Image Description Generation via Outer Product Feature Fusion
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Solution Overview
Problem
Traditional image description generation methods rely on single linear fusion for cross-modal feature interaction learning, limiting the accuracy of generated descriptions to first-order feature interactions between different modalities.
Innovation Solution
The proposed method involves acquiring image region features, obtaining outer product vectors through linear fusion, and calculating attention distributions in spatial and channel dimensions to generate image descriptions, enabling the mining of higher-order feature interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single linear fusion is used for cross-modal feature interaction learning, then the model complexity is low and ease of manufacture is improved, but the measurement precision of feature interactions is limited to first-order only
Solution Approach 1:
The patent introduces outer product operations to transform first-order feature interactions into second-order feature interactions. By computing outer products between image region features and text features, the model captures higher-dimensional interaction patterns that go beyond simple linear fusion, thereby improving measurement precision of feature interactions while managing model complexity through structured computation.
Solution Approach 2:
The patent changes the interaction parameter from linear fusion to outer product fusion, fundamentally altering how features interact across modalities. This parameter change enables the model to capture non-linear relationships and higher-order interactions between image and text features, significantly improving feature interaction accuracy while maintaining computational tractability through attention mechanisms.
2Manufacturing precision
If traditional attention module with single linear fusion is used, then the computation speed is fast and productivity is improved, but the generated image description accuracy deteriorates
Solution Approach 1:
The patent segments the feature interaction process into distinct stages: first computing outer product vectors to capture second-order interactions, then applying attention mechanisms to weigh and aggregate these interactions. This segmentation allows the model to systematically improve description accuracy through multiple processing stages while managing computational load through efficient attention computation.
Solution Approach 2:
The patent introduces outer product vectors as an intermediary representation between raw image features and final image descriptions. These intermediate vectors capture enriched feature interactions that serve as a bridge, allowing the model to generate more accurate descriptions while maintaining reasonable computation speed through subsequent attention-based aggregation.
3Loss of information
If first-order feature interaction is mined only, then the model complexity is low and ease of operation is improved, but the loss of information in cross-modal interactions increases
Solution Approach 1:
The patent combines multiple types of feature interactions into a composite representation by computing outer products between image region features and text features. This creates a richer, multi-dimensional feature interaction space that captures both first-order and second-order relationships, reducing information loss while maintaining structured computation through attention mechanisms to manage complexity.
Data Source
AI summary
The present disclosure relates to the technical field of image processing, and in particular to an image description generation method, apparatus and system, and a medium and an electronic device. The method comprises: acquiring one or more image region features in a target image, and obtaining a current input vector by performing a mean pooling on the image region features; obtaining respective outer product vectors of the image region features by respectively linearly fusing the current input vector and each of the image region features; calculating, based on the respective outer product vectors of the image region features, an attention distribution of the image region features in a spatial dimension and an attention distribution of the image region features in a channel dimension; and generating an image description of the target image based on the attention distribution of the image region features in the spatial dimension and the attention distribution of the image region features in the channel dimension.