Coarse-to-fine multimodal gallery search with attention models
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Solution Overview
Problem
Attention-based multimodal content retrieval systems face computational challenges and scalability issues when dealing with large galleries, making real-time retrieval infeasible due to the high cost of attention computations and vector similarity operations.
Innovation Solution
The system employs a coarse filtering method using averaged feature vectors to quickly identify top candidates, skipping attention computations for most content items and leveraging attention only for selected few, allowing for real-time retrieval without significant performance drop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If attention-based pairwise similarity computation is performed for all content items in large galleries, then retrieval precision is improved, but computational time and system scalability deteriorate
Solution Approach 1:
The patent segments the content gallery into coarse and fine retrieval stages. The coarse stage processes all items with simplified averaged feature vectors, while the fine stage applies computationally intensive attention mechanisms only to a small subset of candidate items. This segmentation resolves the contradiction by distributing computational effort across stages with different precision requirements.
Solution Approach 2:
The patent applies the partial action principle by performing full attention-based similarity computation only on a partial set of candidate items (top-K from coarse retrieval) rather than all gallery items. This reduces overall computational time while maintaining retrieval precision for the most relevant items.
2Measurement precision
If attention computations are performed for all content items, then matching accuracy is improved, but system scalability worsens
Solution Approach 1:
The system segments the retrieval process into two stages: coarse retrieval using averaged feature vectors that scales to large galleries, and fine retrieval using attention mechanisms on selected candidates. This segmentation enables the system to scale to large galleries while maintaining high matching accuracy for relevant items.
Solution Approach 2:
The patent applies attention mechanisms partially, only to candidate items selected from the coarse retrieval stage. This partial application maintains matching accuracy for potential matches while enabling system scalability by avoiding exhaustive attention computations across the entire gallery.
3Measurement precision
If pairwise similarity scores are computed for all content items, then retrieval accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The patent segments the similarity computation process into coarse similarity based on averaged feature vectors and fine similarity using attention-based pairwise scores. This segmentation improves processing speed by performing simplified computations on all items while reserving accurate but slow attention computations for a small subset of candidates.
Solution Approach 2:
The system computes expensive attention-based pairwise similarity scores partially, only for candidate items that pass the coarse filtering stage. This partial computation maintains retrieval accuracy for relevant items while dramatically improving overall processing speed by avoiding unnecessary computations on irrelevant items.
Data Source
AI summary
A method, computer program, and computer system is provided for multimodal content retrieval. A search query corresponding to a request for content is received. Content features corresponding to a subset of content items from among a plurality of content items are retrieved based on receiving the search query. Similarity values are calculated between the search query and the retrieved content features. Attention scores are determined for the calculated similarity values. A content item is selected from among the subset of content items of the plurality of content items. The selected content item contains a content feature corresponding to a highest attention score of the attention scores.