# Symbolic Graph Inference for Compound Scene Understanding

Source: [Symbolic Graph Inference for Compound Scene Understanding](<https://www.sota2.com/research/paper/68fa75781e209131cea682aa>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:35:52.229Z

Authors: FNU Aryan, Simon Stepputtis, Sarthak Bhagat, Joseph Campbell, Kwonjoon Lee, Hossein Nourkhiz Mahjoub, Katia Sycara

Published: 2024-10-30T01:06:12.000Z

## Abstract

Scene understanding is a fundamental capability needed in many domains, ranging from question-answering to robotics. Unlike recent end-to-end approaches that must explicitly learn varying compositions of the same scene, our method reasons over their constituent objects and analyzes their arrangement to infer a scene's meaning. We propose a novel approach that reasons over a scene's scene- and knowledge-graph, capturing spatial information while being able to utilize general domain knowledge in a joint graph search. Empirically, we demonstrate the feasibility of our method on the ADE20K dataset and compare it to current scene understanding approaches.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2410.22626>)

[PDF](<https://arxiv.org/pdf/2410.22626.pdf>)