The content discusses the development of a multi-modal few-shot relation extraction model (MFS-HVE) that combines textual and visual features to predict relations between name entities in sentences. The model includes semantic feature extractors for text and images, as well as multi-modal fusion components to enhance performance. Extensive experiments on public datasets demonstrate the effectiveness of leveraging visual information in improving few-shot relation prediction.
Existing methods for few-shot relation extraction are compared, highlighting the limitations of uni-modal approaches when textual contexts are lacking. The proposed MFS-HVE model addresses these challenges by incorporating both textual and visual information through innovative attention mechanisms. Results show that integrating semantic visual information significantly enhances performance in predicting relations between entities.
The study also includes an ablation study to analyze the impact of different attention units in MFS-HVE, demonstrating the importance of fusing image-guided and object-guided attention for improved results. Additionally, case studies illustrate how the model outperforms text-based models by leveraging informative visual evidence to supplement textual contexts.
Overall, the research showcases the potential of multi-modal approaches in enhancing few-shot relation extraction tasks by effectively combining textual and visual information.
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