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Scene Graph Reasoning for Visual Question Answering
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Scene Graph Reasoning for Visual Question Answering
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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.
Forward citations
Cited by 3 Pith papers
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SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards
Dense scene-graph-grounded rewards let a 7B multimodal LLM trained on 7K synthetic questions beat SFT and sparse-RL baselines and outscore GPT-4o on average across 12 spatial/real-world benchmarks.
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Spatial Atlas: Compute-Grounded Reasoning for Spatial-Aware Research Agent Benchmarks
Spatial Atlas implements compute-grounded reasoning via a structured scene graph engine and deterministic computations to deliver competitive accuracy on spatial QA and Kaggle ML benchmarks while preserving interpretability.
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SG-CoT: An Ambiguity-Aware Robotic Planning Framework using Scene Graph Representations
SG-CoT grounds an LLM planner's chain-of-thought in a scene graph via iterative API queries, improving ambiguity detection and clarification in simulated manipulation, though its success metric credits any clarifying ...
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