Papers Overlapped 15-1
“Overlapped 15-1” 태그가 달린 논문 10편 · 필터 해제
Mitigating 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+15Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation
In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift. Although recent works focused on these issues, ex…
Class-Incremental Semantic SegmentationContinual LearningOverlapped 100-10Overlapped 100-5+8SATS: Self-Attention Transfer for Continual Semantic Segmentation
Continually learning to segment more and more types of image regions is a desired capability for many intelligent systems. However, such continual semantic segmentation suffers from the same catastrophic forgetting issue…
Continual Semantic SegmentationKnowledge DistillationOverlapped 100-10Overlapped 10-1+7Representation Compensation Networks for Continual Semantic Segmentation
In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting. We propose to use a structural re-pa…
Class Incremental LearningContinual LearningContinual Semantic SegmentationDisjoint 10-1+15Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation
Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segment…
Class Incremental LearningContinual LearningContinual Semantic SegmentationOverlapped 10-1+4SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
This paper introduces a solid state-of-the-art baseline for a class-incremental semantic segmentation (CISS) problem. While the recent CISS algorithms utilize variants of the knowledge distillation (KD) technique to tack…
class-incremental learningClass Incremental LearningClass-Incremental Semantic SegmentationContinual Semantic Segmentation+14Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks when learning new ones. In this paper we focus on class incremental continual learning in semantic segmentation, where new catego…
Continual LearningContinual Semantic SegmentationContrastive LearningDisjoint 10-1+6PLOP: Learning without Forgetting for Continual Semantic Segmentation
Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segment…
Class Incremental LearningContinual LearningContinual Semantic SegmentationDisjoint 10-1+15Incremental Learning Techniques for Semantic Segmentation
Deep learning architectures exhibit a critical drop of performance due to catastrophic forgetting when they are required to incrementally learn new tasks. Contemporary incremental learning frameworks focus on image class…
Disjoint 10-1Disjoint 15-1Disjoint 15-5Domain 1-1+13Learning without Forgetting
When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and ret…
Class Incremental LearningContinual LearningDisjoint 10-1Disjoint 15-1+8