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 19762000 of 2262 papers

TitleStatusHype
Weakly Supervised Instance Segmentation using the Bounding Box Tightness PriorCode0
Point Cloud Instance Segmentation using Probabilistic Embeddings0
Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards0
PanDA: Panoptic Data Augmentation0
Deeply Shape-guided Cascade for Instance SegmentationCode0
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic SegmentationCode0
All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting0
Rethinking Normalization and Elimination Singularity in Neural NetworksCode0
Object-Guided Instance Segmentation for Biological Images0
SOGNet: Scene Overlap Graph Network for Panoptic SegmentationCode0
Oriented Boxes for Accurate Instance Segmentation0
Enhancing Generic Segmentation with Learned Region RepresentationsCode0
SimVODIS: Simultaneous Visual Odometry, Object Detection, and Instance SegmentationCode0
Equalization Loss for Large Vocabulary Instance Segmentation0
Recurrent Instance Segmentation using Sequences of Referring Expressions0
PPR-Net:Point-wise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Bin-picking ScenariosCode0
Single-Shot Panoptic Segmentation0
Towards Good Practices for Instance Segmentation0
Team PFDet's Methods for Open Images Challenge 20190
Learning to Track Any Object0
Identifying Unknown Instances for Autonomous Driving0
Organ At Risk Segmentation with Multiple Modality0
What's in my Room? Object Recognition on Indoor Panoramic Images0
Panoptic-DeepLabCode0
Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation0
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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