Open-World Instance Segmentation
6 papers with code • 1 benchmarks • 1 datasets
UVO
Most implemented papers
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity
facebookresearch/Generic-Grouping
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CVPR 2022
From PA we construct a large set of pseudo-ground-truth instance masks; combined with human-annotated instance masks we train GGNs and significantly outperform the SOTA on open-world instance segmentation on various benchmarks including COCO, LVIS, ADE20K, and UVO.
Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization
showlab/sois
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Based on the single-stage instance segmentation framework, we propose a regularization model to predict foreground pixels and use its relation to instance segmentation to construct a cross-task consistency loss.
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
Our approach outperforms the previous state of the art by significant margins on both open-vocabulary panoptic and semantic segmentation tasks.
ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data
In this paper, we present a flexible and effective OIS framework for LiDAR point cloud that can accurately segment both known and unknown instances (i. e., seen and unseen instance categories during training).
SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
aim-uofa/segprompt
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ICCV 2023
In this work, we propose a novel training mechanism termed SegPrompt that uses category information to improve the model's class-agnostic segmentation ability for both known and unknown categories.
General 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.
