Few-shot Instance Segmentation
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Benchmarks
CAMO-FS
Most implemented
Instance-level Few-shot Learning with Class Hierarchy Mining
MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
iFS-RCNN: An Incremental Few-shot Instance Segmenter
Papers
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+2