Learning Furniture Compatibility with Graph Neural Networks Article Swipe

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Luisa F. Polanía , Mauricio Flores , Yiran Li , Matthew Nokleby · YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1109/cvprw50498.2020.00191

We propose a graph neural network (GNN) approach to the problem of predicting the stylistic compatibility of a set of furniture items from images. While most existing results are based on siamese networks which evaluate pairwise compatibility between items, the proposed GNN architecture exploits relational information among groups of items. We present two GNN models, both of which comprise a deep CNN that extracts a feature representation for each image, a gated recurrent unit (GRU) network that models interactions between the furniture items in a set, and an aggregation function that calculates the compatibility score. In the first model, a generalized contrastive loss function that promotes the generation of clustered embeddings for items belonging to the same furniture set is introduced. Also, in the first model, the edge function between nodes in the GRU and the aggregation function are fixed in order to limit model complexity and allow training on smaller datasets; in the second model, the edge function and aggregation function are learned directly from the data. We demonstrate state-of-the art accuracy for compatibility prediction and "fill in the blank" tasks on the Bonn and Singapore furniture datasets. We further introduce a new dataset, called the Target Furniture Collections dataset, which contains over 6000 furniture items that have been hand-curated by stylists to make up 1632 compatible sets. We also demonstrate superior prediction accuracy on this dataset.

Type

preprint

Language

en

Landing Page

https://doi.org/10.1109/cvprw50498.2020.00191

OA Status

green

Cited By

3

References

34

Related Works

20

OpenAlex ID

https://openalex.org/W3017140696

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OpenAlex ID

https://openalex.org/W3017140696

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DOI

https://doi.org/10.1109/cvprw50498.2020.00191

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Title

Learning Furniture Compatibility with Graph Neural Networks

Work title

Type

preprint

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Language

en

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Publication year

2020

Year of publication

Publication date

2020-06-01

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Authors

Luisa F. Polanía, Mauricio Flores, Yiran Li, Matthew Nokleby

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Landing page

https://doi.org/10.1109/cvprw50498.2020.00191

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Open access

Yes

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OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/2004.07268

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Concepts

Computer science, Pairwise comparison, Compatibility (geochemistry), Artificial intelligence, Exploit, Graph, Architecture, Machine learning, Data mining, Pattern recognition (psychology), Theoretical computer science, Engineering, Art, Chemical engineering, Visual arts, Computer security

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Cited by

3

Total citation count in OpenAlex

Citations by year (recent)

2023: 1, 2021: 1, 2020: 1

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References (count)

34

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Related works (count)

20

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