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 1501–1550 of 2262 papers

TitleStatusHype
Fast and Precise Binary Instance Segmentation of 2D Objects for Automotive Applications—0
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed LearningCode0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
Single-Stage Open-world Instance Segmentation with Cross-task Consistency RegularizationCode0
Look in Different Views: Multi-Scheme Regression Guided Cell Instance Segmentation—0
Uni6Dv2: Noise Elimination for 6D Pose Estimation—0
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator—0
Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point SupervisionCode0
Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes—0
Image-based Detection of Surface Defects in Concrete during ConstructionCode0
OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images—0
Training a universal instance segmentation network for live cell images of various cell types and imaging modalitiesCode0
Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition—0
Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation—0
VizWiz-FewShot: Locating Objects in Images Taken by People With Visual Impairments—0
Neural Groundplans: Persistent Neural Scene Representations from a Single Image—0
NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds—0
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach—0
KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image—0
3D Concept Grounding on Neural Fields—0
YOLO2U-Net: Detection-Guided 3D Instance Segmentation for Microscopy—0
Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos—0
Online Video Instance Segmentation via Robust Context Fusion—0
A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation—0
An Embedding-Dynamic Approach to Self-supervised Learning—0
Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling—0
A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction—0
Beyond mAP: Towards better evaluation of instance segmentation—0
BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR—0
InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance SegmentationCode0
UniDAformer: Unified Domain Adaptive Panoptic Segmentation Transformer via Hierarchical Mask Calibration—0
PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage—0
Parallel Pre-trained Transformers (PPT) for Synthetic Data-based Instance Segmentation—0
Broken News: Making Newspapers Accessible to Print-Impaired—0
LDD: A Dataset for Grape Diseases Object Detection and Instance Segmentation—0
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images—0
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation—0
A Unified Sequence Interface for Vision Tasks—0
Consistent Video Instance Segmentation with Inter-Frame Recurrent Attention—0
Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey—0
A Semantic Consistency Feature Alignment Object Detection Model Based on Mixed-Class Distribution Metrics—0
BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks—0
Occlusion-Resistant Instance Segmentation of Piglets in Farrowing Pens Using Center Clustering Network—0
Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes—0
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation—0
Robustness Evaluation and Adversarial Training of an Instance Segmentation Model—0
2D Amodal Instance Segmentation Guided by 3D Shape Prior—0
Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning—0
iFS-RCNN: An Incremental Few-shot Instance SegmenterCode0
UNet#: A UNet-like Redesigning Skip Connections for Medical Image 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