Summary of Dwe+: Dual-way Matching Enhanced Framework For Multimodal Entity Linking, by Shezheng Song et al.
DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking
by Shezheng Song, Shasha Li, Shan Zhao, Xiaopeng Li, Chengyu Wang, Jie Yu, Jun Ma, Tianwei Yan, Bin Ji, Xiaoguang Mao
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed DWE+ model for multimodal entity linking (MEL) addresses current method limitations by capturing finer semantics and maintaining semantic consistency with entities. The approach involves three aspects: extracting fine-grained image features through hierarchical contrastive learning, extracting visual attributes from images to enhance fusion features, and leveraging Wikipedia and ChatGPT to capture the entity representation. This results in a more accurate and dynamic linking of ambiguous mentions to unambiguous entities in knowledge bases. Evaluation on Wikimel, Richpedia, and Wikidiverse datasets demonstrates the effectiveness of DWE+ in improving MEL performance, achieving state-of-the-art performance on enhanced datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DWE+ is a new way to link words and images together using lots of information from the internet. It’s like building a bridge between what we know and what we see. Right now, there are some big problems with linking words and images, but DWE+ solves these issues by looking really closely at small parts of images and using extra information to make sure it gets things right. |
Keywords
» Artificial intelligence » Entity linking » Semantics
