Enhancing Open-Vocabulary Semantic Segmentation with Prototype Retrieval-Reference-Cited by-同舟云学术

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Enhancing Open-Vocabulary Semantic Segmentation with Prototype Retrieval

Published:2023 Issue: Volume: Page:196-208
ISSN:0302-9743
Container-title:Image Analysis and Processing – ICIAP 2023
language:
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Author:

Barsellotti Luca^ORCID,Amoroso Roberto^ORCID,Baraldi Lorenzo^ORCID,Cucchiara Rita^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-43153-1_17

Reference30 articles.

1. Lecture Notes in Computer Science;R Amoroso,2021

2. Lecture Notes in Computer Science;R Amoroso,2021

3. Lecture Notes in Computer Science;P Bruno,2022

4. Cancilla, M., et al.: The DeepHealth toolkit: a unified framework to boost biomedical applications. In: ICPR (2021)

5. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: Semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. TPAMI 27, 834–848 (2017)

Cited by 1 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. FOSSIL: Free Open-Vocabulary Semantic Segmentation through Synthetic References Retrieval;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

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