Global Fusion Attention for Vision and Language Understanding (Student Abstract)


  • Zixin Guo Aalto University
  • Chen Liang City University of Hong Kong
  • Ziyu Wan City University of Hong Kong
  • Yang Bai China University of Geosciences, Beijing


Vision And Language Understanding, Attention Mechanism, Multimodal


We extend the popular transformer architecture to a multi-modal model, processing both visual and textual inputs. We propose a new attention mechanism on Transformer-based architecture for the joint vision and language understanding tasks. Our model fuses multi-level comprehension between images and texts in a weighted manner, which could better curve the internal relationships. Experiments on benchmark VQA dataset CLEVR demonstrate the effectiveness of the proposed attention mechanism. We also observe the improvements in sample efficiency of reinforcement learning through the experiments on grounded language understanding tasks of BabyAI platform.




How to Cite

Guo, Z., Liang, C., Wan, Z., & Bai, Y. (2021). Global Fusion Attention for Vision and Language Understanding (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15789-15790. Retrieved from



AAAI Student Abstract and Poster Program