ERNIE-ViL: Knowledge Enhanced Vision-Language Representations through Scene Graphs
Keywords:Language and Vision, Multimodal Learning
AbstractWe propose a knowledge-enhanced approach, ERNIE-ViL, which incorporates structured knowledge obtained from scene graphs to learn joint representations of vision-language. ERNIE-ViL tries to build the detailed semantic connections (objects, attributes of objects and relationships between objects) across vision and language, which are essential to vision-language cross-modal tasks. Utilizing scene graphs of visual scenes, ERNIE-ViL constructs Scene Graph Prediction tasks, i.e., Object Prediction, Attribute Prediction and Relationship Prediction tasks in the pre-training phase. Specifically, these prediction tasks are implemented by predicting nodes of different types in the scene graph parsed from the sentence. Thus, ERNIE-ViL can learn the joint representations characterizing the alignments of the detailed semantics across vision and language. After pre-training on large scale image-text aligned datasets, we validate the effectiveness of ERNIE-ViL on 5 cross-modal downstream tasks. ERNIE-ViL achieves state-of-the-art performances on all these tasks and ranks the first place on the VCR leaderboard with an absolute improvement of 3.7%.
How to Cite
Yu, F., Tang, J., Yin, W., Sun, Y., Tian, H., Wu, H., & Wang, H. (2021). ERNIE-ViL: Knowledge Enhanced Vision-Language Representations through Scene Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 35(4), 3208-3216. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16431
AAAI Technical Track on Computer Vision III