Summary of Unleashing the Power Of Imbalanced Modality Information For Multi-modal Knowledge Graph Completion, by Yichi Zhang et al.
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
by Yichi Zhang, Zhuo Chen, Lei Liang, Huajun Chen, Wen Zhang
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 Adaptive Multi-modal Fusion and Modality Adversarial Training (AdaMF-MAT) approach addresses the imbalance problem of modality information among entities in multi-modal knowledge graph completion (MMKGC). This method leverages structural, visual, and textual information from different modalities to predict missing triples. By incorporating adaptive modality weights for multi-modal fusion and generating adversarial samples through modality-adversarial training, AdaMF-MAT can efficiently utilize raw modality information and outperform 19 recent MMKGC methods on three public benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MMKGC helps us understand how different things are related to each other. Right now, there’s a problem with the way we do this – some types of information (like words or images) are more important than others. This makes it hard for our computers to figure out the missing connections between things. To solve this, researchers created a new approach called AdaMF-MAT. It adjusts how much weight each type of information gets and also generates fake data that’s similar but slightly different. This helps the computer learn better from all the types of information. With this new method, computers can now do MMKGC better than before, which is exciting for people who want to understand more about how things are connected. |
Keywords
* Artificial intelligence * Knowledge graph * Multi modal
