Multilingual Image Question Answering Model
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Solution Overview
Problem
Current image captioning approaches lack interaction with users as they only provide generic descriptions and do not consider user preferences or interests, limiting the ability to focus on specific parts or objects within an image.
Innovation Solution
A multimodal question answering (mQA) model that incorporates a combination of Long Short-Term Memory (LSTM) components and Convolutional Neural Networks (CNNs) to generate multi-word answers to freestyle questions about image content, allowing for improved user-computer interaction by considering user input and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image captioning systems provide generic descriptions, then they can cover broad image content, but they cannot address specific user interests or preferences
Solution Approach 1:
The system incorporates user feedback through questions and preferences to dynamically adjust and refine image descriptions. The QA model learns from user interactions and adapts its responses based on what users find interesting or important, creating a closed-loop system that continuously improves personalization.
Solution Approach 2:
The system transitions from static generic captions to dynamic personalized descriptions that change based on user input. The model can adapt its focus and detail level according to real-time user preferences, making the interaction flexible and responsive rather than fixed and rigid.
2Measurement precision
If the system provides detailed multi-word answers, then answer accuracy improves, but processing complexity increases
Solution Approach 1:
The system divides the complex task of generating accurate multi-word answers into separate processing stages: visual feature extraction, question encoding, answer generation, and refinement. Each component handles a specific aspect of the problem, making the overall system more manageable while maintaining high accuracy.
Solution Approach 2:
The patent introduces intermediate representations and processing layers that bridge the gap between simple image recognition and complex natural language generation. These intermediary components transform visual data and user questions into structured formats that facilitate accurate multi-word answer generation without requiring the entire system to be maximally complex.
Data Source
AI summary
Embodiments of a multimodal question answering (mQA) system are presented to answer a question about the content of an image. In embodiments, the model comprises four components: a Long Short-Term Memory (LSTM) component to extract the question representation; a Convolutional Neural Network (CNN) component to extract the visual representation; an LSTM component for storing the linguistic context in an answer, and a fusing component to combine the information from the first three components and generate the answer. A Freestyle Multilingual Image Question Answering (FM-IQA) dataset was constructed to train and evaluate embodiments of the mQA model. The quality of the generated answers of the mQA model on this dataset is evaluated by human judges through a Turing Test.