Papers with few-shot domain adaptation setting

2 papers
Retrieving Multimodal Prompts for Generative Visual Question Answering (2023.findings-acl)

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Challenge: Visual question answering (VQA) is a multimodal machine learning problem that challenges a model to answer a question posed about an image.
Approach: They propose a generative model enhanced by multimodal prompt retrieval that integrates retrieved prompts and multimodal features to generate answers in free text.
Outcome: The proposed model outperforms its non-retrieval counterpart by 30% on medical VQA tasks.
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation. (2023.emnlp-main)

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Challenge: Existing datasets that focus on company relations and business entities are lacking in relation classification.
Approach: They introduce a few-shot relation classification dataset for company relations and business entities . they use a dataset that includes 4,708 instances of 12 relation types .
Outcome: The proposed dataset includes 4,708 instances of 12 relation types with corresponding textual evidence extracted from company Wikipedia pages.

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