Looking beyond single images for contrastive semantic segmentation learning
We present an approach to contrastive representation learning for semantic segmentation. Our approach leverages the representational power of existing feature extractors to find corresponding regions across images. These cross-image correspondences are used as auxiliary labels to guide the pixel-lev...
Main Authors: | Zhang, F, Torr, P, Ranftl, R, Richter, S |
---|---|
Formato: | Conference item |
Idioma: | English |
Publicado: |
Neural Information Processing Systems Foundation
2021
|
Títulos similares
-
Open vocabulary semantic segmentation with Patch Aligned Contrastive Learning
por: Mukhoti, J, et al.
Publicado: (2023) -
Scalable cascade inference for semantic image segmentation
por: Sturgess, P, et al.
Publicado: (2012) -
Dense semantic image segmentation with objects and attributes
por: Zheng, S, et al.
Publicado: (2014) -
Pyramid Context Contrast for Semantic Segmentation
por: Yuzhong Chen, et al.
Publicado: (2019-01-01) -
Highly Contrast Image Correction for Dim Boundary Separation of Image Semantic Segmentation
por: Jinyeob Choi, et al.
Publicado: (2021-01-01)