ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph
This work addresses the challenge of detailed semantic alignment in vision-language tasks for AI applications, representing an incremental advance by integrating scene graph knowledge into pre-training.
The paper tackles the problem of learning joint vision-language representations by incorporating structured knowledge from scene graphs to build detailed semantic connections across modalities, achieving state-of-the-art performance on cross-modal tasks with a 3.7% absolute improvement on the VCR leaderboard.
We 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%.