modalities and the inclusion of unwanted noise, particularly when integrating three-dimensional reconstruction techniques based on image, audio, and text information.
Innovation Solution
A model training scheme that constructs a feature tensor from image, audio, and text features, decomposes it into separate vectors using tensor decomposition, and optimizes the training process by correlating and de-noising cross-modal information, thereby improving the reconstruction of realistic visual identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If cross-modal information from multiple modalities is integrated for high-dimensional visual representation reconstruction, then the completeness of information learning is improved, but unwanted noise is also included reducing accuracy
Solution Approach 1:
The patent segments cross-modal information into separate modality-specific tensors (image tensor, audio tensor, text tensor) and processes each independently through dedicated neural network branches. This segmentation allows complete information learning from all modalities while preventing noise contamination between modalities, as each branch can be optimized separately with appropriate noise handling strategies.
Solution Approach 2:
The patent introduces a tensor decomposition mechanism as an intermediary that separates the integrated cross-modal tensor into modality-specific components. This intermediary structure enables the system to learn from all modalities simultaneously while filtering out unwanted noise through the decomposition process, which identifies and isolates relevant information from each modality.
2Device complexity
If conventional cross-modal training methods are used to integrate multiple modalities, then model training is simplified, but training efficiency and accuracy deteriorate
Solution Approach 1:
The training process is segmented into modality-specific training branches that can be optimized independently. Each branch processes one modality through dedicated neural networks with specific loss functions, allowing efficient parallel training while maintaining overall system coordination through the tensor decomposition mechanism.
Solution Approach 2:
The patent transforms the training problem from a conventional flat optimization landscape to a multi-dimensional tensor decomposition space. By representing cross-modal information as tensors and applying decomposition algorithms, the system creates additional optimization dimensions that improve training efficiency and convergence while maintaining manageable complexity.
3Loss of information
If all cross-modal information is processed together, then comprehensive learning is achieved, but noise interference increases reducing model performance
Solution Approach 1:
The patent segments the processing of cross-modal information into separate modality-specific pathways, each handling one type of data (image, audio, or text) independently. This segmentation prevents noise from one modality from interfering with the processing of other modalities, while still achieving comprehensive learning through the integrated tensor decomposition that combines the separate processing results.
Solution Approach 2:
The tensor decomposition mechanism extracts relevant information from the integrated cross-modal tensor by separating it into modality-specific components. This extraction process removes unwanted noise and irrelevant information while retaining the essential features from each modality, thereby reducing noise interference while maintaining learning completeness.
Data Source
AI summary
Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for training a model. The method may include determining image features, audio features, and text features of a reference object based on reference image information, reference audio information, and reference text information associated with the reference object, respectively. The method may also include constructing a feature tensor from the image features, the audio features, and the text features. In addition, the method may further include decomposing the feature tensor into a first feature vector, a second feature vector, and a third feature vector corresponding to the image features, the audio features, and the text features, respectively, to determine a loss function value of the model. The method may also include updating parameters of the model based on the loss function value.