Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
This work addresses the problem of enhancing reasoning and answer diversity in visual dialog for AI systems, representing an incremental advancement with specific gains.
The paper tackled the challenges of reasoning over semantic structures in visual dialog and identifying multiple appropriate answers by proposing a Sparse Graph Learning method and Knowledge Transfer, resulting in significant improvements over baseline methods and outperforming state-of-the-art approaches on the VisDial v1.0 dataset.
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context. In this paper, we study how to address two fundamental challenges for this task: (1) reasoning over underlying semantic structures among dialog rounds and (2) identifying several appropriate answers to the given question. To address these challenges, we propose a Sparse Graph Learning (SGL) method to formulate visual dialog as a graph structure learning task. SGL infers inherently sparse dialog structures by incorporating binary and score edges and leveraging a new structural loss function. Next, we introduce a Knowledge Transfer (KT) method that extracts the answer predictions from the teacher model and uses them as pseudo labels. We propose KT to remedy the shortcomings of single ground-truth labels, which severely limit the ability of a model to obtain multiple reasonable answers. As a result, our proposed model significantly improves reasoning capability compared to baseline methods and outperforms the state-of-the-art approaches on the VisDial v1.0 dataset. The source code is available at https://github.com/gicheonkang/SGLKT-VisDial.