Unleashing the Power of Imbalanced Modality Information for Multi-modal
  Knowledge Graph Completion
Yichi Zhang Zhuo Chen Lei Liang Huajun Chen Wen Zhang
Multi-modal knowledge graph completion (MMKGC) aims to predict the missing
triples in the multi-modal knowledge graphs by incorporating structural,
visual, and textual information of entities into the discriminant models. The
information from different modalities will work together to measure the triple
plausibility. Existing MMKGC methods overlook the imbalance problem of modality
information among entities, resulting in inadequate modal fusion and
inefficient utilization of the raw modality information. To address the
mentioned problems, we propose Adaptive Multi-modal Fusion and Modality
Adversarial Training (AdaMF-MAT) to unleash the power of imbalanced modality
information for MMKGC. AdaMF-MAT achieves multi-modal fusion with adaptive
modality weights and further generates adversarial samples by
modality-adversarial training to enhance the imbalanced modality information.
Our approach is a co-design of the MMKGC model and training strategy which can
outperform 19 recent MMKGC methods and achieve new state-of-the-art results on
three public MMKGC benchmarks. Our code and data have been released at
https://github.com/zjukg/AdaMF-MAT.