Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019-Reference-Cited by-同舟云学术

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Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019

Published:2021 Issue: Volume:23 Page:243-256
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Rossetto Luca^ORCID,Gasser Ralph^ORCID,Lokoc Jakub^ORCID,Bailer Werner^ORCID,Schoeffmann Klaus,Muenzer Bernd^ORCID,Soucek Tomas,Nguyen Phuong Anh^ORCID,Bolettieri Paolo,Leibetseder Andreas,Vrochidis Stefanos^ORCID

Funder

Grantová Agentura České Republiky

European Unions Horizon 2020 research and innovation programme: V4Design

MARCONI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Media Technology,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6046/9296985/09037125.pdf?arnumber=9037125

Cited by 61 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lightweight hashing with contrastive self-cross XLNet and combinational similarity matching for content-based video retrieval;Australian Journal of Electrical and Electronics Engineering;2024-08-04

2. Evaluating Performance and Trends in Interactive Video Retrieval: Insights From the 12th VBS Competition;IEEE Access;2024

3. DiveXplore at the Video Browser Showdown 2024;Lecture Notes in Computer Science;2024

4. PraK Tool: An Interactive Search Tool Based on Video Data Services;Lecture Notes in Computer Science;2024

5. A New Retrieval Engine for Vitrivr;Lecture Notes in Computer Science;2024

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