Papers Class-Incremental Semantic Segmentation
“Class-Incremental Semantic Segmentation” 태그가 달린 논문 31편 · 필터 해제
Completely Weakly Supervised Class-Incremental Learning for Semantic Segmentation
This work addresses the task of completely weakly supervised class-incremental learning for semantic segmentation to learn segmentation for both base and additional novel classes using only image-level labels. While clas…
class-incremental learningClass Incremental LearningClass-Incremental Semantic SegmentationData Augmentation+3MultiCo3D: Multi-Label Voxel Contrast for One-Shot Incremental Segmentation of 3D Neuroimages
3D neuroimages provide a comprehensive view of brain structure and function, aiding in precise localization and functional connectivity analysis. Segmentation of white matter (WM) tracts using 3D neuroimages is vital for…
Class-Incremental Semantic SegmentationFunctional ConnectivitySegmentationSemantic SegmentationIPSeg: Image Posterior Mitigates Semantic Drift in Class-Incremental Segmentation
Class incremental learning aims to enable models to learn from sequential, non-stationary data streams across different tasks without catastrophic forgetting. In class incremental semantic segmentation (CISS), the semant…
class-incremental learningClass Incremental LearningClass-Incremental Semantic SegmentationIncremental Learning+1Adaptive Prototype Replay for Class Incremental Semantic Segmentation
Class incremental semantic segmentation (CISS) aims to segment new classes during continual steps while preventing the forgetting of old knowledge. Existing methods alleviate catastrophic forgetting by replaying distribu…
Class-Incremental Semantic SegmentationIncremental LearningSemantic SegmentationSegACIL: Solving the Stability-Plasticity Dilemma in Class-Incremental Semantic Segmentation
While deep learning has made remarkable progress in recent years, models continue to struggle with catastrophic forgetting when processing continuously incoming data. This issue is particularly critical in continual lear…
Class-Incremental Semantic SegmentationContinual LearningSemantic SegmentationCIT: Rethinking Class-incremental Semantic Segmentation with a Class Independent Transformation
Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by distilling the current knowledge and incorpo…
Class-Incremental Semantic SegmentationSegmentationSemantic SegmentationAWF: Adaptive Weight Fusion for Enhanced Class Incremental Semantic Segmentation
Class Incremental Semantic Segmentation (CISS) aims to mitigate catastrophic forgetting by maintaining a balance between previously learned and newly introduced knowledge. Existing methods, primarily based on regularizat…
Class-Incremental Semantic SegmentationKnowledge DistillationSemantic SegmentationBalanced Residual Distillation Learning for 3D Point Cloud Class-Incremental Semantic Segmentation
Class-incremental learning (CIL) enables continuous learning of new classes while mitigating catastrophic forgetting of old ones. For the performance breakthrough of CIL, it is essential yet challenging to effectively re…
class-incremental learningClass Incremental LearningClass-Incremental Semantic SegmentationIncremental Learning+2Taxonomy-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+2Early Preparation Pays Off: New Classifier Pre-tuning for Class Incremental Semantic Segmentation
Class incremental semantic segmentation aims to preserve old knowledge while learning new tasks, however, it is impeded by catastrophic forgetting and background shift issues. Prior works indicate the pivotal importance …
Class-Incremental Semantic SegmentationSemantic 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+15Background Adaptation with Residual Modeling for Exemplar-Free Class-Incremental Semantic Segmentation
Class Incremental Semantic Segmentation~(CISS), within Incremental Learning for semantic segmentation, targets segmenting new categories while reducing the catastrophic forgetting on the old categories.Besides, backgroun…
Class-Incremental Semantic SegmentationExemplar-FreeIncremental LearningKnowledge Distillation+2Towards Realistic Incremental Scenario in Class Incremental Semantic Segmentation
This paper addresses the unrealistic aspect of the commonly adopted Continuous Incremental Semantic Segmentation (CISS) scenario, termed overlapped. We point out that overlapped allows the same image to reappear in futur…
Class-Incremental Semantic SegmentationIncremental LearningSemantic SegmentationCoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation
Class incremental semantic segmentation aims to strike a balance between the model's stability and plasticity by maintaining old knowledge while adapting to new concepts. However, most state-of-the-art methods use the fr…
Class-Incremental Semantic SegmentationDiversitySemantic SegmentationInherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory
As a front-burner problem in incremental learning, class incremental semantic segmentation (CISS) is plagued by catastrophic forgetting and semantic drift. Although recent methods have utilized knowledge distillation to …
Class-Incremental Semantic SegmentationContrastive LearningIncremental LearningKnowledge Distillation+1Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation
We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to c…
Class-Incremental Semantic SegmentationKnowledge DistillationSemantic SegmentationDiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation
The Class Incremental Semantic Segmentation (CISS) extends the traditional segmentation task by incrementally learning newly added classes. Previous work has introduced generative replay, which involves replaying old cla…
Class-Incremental Semantic SegmentationSegmentationSemantic SegmentationEvolving Knowledge Mining for Class Incremental Segmentation
Class Incremental Semantic Segmentation (CISS) has been a trend recently due to its great significance in real-world applications. Although the existing CISS methods demonstrate remarkable performance, they either levera…
Class-Incremental Semantic SegmentationKnowledge DistillationSemantic SegmentationEndpoints Weight Fusion for Class Incremental Semantic Segmentation
Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic forgetting to improve discrimination. Previous work mainly exploit regularization (e.g., knowledge distillation) to maintain previous…
class-incremental learningClass Incremental LearningClass-Incremental Semantic SegmentationIncremental Learning+2Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class
Class-incremental semantic segmentation aims to incrementally learn new classes while maintaining the capability to segment old ones, and suffers catastrophic forgetting since the old-class labels are unavailable. Mo…
Class-Incremental Semantic SegmentationDecoderKnowledge DistillationSegmentation+1