Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering

1 code implementation • EMNLP 2020 • Zujie Liang, Weitao Jiang, Haifeng Hu, Jiaying Zhu

In the task of Visual Question Answering (VQA), most state-of-the-art models tend to learn spurious correlations in the training set and achieve poor performance in out-of-distribution test data.

Contrastive Learning counterfactual +2

Learning Neural Templates for Recommender Dialogue System

1 code implementation • EMNLP 2021 • Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang

Second, only the items mentioned in the training corpus have a chance to be recommended in the conversation.

Dialogue Generation Recommendation Systems +2

LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answering

1 code implementation • 29 May 2021 • Zujie Liang, Haifeng Hu, Jiaying Zhu

Most existing Visual Question Answering (VQA) systems tend to overly rely on language bias and hence fail to reason from the visual clue.

Question Answering Visual Question Answering

Maria: A Visual Experience Powered Conversational Agent

1 code implementation • ACL 2021 • Zujie Liang, Huang Hu, Can Xu, Chongyang Tao, Xiubo Geng, Yining Chen, Fan Liang, Daxin Jiang

The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image.