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

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Showing 1151–1200 of 2262 papers

TitleStatusHype
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation—0
Dual-stream Network for Visual Recognition—0
Dual-View Selective Instance Segmentation Network for Unstained Live Adherent Cells in Differential Interference Contrast Images—0
Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention—0
Dynamic Y-KD: A Hybrid Approach to Continual Instance Segmentation—0
DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformer—0
Edge-Aware 3D Instance Segmentation Network with Intelligent Semantic Prior—0
Eff-3DPSeg: 3D organ-level plant shoot segmentation using annotation-efficient point clouds—0
Effective Defect Detection Using Instance Segmentation for NDI—0
Efficient 3D Instance Mapping and Localization with Neural Fields—0
Efficient Column Generation for Cell Detection and Segmentation—0
EfficientLPS: Efficient LiDAR Panoptic Segmentation—0
Efficient Microscopic Image Instance Segmentation for Food Crystal Quality Control—0
Efficient Pose and Cell Segmentation using Column Generation—0
Efficient Video Instance Segmentation via Tracklet Query and Proposal—0
Efficient Video Segmentation Models with Per-frame Inference—0
EffiPerception: an Efficient Framework for Various Perception Tasks—0
EipFormer: Emphasizing Instance Positions in 3D Instance Segmentation—0
Embedded Vision for Self-Driving on Forest Roads—0
Embodied Uncertainty-Aware Object Segmentation—0
EMPIRICAL UPPER BOUND IN OBJECT DETECTION—0
End-to-End 3D Point Cloud Instance Segmentation Without Detection—0
End-to-End Deep Convolutional Active Contours for Image Segmentation—0
End-to-End Instance Edge Detection—0
A General Deep Learning framework for Neuron Instance Segmentation based on Efficient UNet and Morphological Post-processing—0
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