SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering
This addresses a practical issue for VQA researchers by reducing reliance on costly annotated data, but it appears incremental as it builds on existing scene graph and self-supervised techniques.
The paper tackled the problem of scene graph-based Visual Question Answering (VQA) methods struggling to generalize with predicted scene graphs from images, and introduced SelfGraphVQA, a self-supervised framework that improves performance by using semantically-preserving augmentations and contrastive learning, though no concrete numbers are provided in the abstract.
The intersection of vision and language is of major interest due to the increased focus on seamless integration between recognition and reasoning. Scene graphs (SGs) have emerged as a useful tool for multimodal image analysis, showing impressive performance in tasks such as Visual Question Answering (VQA). In this work, we demonstrate that despite the effectiveness of scene graphs in VQA tasks, current methods that utilize idealized annotated scene graphs struggle to generalize when using predicted scene graphs extracted from images. To address this issue, we introduce the SelfGraphVQA framework. Our approach extracts a scene graph from an input image using a pre-trained scene graph generator and employs semantically-preserving augmentation with self-supervised techniques. This method improves the utilization of graph representations in VQA tasks by circumventing the need for costly and potentially biased annotated data. By creating alternative views of the extracted graphs through image augmentations, we can learn joint embeddings by optimizing the informational content in their representations using an un-normalized contrastive approach. As we work with SGs, we experiment with three distinct maximization strategies: node-wise, graph-wise, and permutation-equivariant regularization. We empirically showcase the effectiveness of the extracted scene graph for VQA and demonstrate that these approaches enhance overall performance by highlighting the significance of visual information. This offers a more practical solution for VQA tasks that rely on SGs for complex reasoning questions.