Granularity-Aware Adaptors for Cross-Domain Image Retrieval
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Solution Overview
Problem
Existing image search models perform poorly when transitioning between different domains or tasks without additional training, as they are typically trained on specific datasets with labeled images, limiting their ability to effectively search for images across multiple domains.
Innovation Solution
A unified model is created by extending a pre-trained model with independently trained adaptor modules that use pseudo-labels of varying sizes to mimic different granularities, with fusion layers trained to reconcile these adaptors across the feature space, allowing the model to perform well across multiple retrieval tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pre-trained model is trained on specific labeled datasets for each domain, then the model achieves high performance on that specific domain, but the model cannot effectively search for images across multiple domains without additional training
Solution Approach 1:
The model is segmented into a fixed pre-trained backbone and trainable adaptor modules. The adaptor modules are independently trained on different domains and can be selectively activated based on the search domain, allowing cross-domain versatility without retraining the entire model
Solution Approach 2:
A unified model architecture is created that can perform multiple retrieval tasks across different domains. The model uses a shared pre-trained backbone with domain-specific adaptor modules, enabling a single model to handle diverse domains including image-text retrieval, image-image retrieval, and cross-modal search
2Reliability
If adaptor modules are trained independently for each domain, then the model achieves domain-specific performance, but the number of adaptor modules increases model complexity
Solution Approach 1:
The model dynamically selects and weights adaptor modules based on the input query and domain characteristics. Instead of using all adaptor modules simultaneously, the system adaptively activates only the relevant modules for the current task, reducing effective complexity while maintaining domain-specific performance
Solution Approach 2:
Fusion modules serve as intermediaries that aggregate outputs from multiple adaptor modules. These fusion modules learn to combine information from different domain-specific adaptors and the pre-trained model, managing the complexity of multiple adaptors through a unified fusion mechanism
3Adaptability or versatility
If the pre-trained model is fine-tuned on labeled data for each domain, then the model adapts to new domains, but labeled training data is required which limits scalability
Solution Approach 1:
The adaptor modules are trained in an unsupervised self-service manner using the pre-trained model's own features and pseudo-labels. The system generates its own training signals through self-supervised learning mechanisms, eliminating the need for external labeled training data while still achieving domain adaptation
Solution Approach 2:
The pre-trained model performs preliminary feature extraction and representation learning on unlabeled domain data before adaptor training. This preliminary action creates rich feature representations that enable subsequent unsupervised adaptor training, allowing the model to adapt to new domains without requiring labeled data
Data Source
AI summary
An information retrieval training system includes: a training dataset including training data having a feature space; the training data including multiple different types of elements, wherein no labels are provided with the training data; a training module configured to: maintain fixed a pre-trained model configured to receive features of queries; learn sets of pseudo-labels based on the training data; train parameters of adaptor modules for each of the sets of pseudo-labels, respectively, the adaptor modules configured to receive outputs of the pre-trained model, respectively; and train parameters of fusion modules based on neighboring pairs of the training data, the fusion modules configured to fuse together outputs of the adaptor modules, respectively.