SOTAVerified

Instance Segmentation

Instance Segmentation is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object. The goal of instance segmentation is to produce a pixel-wise segmentation map of the image, where each pixel is assigned to a specific object instance.

Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers, CVPR'21

Papers

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Showing 1851–1900 of 2262 papers

TitleStatusHype
Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition—0
Object as Distribution—0
Object Detection Free Instance Segmentation With Labeling Transformations—0
Object-Guided Instance Segmentation for Biological Images—0
ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for Action Recognition—0
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network—0
Object Segmentation with Audio Context—0
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge—0
Occlusion-Ordered Semantic Instance Segmentation—0
Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree Canopies—0
Occlusion-Resistant Instance Segmentation of Piglets in Farrowing Pens Using Center Clustering Network—0
OccuSeg: Occupancy-aware 3D Instance Segmentation—0
Offline-to-Online Knowledge Distillation for Video Instance Segmentation—0
Offshore Wind Plant Instance Segmentation Using Sentinel-1 Time Series, GIS, and Semantic Segmentation Models—0
OG: Equip vision occupancy with instance segmentation and visual grounding—0
OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images—0
On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics—0
On generalisability of segment anything model for nuclear instance segmentation in histology images—0
OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging—0
Online Video Instance Segmentation via Robust Context Fusion—0
On Mutual Information in Contrastive Learning for Visual Representations—0
On Steering Multi-Annotations per Sample for Multi-Task Learning—0
On the Efficacy of Multi-scale Data Samplers for Vision Applications—0
On the Importance of Visual Context for Data Augmentation in Scene Understanding—0
OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation—0
Open-Ended 3D Point Cloud Instance Segmentation—0
OpenInst: A Simple Query-Based Method for Open-World Instance Segmentation—0
QANet -- Quality Assurance Network for Image Segmentation—0
Open-Vocabulary High-Resolution 3D (OVHR3D) Data Segmentation and Annotation Framework—0
Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision—0
Optimization of Autonomous Driving Image Detection Based on RFAConv and Triplet Attention—0
Optimizing Drug Delivery in Smart Pharmacies: A Novel Framework of Multi-Stage Grasping Network Combined with Adaptive Robotics Mechanism—0
Organ At Risk Segmentation with Multiple Modality—0
Oriented Boxes for Accurate Instance Segmentation—0
OSIS: Efficient One-stage Network for 3D Instance Segmentation—0
Outline-Guided Object Inpainting with Diffusion Models—0
OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning—0
P2Seg: Pointly-supervised Segmentation via Mutual Distillation—0
PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection—0
PanDA: Panoptic Data Augmentation—0
PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things—0
Panoptic Instance Segmentation on Pigs—0
Panoptic One-Click Segmentation: Applied to Agricultural Data—0
Panoptic Out-of-Distribution Segmentation—0
Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation—0
Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap—0
PanopticRecon: Leverage Open-vocabulary Instance Segmentation for Zero-shot Panoptic Reconstruction—0
Panoptic Segmentation of Mammograms with Text-To-Image Diffusion Model—0
Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network—0
Panoptic segmentation with highly imbalanced semantic labels—0
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All datasetsCOCO test-devCOCO minivalLVIS v1.0 valCityscapes valADE20K valARMBenchOccluded COCOOoDISSeparated COCOTBBRBDD100K valCOCO val (panoptic labels)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-HAP5080.8—Unverified
2ResNeSt-200 (multi-scale)AP5070.2—Unverified
3CenterMask + VoVNetV2-99 (multi-scale)AP5066.2—Unverified
4CenterMask + VoVNetV2-57 (single-scale)AP5060.8—Unverified
5Co-DETRmask AP57.1—Unverified
6CBNetV2 (EVA02, single-scale)mask AP56.1—Unverified
7ISDA (ResNet-50)APL55.7—Unverified
8EVAmask AP55.5—Unverified
9FD-SwinV2-Gmask AP55.4—Unverified
10Mask Frozen-DETRmask AP55.3—Unverified
#ModelMetricClaimedVerifiedStatus
1InternImage-BGFLOPs501—Unverified
2Co-DETRmask AP56.6—Unverified
3ViT-CoMer-L (Mask RCNN, DINOv2)mask AP55.9—Unverified
4InternImage-Hmask AP55.4—Unverified
5EVAmask AP55—Unverified
6Mask Frozen-DETRmask AP54.9—Unverified
7MasK DINO (SwinL, multi-scale)mask AP54.5—Unverified
8ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2—Unverified
9GLEE-Promask AP54.2—Unverified
10SwinV2-G (HTC++)mask AP53.7—Unverified