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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1176–1200 of 2262 papers

TitleStatusHype
Enhancing Cell Instance Segmentation in Scanning Electron Microscopy Images via a Deep Contour Closing Operator—0
Enhancing Nucleus Segmentation with HARU-Net: A Hybrid Attention Based Residual U-Blocks Network—0
Enhancing Representations through Heterogeneous Self-Supervised Learning—0
Enhancing Transformers Through Conditioned Embedded Tokens—0
ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation—0
Ensembling Instance and Semantic Segmentation for Panoptic Segmentation—0
Entropy Bootstrapping for Weakly Supervised Nuclei Detection—0
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines—0
EPContrast: Effective Point-level Contrastive Learning for Large-scale Point Cloud Understanding—0
Equalization Loss for Large Vocabulary Instance Segmentation—0
ETHSeg: An Amodel Instance Segmentation Network and a Real-World Dataset for X-Ray Waste Inspection—0
Evaluation of Deep Learning Topcoders Method for Neuron Individualization in Histological Macaque Brain Section—0
Evaluation of Video Coding for Machines without Ground Truth—0
Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks—0
Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey—0
Exemplar-FreeSOLO: Enhancing Unsupervised Instance Segmentation With Exemplars—0
A Novel Technique for Evidence based Conditional Inference in Deep Neural Networks via Latent Feature Perturbation—0
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning—0
Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models—0
Exploring Set Similarity for Dense Self-supervised Representation Learning—0
Exploring the Sim2Real Gap Using Digital Twins—0
Exploring Transformers for Open-world Instance Segmentation—0
Extreme Point Supervised Instance Segmentation—0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
Fast and Precise Binary Instance Segmentation of 2D Objects for Automotive Applications—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