TY - JOUR
T1 - Urban perception
T2 - sensing cities via a deep interactive multi-task learning framework
AU - Guan, Weili
AU - Chen, Zhaozheng
AU - Feng, Fuli
AU - Liu, Weifeng
AU - Nie, Liqiang
N1 - Publisher Copyright:
© 2021 Association for Computing Machinery.
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - deep multi-task learning
KW - regional interactions
KW - urban attributes
KW - Urban perception
UR - http://www.scopus.com/inward/record.url?scp=85105016613&partnerID=8YFLogxK
U2 - 10.1145/3424115
DO - 10.1145/3424115
M3 - Article
AN - SCOPUS:85105016613
SN - 1551-6857
VL - 17
JO - ACM Transactions on Multimedia Computing, Communications and Applications
JF - ACM Transactions on Multimedia Computing, Communications and Applications
IS - 1s
M1 - 13
ER -