Summary of Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation, by Yichi Zhang et al.
Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
by Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Binbin Hu, Ziqi Liu, Wen Zhang, Huajun Chen
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 novel framework MyGO introduces a new approach to multi-modal knowledge graph completion (MMKGC) by tokenizing, fusing, and augmenting fine-grained multi-modal representations of entities. This method learns entity representations with a cross-modal entity encoder and incorporates fine-grained contrastive learning to highlight the specificity of the entity representations. The framework demonstrates superior performance on standard MMKGC benchmarks, surpassing 19 latest models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MyGO is a new way to fill in missing information from knowledge graphs by using multiple types of data about each thing, like pictures and words. This helps make the results more accurate and specific. MyGO takes this multi-modal information and turns it into a special kind of code that can be used to understand the relationships between things. It even learns how to highlight the most important details in this code. This makes it better at filling in missing information than other methods. |
Keywords
» Artificial intelligence » Encoder » Knowledge graph » Multi modal
