Consensus Graph Representation Learning for Better Grounded Image Captioning

Authors

  • Wenqiao Zhang Zhejiang University
  • Haochen Shi Zhejiang University
  • Siliang Tang Zhejiang University
  • Jun Xiao Zhejiang University
  • Qiang Yu City Cloud Technology (China)
  • Yueting Zhuang Zhejiang University

Keywords:

Language and Vision

Abstract

The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resulting in the semantic inconsistency between the visual information and the target lexical words. The most common way is to encourage the captioning model to dynamically link generated object words or phrases to appropriate regions of the image, i.e., the grounded image captioning (GIC). However, GIC utilizes an auxiliary task (grounding objects) that has not solved the key issue of object hallucination, i.e., the semantic inconsistency. In this paper, we take a novel perspective on the issue above: exploiting the semantic coherency between the visual and language modalities. Specifically, we propose the Consensus Rraph Representation Learning framework (CGRL) for GIC that incorporates a consensus representation into the grounded captioning pipeline. The consensus is learned by aligning the visual graph (e.g., scene graph) to the language graph that consider both the nodes and edges in a graph. With the aligned consensus, the captioning model can capture both the correct linguistic characteristics and visual relevance, and then grounding appropriate image regions further. We validate the effectiveness of our model, with a significant decline in object hallucination (-9% CHAIRi) on the Flickr30k Entities dataset. Besides, our CGRL also evaluated by several automatic metrics and human evaluation, the results indicate that the proposed approach can simultaneously improve the performance of image captioning (+2.9 Cider) and grounding (+2.3 F1LOC}).

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Published

2021-05-18

How to Cite

Zhang, W., Shi, H., Tang, S., Xiao, J., Yu, Q., & Zhuang, Y. (2021). Consensus Graph Representation Learning for Better Grounded Image Captioning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(4), 3394-3402. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16452

Issue

Section

AAAI Technical Track on Computer Vision III