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  This is the repo for the paper [SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval](https://arxiv.org/abs/2401.13478).
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- <div align="center">
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- <img src=./imgs/Framework.png width=80% />
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- </div>
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  In this paper, we propose a novel SciMMIR benchmark and a corresponding dataset designed to address the gap in evaluating multi-modal information retrieval (MMIR) models in the scientific domain.
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  It is worth mentioning that we define a data hierarchical architecture of "Two subsets, Five subcategories" and use human-created keywords to classify the data (as shown in the table below).
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- <div align="center">
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- <img src=./imgs/data_architecture.png width=50% />
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- </div>
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  As shown in the table below, we conducted extensive baselines (both fine-tuning and zero-shot) within various subsets and subcategories.
 
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  This is the repo for the paper [SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval](https://arxiv.org/abs/2401.13478).
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+ ![main_result](./imgs/Framework.png)
 
 
 
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  In this paper, we propose a novel SciMMIR benchmark and a corresponding dataset designed to address the gap in evaluating multi-modal information retrieval (MMIR) models in the scientific domain.
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  It is worth mentioning that we define a data hierarchical architecture of "Two subsets, Five subcategories" and use human-created keywords to classify the data (as shown in the table below).
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+ ![main_result](./imgs/data_architecture.png)
 
 
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  As shown in the table below, we conducted extensive baselines (both fine-tuning and zero-shot) within various subsets and subcategories.