Papers Few-shot Instance Segmentation
“Few-shot Instance Segmentation” 태그가 달린 논문 14편 · 필터 해제
Boundary-by-Mask: Few-Shot Instance Segmentation with Mask-Conditioned Boundary Learning for Texture-Poor Industrial Parts
Recent advances in large pre-trained models have led to remarkable progress in instance segmentation on general images. However, industrial scenarios remain challenging. Instance definitions are often application-specifi…
Few-shot Instance SegmentationSAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation
We propose SAM-IF, a novel method for incremental few-shot instance segmentation leveraging the Segment Anything Model (SAM). SAM-IF addresses the challenges of class-agnostic instance segmentation by introducing a multi…
DecoderFew-shot Instance SegmentationFew-Shot LearningIncremental Learning+3Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
Nucleus instance segmentation from histopathology images suffers from the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this task, annotation-efficient deep learning…
Few-shot Instance SegmentationFew-Shot LearningInstance SegmentationMeta-Learning+3CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation
While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on massive fine-annotated cell images [2] wit…
Cell SegmentationContrastive LearningFew-shot Instance SegmentationInstance Segmentation+5Instance-level Few-shot Learning with Class Hierarchy Mining
Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to effectively utilizing the relationship be…
Few-shot Instance SegmentationFew-Shot LearningInstance SegmentationSemantic SegmentationThe Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation
Camouflaged object detection and segmentation is a new and challenging research topic in computer vision. There is a serious issue of lacking data on concealed objects such as camouflaged animals in natural scenes. In th…
Camouflaged Object SegmentationFew-shot Instance SegmentationFew-Shot LearningFew-Shot Object Detection+2MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation
Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories. Conve…
Few-shot Instance SegmentationFew-Shot LearningInstance SegmentationSemantic SegmentationReference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
Few-Shot Instance Segmentation (FSIS) requires detecting and segmenting novel classes with limited support examples. Existing methods based on Region Proposal Networks (RPNs) face two issues: 1) Overfitting suppresses no…
BenchmarkingFew-shot Instance SegmentationFew-Shot Object DetectionInstance Segmentation+5Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmenter
This paper introduces a new problem in 3D point cloud: few-shot instance segmentation. Given a few annotated point clouds exemplified a target class, our goal is to segment all instances of this target class in a query p…
Few-shot Instance SegmentationInstance SegmentationSegmentationSemantic SegmentationiFS-RCNN: An Incremental Few-shot Instance Segmenter
This paper addresses incremental few-shot instance segmentation, where a few examples of new object classes arrive when access to training examples of old classes is not available anymore, and the goal is to perform well…
Few-shot Instance SegmentationInstance SegmentationObjectSemantic SegmentationFoxInst: A Frustratingly Simple Baseline for Weakly Few-shot Instance Segmentation
We propose the first weakly-supervised few-shot instance segmentation task and a frustratingly simple but strong baseline model, FoxInst. Our work is distinguished from other approaches in that our method is trained with…
Few-shot Instance SegmentationInstance SegmentationSemantic SegmentationIncremental Few-Shot Instance Segmentation
Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of novel classes. They also require that exampl…
Few-shot Instance SegmentationInstance SegmentationSegmentationSemantic SegmentationFAPIS: A Few-shot Anchor-free Part-based Instance Segmenter
This paper is about few-shot instance segmentation, where training and test image sets do not share the same object classes. We specify and evaluate a new few-shot anchor-free part-based instance segmenter FAPIS. Our key…
Few-shot Instance SegmentationFew-Shot LearningInstance SegmentationObject+2FGN: Fully Guided Network for Few-Shot Instance Segmentation
Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for t…
Few-shot Instance SegmentationFew-Shot LearningInstance SegmentationSegmentation+1