Image Retrieval Encoding Summarization for Fewer Embeddings
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Solution Overview
Problem
Existing dense open vocabulary image retrieval frameworks face scalability issues due to domain shifts and require large computational resources for clustering, leading to inefficient storage and processing of numerous embeddings per image.
Innovation Solution
A computer vision model with a summarization module that restricts the number of learnable queries and embeddings per image, using a fine-tuned summarization head with frozen layers to reduce computational demands while maintaining accuracy, thereby simplifying the encoding process and adapting to target datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense open vocabulary models generate numerous embeddings per image for comprehensive image retrieval, then retrieval accuracy is improved, but computational resources and storage requirements increase significantly
Solution Approach 1:
The patent extracts only the most relevant visual concepts from the full set of possible embeddings by introducing a summarization module that selects a restricted number of queries. This extraction approach maintains retrieval accuracy by focusing on salient features while reducing the total number of embeddings stored and processed per image.
Solution Approach 2:
The patent changes the parameter of embedding quantity from unlimited to restricted by implementing a summarization head that limits the number of learnable queries. This parameter change is achieved through freezing certain layers and applying learning weights only to selected head layers, thereby controlling the output embedding count while preserving retrieval effectiveness.
2Adaptability or versatility
If the computer vision model learns a large number of queries per image to handle domain shifts, then adaptability is improved, but device complexity and computational demands increase
Solution Approach 1:
The patent segments the model into a backbone network and a separate summarization head module. The backbone remains frozen to preserve pre-trained knowledge, while the summarization head is trained separately to adapt to target domains. This segmentation reduces overall model complexity by isolating the adaptive component.
Solution Approach 2:
The patent performs preliminary action by freezing the backbone layers before training the summarization head. This preliminary step preserves the generalization capabilities learned during pre-training while allowing the summarization head to adapt to specific domains, thereby managing complexity through staged training.
3Ease of operation
If clustering is performed on numerous embeddings to manage data scale, then data organization is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by generating only a restricted number of embeddings per image through the summarization module, rather than generating all possible embeddings and then clustering them. This approach achieves sufficient data organization with fewer embeddings, eliminating the need for extensive clustering operations and reducing processing time.
4Measurement precision
If all head layers are trained to maximize retrieval performance, then retrieval accuracy is improved, but training time and computational cost increase
Solution Approach 1:
The patent introduces dynamics by selectively freezing certain head layers while allowing others to be trained. This dynamic approach enables the model to adapt to target domains through the trainable layers while preserving the generalization capabilities of the frozen layers, achieving a balance between accuracy and training efficiency.
Data Source
AI summary
A system for image retrieval includes a processing device connected to a database configured to store a set of images. The processing device includes a computer vision model including a text encoder configured to extract textual features and a vision encoder configured to extract image features, and generate embeddings used for image retrieval tasks, and a summarization module configured to be trained using a targeted dataset, the summarization module configured to restrict a number of queries per image that are learnable by the computer vision model to a selected number.