Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144189
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChen, Zhuoen_US
dc.contributor.authorFan, Kuien_US
dc.contributor.authorWang, Shiqien_US
dc.contributor.authorDuan, Ling-Yuen_US
dc.contributor.authorLin, Weisien_US
dc.contributor.authorKot, Alexen_US
dc.date.accessioned2020-10-20T02:25:26Z-
dc.date.available2020-10-20T02:25:26Z-
dc.date.issued2019-
dc.identifier.citationChen, Z., Fan, K., Wang, S., Duan, L.-Y., Lin, W., & Kot, A. (2019). Lossy intermediate deep learning feature compression and evaluation. Proceedings of 27th ACM International Conference on Multimedia, 2414- 2422. doi:10.1145/3343031.3350849en_US
dc.identifier.isbn9781450368896-
dc.identifier.urihttps://hdl.handle.net/10356/144189-
dc.description.abstractWith the unprecedented success of deep learning in computer vision tasks, many cloud-based visual analysis applications are powered by deep learning models. However, the deep learning models are also characterized with high computational complexity and are task-specific, which may hinder the large-scale implementation of the conventional data communication paradigms. To enable a better balance among bandwidth usage, computational load and the generalization capability for cloud-end servers, we propose to compress and transmit intermediate deep learning features instead of visual signals and ultimately utilized features. The proposed strategy also provides a promising way for the standardization of deep feature coding. As the first attempt to this problem, we present a lossy compression framework and evaluation metrics for intermediate deep feature compression. Comprehensive experimental results show the effectiveness of our proposed methods and the feasibility of the proposed data transmission strategy. It is worth mentioning that the proposed compression framework and evaluation metrics have been adopted into the ongoing AVS (Audio Video Coding Standard Workgroup) - Visual Feature Coding Standard.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.rights© 2019 Association for Computing Machinery (ACM). All rights reserved. This paper was published in 27th ACM International Conference on Multimedia and is made available with permission of Association for Computing Machinery (ACM).en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleLossy intermediate deep learning feature compression and evaluationen_US
dc.typeConference Paperen
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.contributor.conference27th ACM International Conference on Multimediaen_US
dc.identifier.doi10.1145/3343031.3350849-
dc.description.versionAccepted versionen_US
dc.identifier.spage2414en_US
dc.identifier.epage2422en_US
dc.subject.keywordsFeature Compressionen_US
dc.subject.keywordsDeep Learningen_US
dc.citation.conferencelocationNice, Franceen_US
dc.description.acknowledgementThis research is supported by the NTU-PKU Joint Research Institute, a collaboration between the Nanyang Technological University (NTU), Singapore, and Peking University (PKU), China, which is sponsored by a donation from the Ng Teng Fong Charitable Foundation. The research work was done at the Rapid-Rich Object Search (ROSE) Lab at NTU. This work is also supported in part by Singapore Ministry of Education Tier-2 Fund MOE2016-T2-2-057(S), in part by the National Natural Science Foundation of China under Grant 61661146005 and Grant U1611461, in part by Hong Kong RGC Early Career Scheme 9048122 (CityU 21211018), in part by City University of Hong Kong under Grant 7200539/CS, and in part by the National Research Foundation, Prime Minister’s Office, Singapore, through the NRF-NSFC Grant, under Grant NRF2016NRF-NSFC001-098.en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:SCSE Conference Papers
Files in This Item:
File Description SizeFormat 
Lossy Intermediate Deep Learning Feature Compression and Evaluation.pdf1.6 MBAdobe PDFView/Open

PublonsTM
Citations 20

2
Updated on Mar 6, 2021

Page view(s)

23
Updated on Apr 15, 2021

Download(s)

4
Updated on Apr 15, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.