Cross-Modal Late Interaction for Fine-Grained Image-Text Alignment
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Solution Overview
Problem
Existing large-scale Vision-and-Language Pre-training (VLP) models, such as CLIP, lack the ability to capture fine-grained cross-modal interactions between visual objects in images and textual words, leading to insufficient information and inefficiencies in training and inference.
Innovation Solution
Implement a Fine-grained Interactive Language-Image Pre-training (FILIP) technique using a cross-modal late interaction mechanism, which computes token-wise maximum similarities between visual and textual tokens to enhance fine-grained alignment, and applies contrastive learning to model these interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If global feature-based cross-modal alignment is used, then inference efficiency is improved, but fine-grained interaction capability deteriorates
Solution Approach 1:
The patent segments the global feature representation into multiple local region features by dividing the image into spatial regions and using spatial attention mechanisms. This allows the model to capture fine-grained interactions between specific image regions and text tokens while maintaining the efficiency of global alignment through the segmented approach.
Solution Approach 2:
The patent introduces a spatial dimension to the cross-modal alignment process by using spatial attention weights that operate across different regions of the image. This additional dimensional approach enables fine-grained interactions without requiring full token-wise computation across all image patches, thus maintaining efficiency.
2Measurement precision
If token-wise representations with cross-attention are used, then fine-grained interaction capability is improved, but training and inference efficiency deteriorates
Solution Approach 1:
The patent applies local quality by using spatial attention mechanisms that focus computational resources on specific regions of the image that are most relevant to the text input. Instead of uniformly processing all image patches, the model dynamically weights different spatial regions, achieving fine-grained interactions with reduced computational burden.
Solution Approach 2:
The patent implements partial action by selectively attending to only the most relevant image regions rather than performing full token-wise cross-attention across all patches. The spatial attention mechanism identifies and processes only the necessary portions of the image, reducing overall computational requirements while maintaining fine-grained interaction capability.
3Device complexity
If region-of-interest feature extraction is used, then computational complexity is reduced, but information completeness deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-processing the image through a backbone network to extract comprehensive features before the spatial attention mechanism operates. This ensures that all necessary information is initially captured, and then the attention mechanism selectively emphasizes relevant regions without discarding potentially important information from other areas.
Solution Approach 2:
The patent uses feedback mechanisms where the spatial attention weights are computed based on the interaction between image regions and text tokens, and these weights are then used to refine the feature representation. This iterative feedback process ensures that information is preserved and emphasized appropriately based on its relevance to the cross-modal task.
Data Source
AI summary
A method is provided for data processing performed by a processing system. The method comprises determining a set of first tokens for first data and a set of second token for second data, each token comprising information associated with a segment of the respective data, determining pair-wise similarities between the set of first tokens and the set of second tokens, each pair comprising a first token in the set of first tokens and a second token in the set of second tokens, determining, for each first token in the set of first tokens, a maximum similarity based on the determined pair-wise similarities between the respective first token and the second tokens in the set of second tokens, and determining a first similarity between the first data and the second data by aggregating the maximum similarities corresponding to the first tokens in the set of first set of tokens.