  Visual dialog (VisDial) is a task of answering a sequence of questions
grounded in an image, using the dialog history as context. Prior work has
trained the dialog agents solely on VisDial data via supervised learning or
leveraged pre-training on related vision-and-language datasets. This paper
presents a semi-supervised learning approach for visually-grounded dialog,
called Generative Self-Training (GST), to leverage unlabeled images on the Web.
Specifically, GST first retrieves in-domain images through out-of-distribution
detection and generates synthetic dialogs regarding the images via multimodal
conditional text generation. GST then trains a dialog agent on the synthetic
and the original VisDial data. As a result, GST scales the amount of training
data up to an order of magnitude that of VisDial (1.2M to 12.9M QA data). For
robust training of the synthetic dialogs, we also propose perplexity-based data
selection and multimodal consistency regularization. Evaluation on VisDial v1.0
and v0.9 datasets shows that GST achieves new state-of-the-art results on both
datasets. We further observe the robustness of GST against both visual and
textual adversarial attacks. Finally, GST yields strong performance gains in
the low-data regime. Code is available at
https://github.com/gicheonkang/gst-visdial.
