Fine-grained Self-supervision for Generalizable Semantic Segmentation - Université de Rennes Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Circuits and Systems for Video Technology Année : 2023

Fine-grained Self-supervision for Generalizable Semantic Segmentation

Yuhang Zhang
  • Fonction : Auteur
Shishun Tian
Muxin Liao
Wenbin Zou
Chen Xu
  • Fonction : Auteur

Résumé

Unsupervised domain adaptative semantic segmentation is a powerful solution for the distribution shift problem between the source and target domains. However, such methods need specified target domain data that may be unavailable in actual applications due to excess expensive collection. Generalizable semantic segmentation as a new paradigm appears in recent research, which aims to generalize well on distinct unseen domains only using source domain data. The existing methods focus on learning domain-invariant features by using global distribution alignment strategies, which may lead to a decreased discriminability of the model. To cope with this challenge, we propose a fine-grained self-supervision (FGSS) framework for generalizable semantic segmentation that takes into account both discriminability and generalizability from the perspective of the intra-class relationship. The FGSS framework contains single-view and multi-view versions. In the single-view version, we propose a fine-grained self-supervision strategy to distinguish the sub-parts of the semantic class for better class discriminability. In the multi-view version, we propose a class prototype feature enhancement strategy to generate another view (i.e. another representation of the original representation). Then, we propose a multi-view mutual supervision loss to enforce consistency between different views and further enhance the generalizability of the model. Experimental results on five widely-used datasets, i.e., GTAV, SYNTHIA, BDD100K, Cityscapes, and Mapillary, demonstrate that our FGSS framework achieves superior performance compared to state-of-the-art methods.
Fichier principal
Vignette du fichier
Zhang et al. - 2023 - Fine-grained Self-supervision for Generalizable Semantic Segmentation.pdf (7.61 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Licence : CC BY NC - Paternité - Pas d'utilisation commerciale

Dates et versions

hal-04241335 , version 1 (20-11-2023)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Yuhang Zhang, Shishun Tian, Muxin Liao, Zhengyu Zhang, Wenbin Zou, et al.. Fine-grained Self-supervision for Generalizable Semantic Segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 14 (8), pp.1-1. ⟨10.1109/TCSVT.2023.3285091⟩. ⟨hal-04241335⟩
17 Consultations
31 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More