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 16011625 of 2262 papers

TitleStatusHype
INSTA-YOLO: Real-Time Instance Segmentation0
Exploiting Depth Information for Wildlife MonitoringCode0
Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement0
3D Object Detection and Instance Segmentation from 3D Range and 2D Color Images0
A Histogram Thresholding Improvement to Mask R-CNN for Scalable Segmentation of New and Old Rural Buildings0
Instance and Panoptic Segmentation Using Conditional Convolutions0
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection ConsistencyCode1
Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement0
Occluded Video Instance Segmentation: A BenchmarkCode1
SA-Net: Shuffle Attention for Deep Convolutional Neural NetworksCode1
Bottleneck Transformers for Visual RecognitionCode2
Nondiscriminatory Treatment: a straightforward framework for multi-human parsing0
Embedding-based Instance Segmentation in MicroscopyCode1
Simplifying Object Segmentation with PixelLib LibraryCode2
Self-Supervised Representation Learning from Flow Equivariance0
How Shift Equivariance Impacts Metric Learning for Instance SegmentationCode0
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized LabelsCode1
Superpixel-based Refinement for Object Proposal GenerationCode1
One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask FeaturesCode0
ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations0
Weakly Supervised Multi-Object Tracking and Segmentation0
CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological ImagesCode1
Continuous Copy-Paste for One-Stage Multi-Object Tracking and SegmentationCode1
CDNet: Centripetal Direction Network for Nuclear Instance SegmentationCode1
Poly-NL: Linear Complexity Non-Local Layers With 3rd Order Polynomials0
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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