Papers Open-World Instance Segmentation
“Open-World Instance Segmentation” 태그가 달린 논문 14편 · 필터 해제
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 SegmentationOpenInst: A Simple Query-Based Method for Open-World Instance Segmentation
Open-world instance segmentation has recently gained significant popularitydue to its importance in many real-world applications, such as autonomous driving, robot perception, and remote sensing. However, previous method…
Autonomous DrivingInstance SegmentationOpen-World Instance SegmentationSegmentation+1Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision
Many top-down architectures for instance segmentation achieve significant success when trained and tested on pre-defined closed-world taxonomy. However, when deployed in the open world, they exhibit notable bias towards …
Instance SegmentationOpen-World Instance SegmentationSegmentationSemantic SegmentationOpen-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models
We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion …
Open Vocabulary Panoptic SegmentationOpen Vocabulary Semantic SegmentationOpen-World Instance SegmentationPanoptic Segmentation+3ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data
Open-world Instance Segmentation (OIS) is a challenging task that aims to accurately segment every object instance appearing in the current observation, regardless of whether these instances have been labeled in the trai…
Autonomous NavigationClusteringInstance SegmentationOpen-World Instance Segmentation+3Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization
Open-World Instance Segmentation (OWIS) is an emerging research topic that aims to segment class-agnostic object instances from images. The mainstream approaches use a two-stage segmentation framework, which first locate…
Autonomous DrivingInstance SegmentationObjectOpen-World Instance Segmentation+2Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity
Open-world instance segmentation is the task of grouping pixels into object instances without any pre-determined taxonomy. This is challenging, as state-of-the-art methods rely on explicit class semantics obtained from l…
DiversityInstance SegmentationOpen-World Instance SegmentationSemantic SegmentationLearning to Detect Every Thing in an Open World
Many open-world applications require the detection of novel objects, yet state-of-the-art object detection and instance segmentation networks do not excel at this task. The key issue lies in their assumption that regions…
Data AugmentationInstance Segmentationobject-detectionObject Detection+2Learning to Better Segment Objects from Unseen Classes with Unlabeled Videos
The ability to localize and segment objects from unseen classes would open the door to new applications, such as autonomous object learning in active vision. Nonetheless, improving the performance on unseen classes requi…
Instance SegmentationObjectOpen-World Instance SegmentationSemantic Segmentation+2