SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning

TL;DR

This paper introduces SGEITL, a novel framework that enhances visual commonsense reasoning by integrating scene graph structures into multimodal Transformer models, improving understanding and reasoning capabilities.

Contribution

It proposes a multihop graph transformer and a scene-graph-aware pre-training method to incorporate scene graph information into visual-text reasoning models.

Findings

Significant performance improvements on VCR and related tasks.

Effective utilization of scene graph structures enhances reasoning accuracy.

Each proposed component contributes to the overall performance boost.

Abstract

Answering complex questions about images is an ambitious goal for machine intelligence, which requires a joint understanding of images, text, and commonsense knowledge, as well as a strong reasoning ability. Recently, multimodal Transformers have made great progress in the task of Visual Commonsense Reasoning (VCR), by jointly understanding visual objects and text tokens through layers of cross-modality attention. However, these approaches do not utilize the rich structure of the scene and the interactions between objects which are essential in answering complex commonsense questions. We propose a Scene Graph Enhanced Image-Text Learning (SGEITL) framework to incorporate visual scene graphs in commonsense reasoning. To exploit the scene graph structure, at the model structure level, we propose a multihop graph transformer for regularizing attention interaction among hops. As for pre-training, a scene-graph-aware pre-training method is proposed to leverage structure knowledge extracted in the visual scene graph. Moreover, we introduce a method to train and generate domain-relevant visual scene graphs using textual annotations in a weakly-supervised manner. Extensive experiments on VCR and other tasks show a significant performance boost compared with the state-of-the-art methods and prove the efficacy of each proposed component.