Hierarchical Audio-Visual Feature Fusion for Complex Question Answering
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Solution Overview
Problem
Current audio-visual question answering systems lack effective mechanisms for fusing audio and video features, leading to insufficient question answering capability and reduced complex semantic understanding performance.
Innovation Solution
A hierarchical audio-visual feature fusing method that inputs audio-video, video-question, and video embedding at different stages of a baseline model, performing answer decoding and hierarchical integration to generate a final answer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If audio features are fused only at input or output position of a purely visual model framework, then the system structure is simple, but the question answering capability is insufficient and complex semantic understanding performance is reduced
Solution Approach 1:
The patent segments the audio-visual feature fusion process into three distinct hierarchical levels: (1) audio-visual feature fusion at the embedding level, (2) audio-visual feature fusion at the intermediate feature level, and (3) answer probability fusion at the output level. This segmentation allows each level to contribute differently to the final answer, improving question answering capability while maintaining a structured system architecture.
Solution Approach 2:
The patent introduces a hierarchical dimension to the feature fusion process by integrating audio features at multiple levels (embedding level, intermediate level, and output level) rather than a single position. This multi-dimensional approach enables the system to capture audio-visual interactions at different stages of processing, significantly improving complex semantic understanding while maintaining systematic structure.
2Reliability
If audio features are fused at multiple positions in the model, then the question answering capability is improved, but the system complexity increases
Solution Approach 1:
The patent divides the feature fusion process into three clearly defined segments: embedding-level fusion, intermediate-level fusion, and output-level fusion. Each segment has a specific function and operates at a different stage of the processing pipeline, which improves question answering capability while keeping the system structure organized and manageable through clear functional segmentation.
Solution Approach 2:
The patent implements dynamic feature fusion by adaptively integrating audio features at multiple stages based on their relevance to different aspects of the question answering task. The system dynamically adjusts which audio-visual features are fused at each level, allowing flexible information integration that improves capability without requiring a static complex structure.
3Reliability
If the proportion of audio characterization module is increased, then the complex semantic understanding performance is improved, but the computational cost increases
Solution Approach 1:
The patent performs preliminary audio-visual feature fusion at the embedding level before the main processing stages. By pre-integrating audio and visual features early in the pipeline, the system reduces the computational burden on subsequent stages while maintaining comprehensive semantic understanding capability, as the foundational audio-visual relationships are already established.
Solution Approach 2:
The patent implements partial feature fusion by selectively integrating audio features at specific levels rather than processing all audio-visual interactions at maximum detail throughout the entire pipeline. This partial action approach maintains complex semantic understanding performance while reducing overall computational cost by focusing computational resources where they are most beneficial.
Data Source
AI summary
A hierarchical audio-visual feature fusing method for audio-visual question answering and a product relate to the field of audio-visual question answering. By fusing audio embedding in an input video clip with a baseline model as well as video embedding and question embedding respectively at an early stage, a middle stage and a late stage in a hierarchical feature fusing process, a first answer probability distribution, a second answer probability distribution and a third answer probability distribution are obtained, and the answer probability distributions are added based on preset weights, and then averaged for hierarchical integration to generate a final answer.