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We address the problem of incremental semantic segmentation (ISS) recognizing novel object/stuff categories continually without forgetting previous ones that have been learned.
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Ye Xiang, Ying Fu, Pan Ji, and Hua Huang · 2019
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Modeling the background for incremental learning in semantic segmentation
Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulo, Elisa Ricci, and Barbara Caputo · 2020
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PODNet: Pooled outputs distillation for small-tasks incremental learning
Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle · 2020
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Strip Pooling: Rethinking spatial pooling for scene parsing
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Scene parsing through ADE20K dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
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End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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FearNet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
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GDumb: A simple approach that questions our progress in continual learning
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SSUL: Semantic segmentation with unknown label for exemplar-based class-incremental learning
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PLOP: Learning without forgetting for continual semantic segmentation
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Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations
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