VideoQA Query Complexity Estimation Using AST Code Analysis
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Solution Overview
Problem
Conventional VideoQA models struggle to accurately assess the difficulty of queries due to their reliance on static visual information, leading to discrepancies between human perception and machine performance, and existing benchmarking methods fail to generate diverse and challenging queries.
Innovation Solution
A complexity model is developed to estimate query complexity by analyzing the content and structure of generated code, identifying patterns in code subroutines that correlate with model performance, and generating a new benchmark that challenges VideoQA systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If VideoQA models rely on static visual information for query assessment, then the evaluation process is simplified, but the accuracy of difficulty assessment deteriorates due to discrepancies between human perception and machine performance
Solution Approach 1:
The patent introduces an intermediary complexity estimation system that bridges the gap between static visual information and accurate difficulty assessment. This system uses code generation and analysis as a mediator to translate natural language queries into executable code, then evaluates the code's complexity metrics to determine query difficulty, thereby resolving the discrepancy between human perception and machine performance
Solution Approach 2:
The patent replaces the traditional mechanical approach of directly analyzing static visual information with a computational approach using code generation and interpretation. By substituting the evaluation mechanism with code-based complexity analysis, the system achieves more accurate difficulty assessment that aligns with human perception of query complexity
2Ease of manufacture
If conventional benchmarking methods are used, then the benchmarking process is straightforward, but the diversity and challenge level of generated queries deteriorates
Solution Approach 1:
The patent introduces dynamic query generation through code-based complexity estimation, allowing the benchmarking system to adaptively create queries with varying complexity levels. The system dynamically adjusts query difficulty by analyzing code complexity metrics and generating queries that match target complexity thresholds, thereby achieving both diversity and challenge while maintaining processability through automated code analysis
3Measurement precision
If code-based complexity estimation is implemented, then query difficulty assessment accuracy is improved, but the computational complexity and processing time deteriorates
Solution Approach 1:
The patent segments the complexity estimation process into distinct modular components: code generation from natural language, code parsing into abstract syntax trees, complexity metric calculation, and threshold-based classification. This segmentation allows each component to be optimized independently and enables parallel processing, reducing overall computational complexity while maintaining high assessment accuracy
Solution Approach 2:
The patent employs parameter changes by adjusting code complexity thresholds and selection criteria to balance accuracy and computational load. By dynamically setting complexity parameters and filtering queries based on calculated metrics, the system achieves precise difficulty assessment while controlling processing requirements through parameter optimization
Data Source
AI summary
A method for determining a complexity of a natural language query includes converting a first natural language query into executable program code, the first natural language query being a query for a first video to be answered by one or more video question answering (VideoQA) models. The method also includes generating, via a complexity model, an abstract syntax tree (AST) based on the executable program code. The method further includes determining, via the complexity model, a complexity of the first natural language query based on quantity of subtrees, from a group of subtrees, that are present in the AST.