Jielong Wei


2021

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IgSEG: Image-guided Story Ending Generation
Qingbao Huang | Chuan Huang | Linzhang Mo | Jielong Wei | Yi Cai | Ho-fung Leung | Qing Li
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Aligned Dual Channel Graph Convolutional Network for Visual Question Answering
Qingbao Huang | Jielong Wei | Yi Cai | Changmeng Zheng | Junying Chen | Ho-fung Leung | Qing Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Visual question answering aims to answer the natural language question about a given image. Existing graph-based methods only focus on the relations between objects in an image and neglect the importance of the syntactic dependency relations between words in a question. To simultaneously capture the relations between objects in an image and the syntactic dependency relations between words in a question, we propose a novel dual channel graph convolutional network (DC-GCN) for better combining visual and textual advantages. The DC-GCN model consists of three parts: an I-GCN module to capture the relations between objects in an image, a Q-GCN module to capture the syntactic dependency relations between words in a question, and an attention alignment module to align image representations and question representations. Experimental results show that our model achieves comparable performance with the state-of-the-art approaches.