C3VQG: Category Consistent Cyclic Visual Question Generation

TL;DR

This paper introduces C3VQG, a category-aware variational autoencoder for visual question generation that reduces supervision needs and improves question relevance by leveraging image categories and a novel cyclic loss.

Contribution

The paper proposes a weakly supervised VQG model using a VAE with a category consistent cyclic loss to generate relevant questions without ground-truth answers.

Findings

Outperforms state-of-the-art VQG methods

Reduces supervision by eliminating answer annotations

Generates category-relevant questions

Abstract

Visual Question Generation (VQG) is the task of generating natural questions based on an image. Popular methods in the past have explored image-to-sequence architectures trained with maximum likelihood which have demonstrated meaningful generated questions given an image and its associated ground-truth answer. VQG becomes more challenging if the image contains rich contextual information describing its different semantic categories. In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers. Our approach solves two major shortcomings of existing VQG systems: (i) minimize the level of supervision and (ii) replace generic questions with category relevant generations. Most importantly, by eliminating expensive answer annotations, the required supervision is weakened. Using different categories enables us to exploit different concepts as the inference requires only the image and the category. Mutual information is maximized between the image, question, and answer category in the latent space of our VAE. A novel category consistent cyclic loss is proposed to enable the model to generate consistent predictions with respect to the answer category, reducing redundancies and irregularities. Additionally, we also impose supplementary constraints on the latent space of our generative model to provide structure based on categories and enhance generalization by encapsulating decorrelated features within each dimension. Through extensive experiments, the proposed model, C3VQG outperforms state-of-the-art VQG methods with weak supervision.