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 1801–1850 of 2262 papers

TitleStatusHype
How Shift Equivariance Impacts Metric Learning for Instance SegmentationCode0
One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask FeaturesCode0
Weakly Supervised Multi-Object Tracking and Segmentation—0
ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations—0
Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation—0
Aggregation With Feature Detection—0
Poly-NL: Linear Complexity Non-Local Layers With 3rd Order Polynomials—0
Contextual Graph Reasoning Networks—0
EMPIRICAL UPPER BOUND IN OBJECT DETECTION—0
Learning a unified label space—0
X-World: Accessibility, Vision, and Autonomy Meet—0
Robust 2D/3D Vehicle Parsing in Arbitrary Camera Views for CVISCode0
Instance Segmentation of Industrial Point Cloud Data—0
Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNet—0
A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection—0
REFINE: Prediction Fusion Network for Panoptic Segmentation—0
DeepGamble: Towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection—0
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty EstimatesCode0
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions—0
The Role of Regularization in Shaping Weight and Node Pruning Dependency and Dynamics—0
Learning Video Instance Segmentation with Recurrent Graph Neural Networks—0
Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation—0
CARAFE++: Unified Content-Aware ReAssembly of FEatures—0
BoxInst: High-Performance Instance Segmentation with Box Annotations—0
Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline—0
Single-shot Path Integrated Panoptic Segmentation—0
Single-Shot Lightweight Model For The Detection of Lesions And The Prediction of COVID-19 From Chest CT ScansCode0
Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation—0
Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction—0
The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation—0
CellSegmenter: unsupervised representation learning and instance segmentation of modular images—0
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation—0
3D Registration for Self-Occluded Objects in Context—0
Scaling Wide Residual Networks for Panoptic Segmentation—0
Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized ProteinsCode0
Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions—0
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation—0
Learning Regional Purity for Instance Segmentation on 3D Point CloudsCode0
ASIST: Annotation-free synthetic instance segmentation and tracking for microscope video analysis—0
Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds—0
2nd Place Solution to Instance Segmentation of IJCAI 3D AI Challenge 2020—0
AutoBSS: An Efficient Algorithm for Block Stacking Style Search—0
Multi-Stage Fusion for One-Click Segmentation—0
Localized Interactive Instance Segmentation—0
Learning Panoptic Segmentation from Instance ContoursCode0
A Closed-Loop System for Improving Annotation Quality and Efficiency—0
Semantic Flow-guided Motion Removal Method for Robust Mapping—0
Parallax Motion Effect Generation Through Instance Segmentation And Depth Estimation—0
Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track—0
Conditional Negative Sampling for Contrastive Learning of Visual RepresentationsCode0
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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