Papers by Kuangrong Hao

2 papers
Federated Document-Level Biomedical Relation Extraction with Localized Context Contrast (2024.lrec-main)

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Challenge: Existing studies on relation extraction focus on document-level training without sharing raw medical texts.
Approach: They propose a federated framework for relation extraction that enables collaborative training without sharing raw medical texts.
Outcome: The proposed framework extends document-level relation extraction to a federated environment.
IDC: Boost Text-to-image Retrieval via Indirect and Direct Connections (2024.lrec-main)

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Challenge: Dual Encoders (DE) and Cross Attention (CA) frameworks for image and text retrieval are more accurate but slower.
Approach: They propose a dual encoders-based approach to map image and text inputs into a coordinated representation space and calculate their similarity directly.
Outcome: Extensive experiments on the MSCOCO and Flickr30K datasets validate the effectiveness of the proposed methods.

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