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Examples of Recovering from Entity Linking Errors

23 Jun 2024

Here, we illustrate our proposal of using entity mentions to recover from entity linking errors. In the training set, we have the following example:

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Med-Flamingo: a Multimodal Medical Few-shot Learner - Appendix

19 Jun 2024

Med-Flamingo enhances multimodal few-shot learning in medical VQA, achieving up to 20% improvement with generative answers from a specialized dataset.

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Med-Flamingo: a Multimodal Medical Few-shot Learner - Discussion, Acknowledgments, and References

19 Jun 2024

Med-Flamingo enhances multimodal few-shot learning in medical VQA, achieving up to 20% improvement with generative answers from a specialized dataset.

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Med-Flamingo: a Multimodal Medical Few-shot Learner - Results

19 Jun 2024

Med-Flamingo enhances multimodal few-shot learning in medical VQA, achieving up to 20% improvement with generative answers from a specialized dataset.

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Med-Flamingo: a Multimodal Medical Few-shot Learner - Evaluation

19 Jun 2024

Med-Flamingo enhances multimodal few-shot learning in medical VQA, achieving up to 20% improvement with generative answers from a specialized dataset.

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Med-Flamingo: a Multimodal Medical Few-shot Learner - Med-Flamingo

19 Jun 2024

Med-Flamingo enhances multimodal few-shot learning in medical VQA, achieving up to 20% improvement with generative answers from a specialized dataset.

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Med-Flamingo: a Multimodal Medical Few-shot Learner - Related Works

19 Jun 2024

Med-Flamingo enhances multimodal few-shot learning in medical VQA, achieving up to 20% improvement with generative answers from a specialized dataset.

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Med-Flamingo: a Multimodal Medical Few-shot Learner - Abstract and Introduction

19 Jun 2024

Med-Flamingo enhances multimodal few-shot learning in medical VQA, achieving up to 20% improvement with generative answers from a specialized dataset.

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Towards Automatic Satellite Images Captions Generation Using LLMs: References

16 Jun 2024

Researchers present ARSIC, a method for remote sensing image captioning using LLMs and APIs, improving accuracy and reducing human annotation needs.