Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank

Alonso, Iñigo (Universidad de Zaragoza) ; Sabater, Alberto (Universidad de Zaragoza) ; Ferstl, David ; Montesano, Luis (Universidad de Zaragoza) ; Murillo, Ana Cristina (Universidad de Zaragoza)

Resumen: This work presents a novel approach for semi-supervised semantic segmentation. The key element of this approach is our contrastive learning module that enforces the segmentation network to yield similar pixel-level feature representations for same-class samples across the whole dataset. To achieve this, we maintain a memory bank continuously updated with relevant and high-quality feature vectors from labeled data. In an end-to-end training, the features from both labeled and unlabeled data are optimized to be similar to same-class samples from the memory bank. Our approach outperforms the current state-of-the-art for semi-supervised semantic segmentation and semi-supervised domain adaptation on well-known public benchmarks, with larger improvements on the most challenging scenarios, i.e., less available labeled data.

Idioma: Inglés

DOI: 10.1109/ICCV48922.2021.00811

Año: 2021

Publicado en: Proceedings (IEEE International Conference on Computer Vision) 2021 (2021), 8219-8228
ISSN: 1550-5499

Originalmente disponible en: Texto completo de la revista

Financiación: info:eu-repo/grantAgreement/ES/DGA/T45-17R
Financiación: info:eu-repo/grantAgreement/ES/MINECO-FEDER/PGC2018-098817-A-I00

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)