Papers Generalized Few-Shot Semantic Segmentation
“Generalized Few-Shot Semantic Segmentation” 태그가 달린 논문 15편 · 필터 해제
Training-Free Generalized Few-Shot Segmentation through Open-Vocabulary Semantic Arbitration
Generalized Few-Shot Semantic Segmentation (GFSS) has traditionally been approached as a representation-learning problem, requiring task-specific adaptation to incorporate novel classes from limited support examples. Rec…
Generalized Few-Shot Semantic SegmentationMake It Up: Fake Images, Real Gains in Generalized Few-shot Semantic Segmentation
Generalized few-shot semantic segmentation (GFSS) is fundamentally limited by the coverage of novel-class appearances under scarce annotations. While diffusion models can synthesize novel-class images at scale, practical…
Generalized Few-Shot Semantic SegmentationEnhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge Transfer
Generalized few-shot semantic segmentation (GFSS) aims to segment objects of both base and novel classes, using sufficient samples of base classes and few samples of novel classes. Representative GFSS approaches typicall…
Classifier calibrationFew-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationSemantic Segmentation+1A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation
The goal of generalized few-shot semantic segmentation (GFSS) is to recognize novel-class objects through training with a few annotated examples and the base-class model that learned the knowledge about the base classes.…
Few-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationInductive LearningSegmentation+1Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark
Learning with limited labelled data is a challenging problem in various applications, including remote sensing. Few-shot semantic segmentation is one approach that can encourage deep learning models to learn from few lab…
Few-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationSegmentationSemantic SegmentationApplying ViT in Generalized Few-shot Semantic Segmentation
This paper explores the capability of ViT-based models under the generalized few-shot semantic segmentation (GFSS) framework. We conduct experiments with various combinations of backbone models, including ResNets and pre…
DecoderFew-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationSegmentation+1Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach
The emergence of attention-based transformer models has led to their extensive use in various tasks, due to their superior generalization and transfer properties. Recent research has demonstrated that such models, when p…
DecoderGeneralized Few-Shot Semantic SegmentationSemantic SegmentationVisual PromptingPrototypical Kernel Learning and Open-set Foreground Perception for Generalized Few-shot Semantic Segmentation
Generalized Few-shot Semantic Segmentation (GFSS) extends Few-shot Semantic Segmentation (FSS) to simultaneously segment unseen classes and seen classes during evaluation. Previous works leverage additional branch or pro…
Few-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationSegmentationSemantic SegmentationHarmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation
Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes. To overcome this limitation, the task of generalized f…
Few-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationSegmentationSemantic SegmentationFew-shot 3D LiDAR Semantic Segmentation for Autonomous Driving
In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible way to solve these issues. However, curren…
Autonomous DrivingFew-Shot LearningFew-Shot Semantic SegmentationGeneralized Few-Shot Semantic Segmentation+4Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation
Generalized few-shot semantic segmentation (GFSS) distinguishes pixels of base and novel classes from the background simultaneously, conditioning on sufficient data of base classes and a few examples from novel class…
Generalized Few-Shot Semantic SegmentationA Strong Baseline for Generalized Few-Shot Semantic Segmentation
This paper introduces a generalized few-shot segmentation framework with a straightforward training process and an easy-to-optimize inference phase. In particular, we propose a simple yet effective model based on the wel…
Few-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationSegmentationSemantic SegmentationPrediction Calibration for Generalized Few-shot Semantic Segmentation
Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e.g., 1-5) training images per class. C…
Few-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationPredictionSemantic SegmentationGeneralized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning
Generalized few-shot semantic segmentation was introduced to move beyond only evaluating few-shot segmentation models on novel classes to include testing their ability to remember base classes. While the current state-of…
AllFew-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationMeta-Learning+3Generalized Few-shot Semantic Segmentation
Training semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. Few-Shot Segmentation (FS-Seg) tackles this problem …
Few-Shot Semantic SegmentationGeneralized Few-Shot Semantic SegmentationSegmentationSemantic Segmentation