Attention-Based AI Explanation via Hierarchical Image Segmentation
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Solution Overview
Problem
Artificial intelligence models, particularly those using machine learning, often operate as 'black boxes,' making it difficult to explain erratic or unexpected results, limiting user trust and understanding, especially in applications like visual question answering (VQA) models where the reasoning behind answers is not transparent.
Innovation Solution
Implementing attention-based explanations that segment images into hierarchically arranged semantic areas, assigning attention scores to objects and parts, and visually updating the image to highlight the basis for the model's output, providing a comprehensive explanation and facilitating user understanding and trust.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If AI models operate as black boxes to simplify operation, then ease of operation is improved, but understanding and trust of the model decisions deteriorates
Solution Approach 1:
The patent introduces attention maps as an intermediary visual element that mediates between the AI model's internal decision-making process and the user's understanding. The attention map highlights regions in the input image that influenced the model's output, serving as a bridge that translates complex model behavior into interpretable visual information without requiring changes to the model's core operation
Solution Approach 2:
The patent segments the input image into regions of interest based on attention scores, dividing the visual input into meaningful segments that correspond to the model's focus areas. This segmentation allows users to understand which parts of the input contributed most to the output, providing explanatory power while maintaining model simplicity
2Reliability
If attention-based explanations are implemented to improve understanding, then user trust and understanding are improved, but device complexity increases
Solution Approach 1:
The patent extracts attention information from the model's processing and presents it separately as visual highlights on the input image. By taking out the explanation component from the core model operation and presenting it in a separate, intuitive visual format, the system builds user trust without significantly complicating the overall device architecture
Solution Approach 2:
The patent creates a visual copy or representation of the model's attention focus by overlaying highlights on the original input image. This copied visual representation conveys the model's reasoning without requiring additional complex hardware or processing components, maintaining device simplicity while enhancing interpretability
Data Source
AI summary
In general, the disclosure describes various aspects of techniques for attention-based explanations for artificial intelligence behavior. A device comprising a memory and a computation engine executing a processor may be configured to perform the techniques. The memory may store the artificial intelligence model and the image. The computation engine may receive a query regarding the image, and execute the artificial intelligence model to analyze the image in order to output the result to the query. The artificial intelligence model may, when analyzing the image to output the result, segment the image into hierarchically arranged semantic areas in which objects in the image are segmented into parts, determine, based on the query, an attention mask for the areas, update, based on the attention mask, the image to visually identify which of the areas formed a basis for the result, and output the updated image.