Modality-Balanced Models for Visual Dialogue

TL;DR

This paper introduces a balanced multimodal approach for visual dialogue by combining image-only and joint models, improving generalization and performance on the Visual Dialog challenge.

Contribution

The paper proposes explicitly maintaining and integrating image-only and joint models to balance their strengths, enhancing visual dialogue performance.

Findings

Achieved rank 3 on NDCG in Visual Dialog challenge 2019.

Outperformed the 2018 challenge winner on most metrics.

Models demonstrate strong generalization and balanced metric performance.

Abstract

The Visual Dialog task requires a model to exploit both image and conversational context information to generate the next response to the dialogue. However, via manual analysis, we find that a large number of conversational questions can be answered by only looking at the image without any access to the context history, while others still need the conversation context to predict the correct answers. We demonstrate that due to this reason, previous joint-modality (history and image) models over-rely on and are more prone to memorizing the dialogue history (e.g., by extracting certain keywords or patterns in the context information), whereas image-only models are more generalizable (because they cannot memorize or extract keywords from history) and perform substantially better at the primary normalized discounted cumulative gain (NDCG) task metric which allows multiple correct answers. Hence, this observation encourages us to explicitly maintain two models, i.e., an image-only model and an image-history joint model, and combine their complementary abilities for a more balanced multimodal model. We present multiple methods for this integration of the two models, via ensemble and consensus dropout fusion with shared parameters. Empirically, our models achieve strong results on the Visual Dialog challenge 2019 (rank 3 on NDCG and high balance across metrics), and substantially outperform the winner of the Visual Dialog challenge 2018 on most metrics.