Continual Semantic Segmentation
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Benchmarks
Most implemented
Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation
PLOP: Learning without Forgetting for Continual Semantic Segmentation
Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation
Strike a Balance in Continual Panoptic Segmentation
Learning at a Glance: Towards Interpretable Data-limited Continual Semantic Segmentation via Semantic-Invariance Modelling
Mitigating Background Shift in Class-Incremental Semantic Segmentation
Papers
Continual Segmentation under Joint Nonstationarity
Evolving data streams induce joint nonstationarity in continual semantic segmentation, where semantic classes, input distributions, and supervision availability change simultaneously over time. This setting reflects prac…
Continual Semantic SegmentationStructured PredictionContinual LearningMILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities
Continual semantic segmentation requires models to adapt to new domains or modalities without sacrificing performance on previously learned tasks. Expert-based learning, in which task-specific modules specialize in diffe…
Continual Semantic SegmentationZero-Forgetting CISS via Dual-Phase Cognitive Cascades
Continual semantic segmentation (CSS) is a cornerstone task in computer vision that enables a large number of downstream applications, but faces the catastrophic forgetting challenge. In conventional class-incremental se…
Continual Semantic SegmentationContinual LearningDecoupling Continual Semantic Segmentation
Continual Semantic Segmentation (CSS) requires learning new classes without forgetting previously acquired knowledge, addressing the fundamental challenge of catastrophic forgetting in dense prediction tasks. However, ex…
Continual Semantic SegmentationContinual LearningRevisiting Continual Semantic Segmentation with Pre-trained Vision Models
Continual Semantic Segmentation (CSS) seeks to incrementally learn to segment novel classes while preserving knowledge of previously encountered ones. Recent advancements in CSS have been largely driven by the adoption o…
Continual Semantic SegmentationBeyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation
In this work, we focus on continual semantic segmentation (CSS), where segmentation networks are required to continuously learn new classes without erasing knowledge of previously learned ones. Although storing images of…
Continual Semantic SegmentationSemantic Segmentation