Papers Continual Semantic Segmentation
“Continual Semantic Segmentation” 태그가 달린 논문 39편 · 필터 해제
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 SegmentationLow-Rank Continual Pyramid Vision Transformer: Incrementally Segment Whole-Body Organs in CT with Light-Weighted Adaptation
Deep segmentation networks achieve high performance when trained on specific datasets. However, in clinical practice, it is often desirable that pretrained segmentation models can be dynamically extended to enable segmen…
Continual Semantic SegmentationOrgan SegmentationSegmentationSemantic SegmentationTaxonomy-Aware Continual Semantic Segmentation in Hyperbolic Spaces for Open-World Perception
Semantic segmentation models are typically trained on a fixed set of classes, limiting their applicability in open-world scenarios. Class-incremental semantic segmentation aims to update models with emerging new classes …
Autonomous DrivingClass-Incremental Semantic SegmentationContinual Semantic SegmentationIncremental Learning+2Strike a Balance in Continual Panoptic Segmentation
This study explores the emerging area of continual panoptic segmentation, highlighting three key balances. First, we introduce past-class backtrace distillation to balance the stability of existing knowledge with the ada…
Continual Panoptic SegmentationContinual Semantic SegmentationLearning at a Glance: Towards Interpretable Data-limited Continual Semantic Segmentation via Semantic-Invariance Modelling
Continual semantic segmentation (CSS) based on incremental learning (IL) is a great endeavour in developing human-like segmentation models. However, current CSS approaches encounter challenges in the trade-off between pr…
Continual Semantic SegmentationContrastive LearningIncremental LearningSemantic SegmentationMitigating Background Shift in Class-Incremental Semantic Segmentation
Class-Incremental Semantic Segmentation(CISS) aims to learn new classes without forgetting the old ones, using only the labels of the new classes. To achieve this, two popular strategies are employed: 1) pseudo-labeling …
Class Incremental LearningClass-Incremental Semantic SegmentationContinual LearningContinual Semantic Segmentation+15BACS: Background Aware Continual Semantic Segmentation
Semantic segmentation plays a crucial role in enabling comprehensive scene understanding for robotic systems. However, generating annotations is challenging, requiring labels for every pixel in an image. In scenarios lik…
Autonomous DrivingContinual LearningContinual Semantic SegmentationDecoder+3ConSept: Continual Semantic Segmentation via Adapter-based Vision Transformer
In this paper, we delve into the realm of vision transformers for continual semantic segmentation, a problem that has not been sufficiently explored in previous literature. Empirical investigations on the adaptation of e…
Continual Semantic SegmentationSegmentationSemantic SegmentationPrivacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery
Deep Neural Networks (DNNs) based semantic segmentation of the robotic instruments and tissues can enhance the precision of surgical activities in robot-assisted surgery. However, in biological learning, DNNs cannot lear…
Continual LearningContinual Semantic SegmentationPrivacy PreservingSegmentation+1FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding
Continual Learning in semantic scene segmentation aims to continually learn new unseen classes in dynamic environments while maintaining previously learned knowledge. Prior studies focused on modeling the catastrophic fo…
Continual LearningContinual Semantic SegmentationFairnessScene Segmentation+2A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application
Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep learning and AI systems. It breaks through the obstacle of one-way training on close sets and enables continuo…
Continual LearningContinual Semantic SegmentationIncremental LearningSemantic SegmentationRethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay
Endoscopy is a widely used technique for the early detection of diseases or robotic-assisted minimally invasive surgery (RMIS). Numerous deep learning (DL)-based research works have been developed for automated diagnosis…
Continual LearningContinual Semantic SegmentationImage SegmentationRepresentation Learning+1SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers
This paper investigates the capability of plain Vision Transformers (ViTs) for semantic segmentation using the encoder-decoder framework and introduces \textbf{SegViTv2}. In this study, we introduce a novel Attention-to-…
Continual LearningContinual Semantic SegmentationDecoderSegmentation+1Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes. Although prior studies have shown impressive progress in recent years, the fairness concern in the co…
Continual LearningContinual Semantic SegmentationFairnessKnowledge Distillation+3Continual Semantic Segmentation with Automatic Memory Sample Selection
Continual Semantic Segmentation (CSS) extends static semantic segmentation by incrementally introducing new classes for training. To alleviate the catastrophic forgetting issue in CSS, a memory buffer that stores a small…
Continual Semantic SegmentationDecision MakingDiversitySemantic Segmentation