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 1201–1250 of 2262 papers

TitleStatusHype
ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for Action Recognition—0
Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological ProcessingCode1
Human Instance Segmentation and Tracking via Data Association and Single-stage Detector—0
Exploring Plain Vision Transformer Backbones for Object DetectionCode2
CHEX: CHannel EXploration for CNN Model CompressionCode1
Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene SegmentationCode2
Self-Supervised Image Representation Learning with Geometric Set Consistency—0
mc-BEiT: Multi-choice Discretization for Image BERT Pre-trainingCode1
Eigencontours: Novel Contour Descriptors Based on Low-Rank ApproximationCode1
SepViT: Separable Vision TransformerCode1
MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation—0
HUNIS: High-Performance Unsupervised Nuclei Instance Segmentation—0
Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation?Code1
Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy imagesCode0
SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation—0
Sparse Instance Activation for Real-Time Instance SegmentationCode2
Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention TransformerCode1
PaCa-ViT: Learning Patch-to-Cluster Attention in Vision TransformersCode1
Test-time Adaptation with Slot-Centric ModelsCode1
ContrastMask: Contrastive Learning to Segment Every ThingCode1
Object discovery and representation networksCode0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation—0
Deformable VisTR: Spatio temporal deformable attention for video instance segmentationCode0
One-stage Video Instance Segmentation: From Frame-in Frame-out to Clip-in Clip-outCode0
Active Token MixerCode1
Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection—0
RankSeg: Adaptive Pixel Classification with Image Category Ranking for SegmentationCode1
Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention—0
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance SegmentationCode2
Boosting Mask R-CNN Performance for Long, Thin Forensic Traces with Pre-Segmentation and IoU Region Merging—0
Signature and Log-signature for the Study of Empirical Distributions Generated with GANsCode0
End-to-end video instance segmentation via spatial-temporal graph neural networksCode0
Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images—0
On Steering Multi-Annotations per Sample for Multi-Task Learning—0
Towards Robust Part-aware Instance Segmentation for Industrial Bin Picking—0
Panoptic segmentation with highly imbalanced semantic labels—0
Nuclei instance segmentation and classification in histopathology images with StarDistCode3
Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification—0
Efficient Video Instance Segmentation via Tracklet Query and Proposal—0
Instance Segmentation for Autonomous Log Grasping in Forestry OperationsCode1
SoftGroup for 3D Instance Segmentation on Point CloudsCode2
SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment—0
Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector—0
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and CountingCode0
DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN TrainingCode1
ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network—0
Confidence Calibration for Object Detection and Segmentation—0
Weakly Supervised Instance Segmentation using Motion Information via Optical Flow—0
Efficient Video Segmentation Models with Per-frame Inference—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