Visual Question Answering Model Using Speech-Visual Embeddings
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Solution Overview
Problem
Existing visual question answering models do not fully utilize the information in coordinated text-video representations and fail to leverage relationships between different modal spaces, such as speech and vision, especially when trained on unlabeled videos without annotation.
Innovation Solution
A system that learns a shared embedding space using speech-visual correspondence on unlabeled videos, generating additional embeddings for question, video, and answer, enabling the training of a visual question answering model that can perform tasks like action recognition, object recognition, and captioning without requiring labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If visual question answering models are trained using traditional methods with labeled data, then they can achieve basic VQA performance, but they fail to fully utilize information in coordinated text-video representations and do not leverage relationships between different modal spaces
Solution Approach 1:
The patent merges speech and vision modalities into a shared embedding space, allowing the model to leverage relationships between different modal spaces. By embedding both speech representations and visual representations in the same vector space, the model can utilize coordinated text-video representations more effectively, resolving the contradiction between information utilization and performance reliability.
Solution Approach 2:
The shared embedding space serves multiple functions: it enables speech-visual correspondence learning, supports visual question answering, and facilitates action recognition. This multi-functional approach allows the same embedding space to be used for different tasks, improving both information utilization and VQA performance simultaneously.
2Ease of manufacture
If models are trained on unlabeled videos without annotation, then data availability increases and annotation costs decrease, but existing models cannot effectively learn from these unlabeled videos
Solution Approach 1:
The model performs self-supervised learning by learning speech-visual correspondence from unlabeled videos without requiring external annotations. The system uses the inherent structure and relationships within the unlabeled data itself to train the embedding space, making the training process self-service and eliminating the need for manual labeling while maintaining learning effectiveness.
3Loss of information
If a shared embedding space is learned using speech-visual correspondence on unlabeled videos, then the model can leverage freely available data and improve information utilization, but the complexity of learning multiple embeddings in coordinated spaces increases
Solution Approach 1:
The shared embedding space acts as an intermediary that bridges speech and vision modalities. By introducing this intermediate representation space, the model can learn complex relationships between different modalities without directly comparing speech and visual features, thereby managing the complexity of learning multiple embeddings while effectively utilizing unlabeled video information.
Data Source
AI summary
An example system includes a processor to learn a shared embedding space on unlabeled videos using speech visual correspondence. The processor can learn a number of additional embeddings including a question plus video embedding and an answer embedding using the shared embedding space to generate a trained visual question answering model. The processor can execute a visual question answering based on the trained visual question answering model.