RobustRepStream: Robust stream clustering using self-controlled connectivity graph

Callister, Ross; Lazarescu, Mihai; Pham, Duc-Son

doi:10.3233/ida-194715

IDA

2020

DOI: 10.3233/ida-194715

|View full text |Cite

RobustRepStream: Robust stream clustering using self-controlled connectivity graph

Ross Callister

Mihai Lazarescu

Duc-Son Pham

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Urban street scene analysis using lightweight multi-level multi-path feature aggregation network

Singha¹,

Pham²,

Krishna³

2021

MGS

View full text Add to dashboard Cite

Urban street scene analysis is an important problem in computer vision with many off-line models achieving outstanding semantic segmentation results. However, it is an ongoing challenge for the research community to develop and optimize the deep neural architecture with real-time low computing requirements whilst maintaining good performance. Balancing between model complexity and performance has been a major hurdle with many models dropping too much accuracy for a slight reduction in model size and unable to handle high-resolution input images. The study aims to address this issue with a novel model, named M2FANet, that provides a much better balance between model’s efficiency and accuracy for scene segmentation than other alternatives. The proposed optimised backbone helps to increase model’s efficiency whereas, suggested Multi-level Multi-path (M2) feature aggregation approach enhances model’s performance in the real-time environment. By exploiting multi-feature scaling technique, M2FANet produces state-of-the-art results in resource-constrained situations by handling full input resolution. On the Cityscapes benchmark data set, the proposed model produces 68.5% and 68.3% class accuracy on validation and test sets respectively, whilst having only 1.3 million parameters. Compared with all real-time models of less than 5 million parameters, the proposed model is the most competitive in both performance and real-time capability.

show abstract

Urban street scene analysis using lightweight multi-level multi-path feature aggregation network

Singha¹,

Pham²,

Krishna³

2021

MGS

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

RobustRepStream: Robust stream clustering using self-controlled connectivity graph

Cited by 1 publication

References 35 publications

Urban street scene analysis using lightweight multi-level multi-path feature aggregation network

Urban street scene analysis using lightweight multi-level multi-path feature aggregation network

Contact Info

Product

Resources

About