Domain-Aware Time-Series Captioning via Retrieval Augmentation
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Solution Overview
Problem
Conventional methods for time-series image captioning generate generic, domain-agnostic captions that lack contextual relevance and domain specificity, requiring extensive retraining to adapt to new domains, which is time-consuming and expensive.
Innovation Solution
A method utilizing contrastive language-image pretraining (CLIP) and determinantal point process (DPP) to select diverse samples, combined with a CLIP model as a visual encoder and a decoder like GPT-2, generates domain-aware captions by adapting generic captions to specific domains without retraining, using synthetic datasets for parameter-based sentiment association.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image captioning methods are used for time-series images, then generic captions can be generated, but the captions lack domain specificity and contextual relevance
Solution Approach 1:
The system performs preliminary actions by retrieving domain-specific captions from a datastore before generating the final caption. These retrieved captions serve as prior knowledge that guides the generation process, allowing the model to adapt to the target domain without retraining while preserving domain context information.
Solution Approach 2:
The system introduces an intermediary mechanism - a retrieval module that fetches domain-specific captions from an external datastore. This intermediary bridges the gap between generic pre-trained models and domain-specific requirements, enabling the model to access domain context information without losing its general capabilities.
2Adaptability or versatility
If model fine-tuning is performed for domain adaptation, then domain-specific captions can be generated, but the process becomes time-consuming and expensive
Solution Approach 1:
Instead of fine-tuning the model parameters for each domain, the system copies domain-specific knowledge from pre-existing captions in the datastore. By retrieving and utilizing these copied captions as retrieval augmented input, the system achieves domain adaptation without the time-consuming process of retraining or fine-tuning the model.
3Loss of information
If retrieval-based captioning is used with external datastore, then domain-specific information can be accessed, but the system complexity increases
Solution Approach 1:
The system employs a universal approach where the same retrieval and generation framework can handle multiple domains by simply changing the query parameters. The datastore and model architecture remain the same across different domains, reducing system complexity while maintaining the ability to access domain-specific information through the retrieval mechanism.
Data Source
AI summary
A method and a system for generating domain-aware captions for time-series images that are capable of adapting to new domains without retraining are provided. The method includes: receiving information that relates to a first time-series image that is associated with a target domain; generating a generic caption for the first time-series image; extracting, from a memory based on the generic caption, a plurality of image-caption pairs; generating, for each respective one of the plurality of image-caption pairs, a corresponding domain-agnostic caption that includes information that describes a shape of the respective image; and adapting the generic caption into a domain-specific caption for the first time-series image that relates to the target domain. The generating of domain-agnostic captions and domain-specific captions may be performed by using models that are respectively trained based on synthetic time series images having captions that correspond to a set of parameters.