Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2021

Abstract

Social scientists have shown evidence that visual perceptions of urban attributes, such as safe, wealthy, and beautiful perspectives of the given cities, are highly correlated to the residents' behaviors and quality of life. Despite their significance, measuring visual perceptions of urban attributes is challenging due to the following facts: (1) Visual perceptions are subjectively contradistinctive rather than absolute. (2) Perception comparisons between image pairs are usually conducted region by region, and highly related to the specific urban attributes. And (3) the urban attributes have both the shared and specific information. To address these problems, in this article, we present a Deep inteRActive Multi-task leArning scheme, DRAMA for short. DRAMA comparatively quantifies the perceptions of urban attributes by jointly integrating the pairwise comparisons, regional interactions, and urban attribute correlations within a unified deep scheme. In DRAMA, each urban attribute is treated as a task, whereby the task-sharing and the task-specific information is fully explored. By conducting extensive experiments over a public large-scale benchmark dataset, it is demonstrated that our proposed DRAMA scheme outperforms several state-of-the-art baselines. Meanwhile, we applied the pairwise comparisons of our DRAMA model to further quantify the urban attributes and hence rank cities with respect to the given urban attributes. As a byproduct, we have released the codes and parameter settings to facilitate other researches.

Keywords

Urban perception, urban attributes, regional interactions, deep multi-task learning

Discipline

Numerical Analysis and Scientific Computing | Theory and Algorithms | Urban Studies

Publication

ACM Transactions on Multimedia Computing, Communications and Applications

Volume

17

Issue

1

First Page

1

Last Page

20

ISSN

1551-6857

Identifier

10.1145/3424115

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Publisher

Additional URL

https://doi.org//10.1145/3424115

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