Scene Graph Reasoning for Visual Question Answering

Abstract

Visual question answering is concerned with answering free-form questions about an image. Since it requires a deep linguistic understanding of the question and the ability to associate it with various objects that are present in the image, it is an ambitious task and requires techniques from both computer vision and natural language processing. We propose a novel method that approaches the task by performing context-driven, sequential reasoning based on the objects and their semantic and spatial relationships present in the scene. As a first step, we derive a scene graph which describes the objects in the image, as well as their attributes and their mutual relationships. A reinforcement agent then learns to autonomously navigate over the extracted scene graph to generate paths, which are then the basis for deriving answers. We conduct a first experimental study on the challenging GQA dataset with manually curated scene graphs, where our method almost reaches the level of human performance.

ICML Workshop Graph Representation Learning and Beyond (GRL+)

References in corpus (6)

• Semi-Supervised Classification with Graph Convolutional Networks
• VQA: Visual Question Answering
• EfficientDet: Scalable and Efficient Object Detection
• Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering
• Reasoning on Knowledge Graphs with Debate Dynamics
• Relation Transformer Network

Cited by in corpus (5)

• Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
• Learning through structure: towards deep neuromorphic knowledge graph embeddings
• Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
• Relation Transformer Network
• Scenes and Surroundings: Scene Graph Generation using Relation Transformer