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 1276–1300 of 2262 papers

TitleStatusHype
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation—0
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation—0
Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle RepresentationCode0
Automatic Cadastral Boundary Detection of Very High Resolution Images Using Mask R-CNN—0
Deployment and Analysis of Instance Segmentation Algorithm for In-field Grade Estimation of Sweetpotatoes—0
A Unified Query-based Paradigm for Camouflaged Instance SegmentationCode0
Instance segmentation based 6D pose estimation of industrial objects using point clouds for robotic bin-picking—0
Fine-grained building roof instance segmentation based on domain adapted pretraining and composite dual-backbone—0
Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak LabelsCode0
A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision—0
Exploring Transformers for Open-world Instance Segmentation—0
1st Place Solution for CVPR2023 BURST Long Tail and Open World Challenges—0
DiT: Efficient Vision Transformers with Dynamic Token RoutingCode0
Mask Frozen-DETR: High Quality Instance Segmentation with One GPU—0
Enhancing Nucleus Segmentation with HARU-Net: A Hybrid Attention Based Residual U-Blocks Network—0
FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels—0
Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene Understanding of Healthcare Facilities—0
Weakly Supervised 3D Instance Segmentation without Instance-level Annotations—0
MonoNext: A 3D Monocular Object Detection with ConvNext—0
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding—0
A novel integrated method of detection-grasping for specific object based on the box coordinate matching—0
ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotations—0
Light-Weight Vision Transformer with Parallel Local and Global Self-Attention—0
PSGformer: Enhancing 3D Point Cloud Instance Segmentation via Precise Semantic Guidance—0
MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression—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