A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training

• URL: http://arxiv.org/abs/2206.05555v1
• Date: Sat, 11 Jun 2022 16:05:06 GMT
• Title: A Unified Continuous Learning Framework for Multi-modal Knowledge Discovery and Pre-training
• Authors: Zhihao Fan, Zhongyu Wei, Jingjing Chen, Siyuan Wang, Zejun Li, Jiarong Xu, Xuanjing Huang
• Abstract summary: We propose to unify knowledge discovery and multi-modal pre-training in a continuous learning framework. For knowledge discovery, a pre-trained model is used to identify cross-modal links on a graph. For model pre-training, the knowledge graph is used as the external knowledge to guide the model updating.
• Score: 73.7507857547549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal pre-training and knowledge discovery are two important research topics in multi-modal machine learning. Nevertheless, none of existing works make attempts to link knowledge discovery with knowledge guided multi-modal pre-training. In this paper, we propose to unify them into a continuous learning framework for mutual improvement. Taking the open-domain uni-modal datasets of images and texts as input, we maintain a knowledge graph as the foundation to support these two tasks. For knowledge discovery, a pre-trained model is used to identify cross-modal links on the graph. For model pre-training, the knowledge graph is used as the external knowledge to guide the model updating. These two steps are iteratively performed in our framework for continuous learning. The experimental results on MS-COCO and Flickr30K with respect to both knowledge discovery and the pre-trained model validate the effectiveness of our framework.

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