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 1451–1500 of 2262 papers

TitleStatusHype
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image—0
Deep Active Contours Using Locally Controlled Distance Vector Flow—0
Deep Affinity Net: Instance Segmentation via Affinity—0
DeeperLab: Single-Shot Image Parser—0
DeepGamble: Towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection—0
Deep GrabCut for Object Selection—0
Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System—0
Deep Instance Segmentation with Automotive Radar Detection Points—0
Deep learning based automatic detection of offshore oil slicks using SAR data and contextual information—0
Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach—0
Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images—0
Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation—0
Deep Learning Based Video System for Accurate and Real-Time Parking Measurement—0
Deep Learning Techniques for Video Instance Segmentation: A Survey—0
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation—0
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images—0
Deep Polarization Cues for Transparent Object Segmentation—0
Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge—0
Deep Semantic Instance Segmentation of Tree-like Structures Using Synthetic Data—0
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation—0
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing—0
Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework—0
DefMamba: Deformable Visual State Space Model—0
Deformably-Scaled Transposed Convolution—0
Dense Semantic Contrast for Self-Supervised Visual Representation Learning—0
Density-Based Clustering for 3D Object Detection in Point Clouds—0
Deployment and Analysis of Instance Segmentation Algorithm for In-field Grade Estimation of Sweetpotatoes—0
Depth-Guided Semi-Supervised Instance Segmentation—0
Descriptive Modeling of Textiles using FE Simulations and Deep Learning—0
Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction—0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation—0
Detailed Evaluation of Modern Machine Learning Approaches for Optic Plastics Sorting—0
DetectFusion: Detecting and Segmenting Both Known and Unknown Dynamic Objects in Real-time SLAM—0
Detecting Reflections by Combining Semantic and Instance Segmentation—0
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning—0
DetNet: Design Backbone for Object Detection—0
DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation—0
Dilated SpineNet for Semantic Segmentation—0
Dimensions of Motion: Monocular Prediction through Flow Subspaces—0
DIS-Mine: Instance Segmentation for Disaster-Awareness in Poor-Light Condition in Underground Mines—0
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation—0
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN—0
Diversified Dynamic Routing for Vision Tasks—0
DIY Graphics Tab: A Cost-Effective Alternative to Graphics Tablet for Educators—0
DMSANet: Dual Multi Scale Attention Network—0
Domain Adaptation for Unknown Image Distortions in Instance Segmentation—0
Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling—0
DOT: Dynamic Object Tracking for Visual SLAM—0
D-PLS: Decoupled Semantic Segmentation for 4D-Panoptic-LiDAR-Segmentation—0
DPPD: Deformable Polar Polygon Object Detection—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