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 1451–1500 of 2262 papers

TitleStatusHype
MISS: Memory-efficient Instance Segmentation Framework By Visual Inductive Priors Flow Propagation—0
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model—0
MobileInst: Video Instance Segmentation on the Mobile—0
MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation—0
Monocular Object Instance Segmentation and Depth Ordering with CNNs—0
MonoNext: A 3D Monocular Object Detection with ConvNext—0
MOPT: Multi-Object Panoptic Tracking—0
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation—0
MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection—0
MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression—0
MP-PolarMask: A Faster and Finer Instance Segmentation for Concave Images—0
MR-NOM: Multi-scale Resolution of Neuronal cells in Nissl-stained histological slices via deliberate Over-segmentation and Merging—0
MSFormer: A Skeleton-multiview Fusion Method For Tooth Instance Segmentation—0
MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation—0
Mudslide: A Universal Nuclear Instance Segmentation Method—0
Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing—0
Multi-lane Detection Using Instance Segmentation and Attentive Voting—0
Multimodal Contrastive Training for Visual Representation Learning—0
Multi-Object Tracking and Segmentation with a Space-Time Memory Network—0
Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNet—0
MultiPly: Reconstruction of Multiple People from Monocular Video in the Wild—0
Multi-scale attention-based instance segmentation for measuring crystals with large size variation—0
Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines—0
Multi-Stage Fusion for One-Click Segmentation—0
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation—0
Multi-Task Learning in Histo-pathology for Widely Generalizable Model—0
Multi-Task Learning via Scale Aware Feature Pyramid Networks and Effective Joint Head—0
Multi-view PointNet for 3D Scene Understanding—0
MUVA: A New Large-Scale Benchmark for Multi-View Amodal Instance Segmentation in the Shopping Scenario—0
MV-Adapter: Enhancing Underwater Instance Segmentation via Adaptive Channel Attention—0
MVTec D2S: Densely Segmented Supermarket Dataset—0
Nacala-Roof-Material: Drone Imagery for Roof Detection, Classification, and Segmentation to Support Mosquito-borne Disease Risk Assessment—0
NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images—0
NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds—0
Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data—0
Neural Mesh Refiner for 6-DoF Pose Estimation—0
NINEPINS: Nuclei Instance Segmentation with Point Annotations—0
Nondiscriminatory Treatment: a straightforward framework for multi-human parsing—0
Non-Hierarchical Transformers for Pedestrian Segmentation—0
Non-local RoI for Cross-Object Perception—0
Non-local RoIs for Instance Segmentation—0
NOVIS: A Case for End-to-End Near-Online Video Instance Segmentation—0
Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework—0
Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review—0
Nuclei Instance Segmentation of Cryosectioned H&E Stained Histological Images using Triple U-Net Architecture—0
NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation—0
NuClick: From Clicks in the Nuclei to Nuclear Boundaries—0
NVSMask3D: Hard Visual Prompting with Camera Pose Interpolation for 3D Open Vocabulary Instance Segmentation—0
OAFormer: Learning Occlusion Distinguishable Feature for Amodal Instance Segmentation—0
Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition—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