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 51–100 of 2262 papers

TitleStatusHype
NVSMask3D: Hard Visual Prompting with Camera Pose Interpolation for 3D Open Vocabulary Instance Segmentation—0
Occlusion-Ordered Semantic Instance Segmentation—0
Single-shot Star-convex Polygon-based Instance Segmentation for Spatially-correlated Biomedical Objects—0
CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting—0
CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image—0
Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data GenerationCode0
P2Object: Single Point Supervised Object Detection and Instance SegmentationCode2
Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian SplattingCode1
S^4M: Boosting Semi-Supervised Instance Segmentation with SAM—0
BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance SegmentationCode0
Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline ResultsCode1
APSeg: Auto-Prompt Model with Acquired and Injected Knowledge for Nuclear Instance Segmentation and Classification—0
Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite ImageryCode2
Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology—0
Scene-Centric Unsupervised Panoptic SegmentationCode2
v-CLR: View-Consistent Learning for Open-World Instance SegmentationCode1
RipVIS: Rip Currents Video Instance Segmentation Benchmark for Beach Monitoring and Safety—0
CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and ClassificationCode1
Pre-training with 3D Synthetic Data: Learning 3D Point Cloud Instance Segmentation from 3D Synthetic Scenes—0
Foveated Instance SegmentationCode0
Prompting Vision-Language Model for Nuclei Instance Segmentation and ClassificationCode0
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery—0
Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines—0
HiRes-FusedMIM: A High-Resolution RGB-DSM Pre-trained Model for Building-Level Remote Sensing Applications—0
EgoSurgery-HTS: A Dataset for Egocentric Hand-Tool Segmentation in Open Surgery VideosCode0
A Temporal Modeling Framework for Video Pre-Training on Video Instance Segmentation—0
Should we pre-train a decoder in contrastive learning for dense prediction tasks?—0
SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments—0
Leveraging Vision-Language Models for Open-Vocabulary Instance Segmentation and TrackingCode0
Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian SplattingCode2
Ship Detection in Remote Sensing Imagery for Arbitrarily Oriented Object Detection—0
3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different SensorsCode0
COIN: Confidence Score-Guided Distillation for Annotation-Free Cell SegmentationCode0
CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence MicroscopyCode0
SAS: Segment Any 3D Scene with Integrated 2D PriorsCode1
Aligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives—0
SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model—0
Segment Anything, Even Occluded—0
S4M: Segment Anything with 4 Extreme Points—0
TomatoScanner: phenotyping tomato fruit based on only RGB imageCode0
Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches—0
Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing—0
Automatic Drywall Analysis for Progress Tracking and Quality Control in Construction—0
AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model—0
Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide ImagesCode0
Label-Efficient LiDAR Panoptic Segmentation—0
OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging—0
UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language InterfaceCode3
Training-Free Dataset Pruning for Instance SegmentationCode0
Ranking pre-trained segmentation models for zero-shot transferability—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
8GLEE-Promask AP54.2—Unverified
9ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2—Unverified
10SwinV2-G (HTC++)mask AP53.7—Unverified