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

Showing 226250 of 2262 papers

TitleStatusHype
Augmentation for small object detectionCode1
Clustering Plotted Data by Image SegmentationCode1
Contrastive Object-level Pre-training with Spatial Noise Curriculum LearningCode1
AugmenTory: A Fast and Flexible Polygon Augmentation LibraryCode1
ConvMLP: Hierarchical Convolutional MLPs for VisionCode1
CLUSTSEG: Clustering for Universal SegmentationCode1
Dynamic Convolution for 3D Point Cloud Instance SegmentationCode1
AggMask: Exploring locally aggregated learning of mask representations for instance segmentationCode1
COVID-CT-Mask-Net: Prediction of COVID-19 from CT Scans Using Regional FeaturesCode1
AutoFocusFormer: Image Segmentation off the GridCode1
AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D ScansCode1
Crossover Learning for Fast Online Video Instance SegmentationCode1
Cross-View Regularization for Domain Adaptive Panoptic SegmentationCode1
Automated Classification of Cell Shapes: A Comparative Evaluation of Shape DescriptorsCode1
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image EncodingCode1
Efficient Multi-Task RGB-D Scene Analysis for Indoor EnvironmentsCode1
CTVIS: Consistent Training for Online Video Instance SegmentationCode1
CycleMLP: A MLP-like Architecture for Dense PredictionCode1
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance SegmentationCode1
D2Det: Towards High Quality Object Detection and Instance SegmentationCode1
ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR DataCode1
Exploring The Role of Mean Teachers in Self-supervised Masked Auto-EncodersCode1
MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object DetectionCode1
ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance SegmentationCode1
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak SupervisionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-HAP5080.8Unverified
2ResNeSt-200 (multi-scale)AP5070.2Unverified
3CenterMask + VoVNetV2-99 (multi-scale)AP5066.2Unverified
4CenterMask + VoVNetV2-57 (single-scale)AP5060.8Unverified
5Co-DETRmask AP57.1Unverified
6CBNetV2 (EVA02, single-scale)mask AP56.1Unverified
7ISDA (ResNet-50)APL55.7Unverified
8EVAmask AP55.5Unverified
9FD-SwinV2-Gmask AP55.4Unverified
10Mask Frozen-DETRmask AP55.3Unverified
#ModelMetricClaimedVerifiedStatus
1InternImage-BGFLOPs501Unverified
2Co-DETRmask AP56.6Unverified
3ViT-CoMer-L (Mask RCNN, DINOv2)mask AP55.9Unverified
4InternImage-Hmask AP55.4Unverified
5EVAmask AP55Unverified
6Mask Frozen-DETRmask AP54.9Unverified
7MasK DINO (SwinL, multi-scale)mask AP54.5Unverified
8ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2Unverified
9GLEE-Promask AP54.2Unverified
10SwinV2-G (HTC++)mask AP53.7Unverified