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 1551–1600 of 2262 papers

TitleStatusHype
Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds—0
Parallax Motion Effect Generation Through Instance Segmentation And Depth Estimation—0
Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation—0
Parallel Pre-trained Transformers (PPT) for Synthetic Data-based Instance Segmentation—0
ParFormer: A Vision Transformer with Parallel Mixer and Sparse Channel Attention Patch Embedding—0
Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping—0
Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning—0
Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation—0
Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery—0
Performance Evaluation of YOLOv8 Model Configurations, for Instance Segmentation of Strawberry Fruit Development Stages in an Open Field Environment—0
Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing—0
PerMO: Perceiving More at Once from a Single Image for Autonomous Driving—0
Pixel Consensus Voting for Panoptic Segmentation—0
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling—0
PlaneSAM: Multimodal Plane Instance Segmentation Using the Segment Anything Model—0
PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN—0
Playing for Benchmarks—0
PLUTO: Pathology-Universal Transformer—0
PMODE: Prototypical Mask based Object Dimension Estimation—0
PM-VIS: High-Performance Box-Supervised Video Instance Segmentation—0
Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation—0
Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest—0
Point Cloud Instance Segmentation using Probabilistic Embeddings—0
PointINS: Point-based Instance Segmentation—0
PointInst3D: Segmenting 3D Instances by Points—0
PointIT: A Fast Tracking Framework Based on 3D Instance Segmentation—0
PolarNeXt: Rethink Instance Segmentation with Polar Representation—0
Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding—0
Poly-NL: Linear Complexity Non-local Layers with Polynomials—0
Poly-NL: Linear Complexity Non-Local Layers With 3rd Order Polynomials—0
Pose2Instance: Harnessing Keypoints for Person Instance Segmentation—0
Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation—0
PoSeg: Pose-Aware Refinement Network for Human Instance Segmentation—0
Pre-training with 3D Synthetic Data: Learning 3D Point Cloud Instance Segmentation from 3D Synthetic Scenes—0
Primitive-based 3D Building Modeling, Sensor Simulation, and Estimation—0
Probabilistic Deep Learning for Instance Segmentation—0
Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers—0
ProMerge: Prompt and Merge for Unsupervised Instance Segmentation—0
CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding—0
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks—0
ProtoSeg: A Prototype-Based Point Cloud Instance Segmentation Method—0
Pseudo Mask Augmented Object Detection—0
PSGformer: Enhancing 3D Point Cloud Instance Segmentation via Precise Semantic Guidance—0
PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage—0
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning—0
PUPS: Point Cloud Unified Panoptic Segmentation—0
Putting 3D Spatially Sparse Networks on a Diet—0
Quantification of cardiac capillarization in single-immunostained myocardial slices using weakly supervised instance segmentation—0
Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks—0
Query Refinement Transformer for 3D Instance Segmentation—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