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 201250 of 2262 papers

TitleStatusHype
Effective Self-supervised Pre-training on Low-compute Networks without DistillationCode1
Efficient Connectivity-Preserving Instance Segmentation with Supervoxel-Based Loss FunctionCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-raysCode1
A Structure-Aware Relation Network for Thoracic Diseases Detection and SegmentationCode1
CompFeat: Comprehensive Feature Aggregation for Video Instance SegmentationCode1
EDAPS: Enhanced Domain-Adaptive Panoptic SegmentationCode1
DVIS++: Improved Decoupled Framework for Universal Video SegmentationCode1
Compositional Human-Scene Interaction Synthesis with Semantic ControlCode1
Conditional Convolutions for Instance SegmentationCode1
Conformer: Local Features Coupling Global Representations for Visual RecognitionCode1
Asymmetric Patch Sampling for Contrastive LearningCode1
Deep learning approaches to building rooftop thermal bridge detection from aerial imagesCode1
Container: Context Aggregation NetworksCode1
DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic ConvolutionCode1
A Tri-Layer Plugin to Improve Occluded DetectionCode1
Contextual Transformer Networks for Visual RecognitionCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
Complete Instances Mining for Weakly Supervised Instance SegmentationCode1
Coherent Reconstruction of Multiple Humans from a Single ImageCode1
DynaMask: Dynamic Mask Selection for Instance SegmentationCode1
Attention-guided Context Feature Pyramid Network for Object DetectionCode1
1st Place Solutions for OpenImage2019 -- Object Detection and Instance SegmentationCode1
HCFormer: Unified Image Segmentation with Hierarchical ClusteringCode1
Audio-Visual Instance SegmentationCode1
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