Object-Specific VQA Processing for Ambiguous Multi-Object Images
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Solution Overview
Problem
Conventional visual question answering (VQA) systems face challenges in identifying the correct object when multiple objects are present in an image and require a large number of questions to be processed, leading to ambiguous answers and increased processing complexity.
Innovation Solution
An information processing device with a detection unit, cut-out unit, and VQA processing unit that detects objects, generates object images, and acquires specific questions based on object identification information, allowing for targeted VQA processing on each object, reducing unnecessary processing and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional VQA systems process all questions for all detected objects, then comprehensive coverage is achieved, but processing complexity and computational cost increase significantly
Solution Approach 1:
The patent segments the VQA processing by dividing questions into object-specific categories (e.g., questions about appearance, questions about actions, questions about relationships) and assigning them to corresponding object types. This segmentation allows the system to process only relevant questions for each object type rather than all questions, reducing processing complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent applies local quality by assigning different question sets to different object types based on their characteristics. For example, person objects receive questions about actions and relationships, while animal objects receive questions about appearance and behavior. This localized approach ensures each object is queried with appropriate questions, reducing unnecessary processing while maintaining adaptability.
2Loss of information
If conventional VQA systems apply all questions to multiple objects, then all possible information is extracted, but answer accuracy decreases due to ambiguity
Solution Approach 1:
The patent segments questions into object-specific categories and assigns them selectively to object types. This ensures that each object is asked only relevant questions, preventing ambiguous answers that would occur when inappropriate questions are applied to wrong object types. For example, questions about 'what the object is doing' are only applied to animate objects, not inanimate objects.
Solution Approach 2:
The patent implements local quality by tailoring question sets to specific object types. Each object type (person, animal, vehicle, etc.) has a customized question set that matches its characteristics. This localized approach improves answer accuracy by ensuring questions are appropriate for each object, while still extracting comprehensive information across all object types through the segmented question assignment.
3Reliability
If conventional VQA systems process questions for every object in the image, then complete analysis is achieved, but processing time increases
Solution Approach 1:
The patent segments the question processing into object-type-specific groups, allowing parallel processing of different object types with their respective question sets. This segmentation enables the system to process multiple objects simultaneously with optimized question sets, reducing overall processing time while maintaining complete analysis coverage across all object types.
Solution Approach 2:
The patent applies local quality by optimizing question sets for each object type, processing only relevant questions for each object rather than all questions. This reduces the number of VQA operations required while ensuring complete analysis of each object's relevant attributes, thereby reducing processing time without sacrificing analysis completeness.
Data Source
AI summary
According to an embodiment, an information processing device includes a detection unit, a cut-out unit, an acquisition unit, and a visual question answering (VQA) processing unit. The detection unit is configured to detect at least one piece of object information including an object area containing an object to be detected and object identification information for identifying the object to be detected, from an image. The cut-out unit is configured to generate at least one object image, by cutting out at least one object area from the image. The acquisition unit is configured to acquire at least one question according to the object identification information. The VQA processing unit is configured to perform a VQA process with the at least one question, for each of the at least one object image.