Open-World Instance Segmentation
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Benchmarks
UVO
Most implemented
Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization
v-CLR: View-Consistent Learning for Open-World Instance Segmentation
SOS: Segment Object System for Open-World Instance Segmentation With Object Priors
General Object Foundation Model for Images and Videos at Scale
OpenInst: A Simple Query-Based Method for Open-World Instance Segmentation
Papers
v-CLR: View-Consistent Learning for Open-World Instance Segmentation
In this paper, we address the challenging problem of open-world instance segmentation. Existing works have shown that vanilla visual networks are biased toward learning appearance information, \eg texture, to recognize o…
Instance SegmentationObjectOpen-World Instance SegmentationSemantic SegmentationLifting by Gaussians: A Simple, Fast and Flexible Method for 3D Instance Segmentation
We introduce Lifting By Gaussians (LBG), a novel approach for open-world instance segmentation of 3D Gaussian Splatted Radiance Fields (3DGS). Recently, 3DGS Fields have emerged as a highly efficient and explicit alterna…
3DGS3D Instance Segmentation3D Semantic SegmentationInstance Segmentation+4SOS: Segment Object System for Open-World Instance Segmentation With Object Priors
We propose an approach for Open-World Instance Segmentation (OWIS), a task that aims to segment arbitrary unknown objects in images by generalizing from a limited set of annotated object classes during training. Our Segm…
Instance SegmentationObjectOpen-World Instance SegmentationSemantic SegmentationGeneral Object Foundation Model for Images and Videos at Scale
We present GLEE in this work, an object-level foundation model for locating and identifying objects in images and videos. Through a unified framework, GLEE accomplishes detection, segmentation, tracking, grounding, and i…
Instance SegmentationLong-tail Video Object SegmentationMulti-Object TrackingObject+8SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
Current closed-set instance segmentation models rely on pre-defined class labels for each mask during training and evaluation, largely limiting their ability to detect novel objects. Open-world instance segmentation (OWI…
Instance SegmentationOpen-World Instance SegmentationPrompt LearningSegmentation+1Exploring Transformers for Open-world Instance Segmentation
Open-world instance segmentation is a rising task, which aims to segment all objects in the image by learning from a limited number of base-category objects. This task is challenging, as the number of unseen categories c…
Contrastive LearningInstance SegmentationOpen-World Instance SegmentationSemantic Segmentation