Multi-modal Visual Question Answering via Symbolic Embeddings
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Solution Overview
Problem
Traditional visual question answering (VQA) approaches are limited by hard-coded templates, leading to inefficiencies, inaccuracies, and difficulties in scaling, particularly in handling inferential question types, as they fail to effectively utilize multi-modal knowledge embeddings.
Innovation Solution
A system that generates multi-modal embeddings by combining symbolic and sub-symbolic features through a scene graph enricher and multi-modal embedding framework, allowing for the aggregation of image and natural language information to provide accurate responses to a wide range of questions, including inferential ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hard-coded templates are used for visual question answering, then the system can provide structured responses, but the system lacks adaptability to handle diverse question types including inferential questions
Solution Approach 1:
The patent transforms the rigid template-based approach into a flexible embedding-based approach. By changing the parameter representation from fixed templates to continuous vector embeddings, the system can adapt to diverse question types including inferential questions without requiring complex hard-coded templates for each scenario.
Solution Approach 2:
The patent replaces the mechanical template-matching system with a neural embedding system. Instead of mechanically selecting from pre-defined templates, the system uses learned embeddings to dynamically generate responses, substituting the rigid mechanical approach with a more adaptive neural representation approach.
2Reliability
If traditional VQA approaches are used, then the system can answer literal questions, but the system provides limited predictive power for inferential questions
Solution Approach 1:
The patent merges multiple knowledge sources including visual embeddings, textual embeddings, and knowledge graph embeddings into a unified multi-modal embedding space. This combination allows the system to leverage information from multiple modalities simultaneously, improving predictive power for inferential questions by integrating diverse knowledge sources rather than relying on single-modality approaches.
Solution Approach 2:
The patent introduces multi-modal embeddings as an intermediary layer between raw input data and final predictions. These embeddings serve as a mediator that transforms and integrates information from different sources (images, text, knowledge graphs) into a unified representation that enhances the system's ability to make accurate predictions for both literal and inferential questions.
3Productivity
If hard-coded templates are used for VQA, then the system can provide consistent responses, but the system is difficult to scale and maintain
Solution Approach 1:
The patent enables the system to automatically learn and adapt through training on data, rather than requiring manual creation and maintenance of templates. The embedding models self-improve through exposure to training data, automatically capturing patterns and relationships without human intervention, thereby eliminating the need for continuous manual template maintenance and improving scalability.
Solution Approach 2:
The patent transitions from static hard-coded templates to dynamic learned embeddings. The embedding representations are not fixed but are dynamically adjusted through training and can adapt to new question types and patterns. This dynamic nature allows the system to scale efficiently as it can learn new patterns from data without requiring manual template updates for each new scenario.
Data Source
AI summary
The systems and methods described herein may generate multi-modal embeddings with sub-symbolic features and symbolic features. The sub-symbolic embeddings may be generated with computer vision processing. The symbolic features may include mathematical representations of image content, which are enriched with information from background knowledge sources. The system may aggregate the sub-symbolic and symbolic features using aggregation techniques such as concatenation, averaging, summing, and/or maxing. The multi-modal embeddings may be included in a multi-modal embedding model and trained via supervised learning. Once the multi-modal embeddings are trained, the system may generate inferences based on linear algebra operations involving the multi-modal embeddings that are relevant to an inference response to the natural language question and input image.