Two Causal Principles for Improving Visual Dialog

Abstract

This paper unravels the design tricks adopted by us, the champion team MReaL-BDAI, for Visual Dialog Challenge 2019: two causal principles for improving Visual Dialog (VisDial). By "improving", we mean that they can promote almost every existing VisDial model to the state-of-the-art performance on the leader-board. Such a major improvement is only due to our careful inspection on the causality behind the model and data, finding that the community has overlooked two causalities in VisDial. Intuitively, Principle 1 suggests: we should remove the direct input of the dialog history to the answer model, otherwise a harmful shortcut bias will be introduced; Principle 2 says: there is an unobserved confounder for history, question, and answer, leading to spurious correlations from training data. In particular, to remove the confounder suggested in Principle 2, we propose several causal intervention algorithms, which make the training fundamentally different from the traditional likelihood estimation. Note that the two principles are model-agnostic, so they are applicable in any VisDial model. The code is available at https://github.com/simpleshinobu/visdial-principles.

Accepted by CVPR 2020

References in corpus (1)

• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Cited by in corpus (5)

• Unbiased Scene Graph Generation from Biased Training
• iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering
• Counterfactual Variable Control for Robust and Interpretable Question Answering
• iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability
• SeqDialN: Sequential Visual Dialog Networks in Joint Visual-Linguistic Representation Space