BORM: Bayesian Object Relation Model for Indoor Scene Recognition

TL;DR

This paper introduces BORM, a Bayesian model that leverages object relations and prior knowledge for improved indoor scene recognition, demonstrating superior performance and generalization on standard datasets.

Contribution

The paper proposes a Bayesian object relation model (BORM) that integrates object co-occurrence and relations into scene recognition, enhancing accuracy without retraining.

Findings

Outperforms state-of-the-art methods on Places365 and SUN RGB-D datasets

Demonstrates strong generalization ability without retraining

Effectively incorporates object relations into scene understanding

Abstract

Scene recognition is a fundamental task in robotic perception. For human beings, scene recognition is reasonable because they have abundant object knowledge of the real world. The idea of transferring prior object knowledge from humans to scene recognition is significant but still less exploited. In this paper, we propose to utilize meaningful object representations for indoor scene representation. First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene. To analyze the object co-occurrences and pairwise object relations, we formulate the IOM from a Bayesian perspective as the Bayesian object relation model (BORM). Meanwhile, we incorporate the proposed BORM with the PlacesCNN model as the combined Bayesian object relation model (CBORM) for scene recognition and significantly outperforms the state-of-the-art methods on the reduced Places365 dataset, and SUN RGB-D dataset without retraining, showing the excellent generalization ability of the proposed method. Code can be found at https://github.com/hszhoushen/borm.