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 1401–1450 of 2262 papers

TitleStatusHype
Leveraging Open-Vocabulary Diffusion to Camouflaged Instance Segmentation—0
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation—0
Leveraging Superfluous Information in Contrastive Representation Learning—0
LiCAR: pseudo-RGB LiDAR image for CAR segmentation—0
Lifting by Gaussians: A Simple, Fast and Flexible Method for 3D Instance Segmentation—0
LIGHT: Joint Individual Building Extraction and Height Estimation from Satellite Images through a Unified Multitask Learning Network—0
Light-Weight Vision Transformer with Parallel Local and Global Self-Attention—0
Linear Object Detection in Document Images using Multiple Object Tracking—0
LIP: Learning Instance Propagation for Video Object Segmentation—0
Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification—0
Localized Interactive Instance Segmentation—0
LoCo: Local Contrastive Representation Learning—0
LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision—0
LoCUS: Learning Multiscale 3D-consistent Features from Posed Images—0
Look Before You Match: Instance Understanding Matters in Video Object Segmentation—0
Look in Different Views: Multi-Scheme Regression Guided Cell Instance Segmentation—0
LoTE-Animal: A Long Time-span Dataset for Endangered Animal Behavior Understanding—0
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding—0
LPSNet: A Lightweight Solution for Fast Panoptic Segmentation—0
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB—0
LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS—0
LVIS Challenge Track Technical Report 1st Place Solution: Distribution Balanced and Boundary Refinement for Large Vocabulary Instance Segmentation—0
Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation—0
MAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition—0
Marine Debris Detection in Satellite Surveillance using Attention Mechanisms—0
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors—0
MaskClustering: View Consensus based Mask Graph Clustering for Open-Vocabulary 3D Instance Segmentation—0
Mask Encoding for Single Shot Instance Segmentation—0
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations—0
Mask Frozen-DETR: High Quality Instance Segmentation with One GPU—0
MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation—0
Mask-Guided Matting in the Wild—0
Mask-guided sample selection for Semi-Supervised Instance Segmentation—0
Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection—0
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features—0
MaskPlus: Improving Mask Generation for Instance Segmentation—0
Joint Object Contour Points and Semantics for Instance Segmentation—0
Mask Propagation Network for Video Object Segmentation—0
MaskUno: Switch-Split Block For Enhancing Instance Segmentation—0
MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors—0
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation—0
McGAN: Generating Manufacturable Designs by Embedding Manufacturing Rules into Conditional Generative Adversarial Network—0
Memory Efficient Transformer Adapter for Dense Predictions—0
MeNToS: Tracklets Association with a Space-Time Memory Network—0
MetaFood3D: 3D Food Dataset with Nutrition Values—0
Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art—0
MGTUNet: An new UNet for colon nuclei instance segmentation and quantification—0
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images—0
Minimizing Labeled, Maximizing Unlabeled: An Image-Driven Approach for Video Instance Segmentation—0
Mirror-Yolo: A Novel Attention Focus, Instance Segmentation and Mirror Detection Model—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