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 1901–1950 of 2262 papers

TitleStatusHype
Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set—0
Joint 3D Instance Segmentation and Object Detection for Autonomous Driving—0
End-to-End 3D Point Cloud Instance Segmentation Without Detection—0
TESA: Tensor Element Self-Attention via Matricization—0
Deep Polarization Cues for Transparent Object Segmentation—0
Automated Measurements of Key Morphological Features of Human Embryos for IVF—0
On Mutual Information in Contrastive Learning for Visual Representations—0
An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnosticsCode0
Panoptic Instance Segmentation on Pigs—0
What Makes for Good Views for Contrastive Learning?—0
Self-supervised Transfer Learning for Instance Segmentation through Physical InteractionCode0
Reinforced Coloring for End-to-End Instance Segmentation—0
Multi-Task Learning in Histo-pathology for Widely Generalizable Model—0
Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks—0
A novel Region of Interest Extraction Layer for Instance SegmentationCode0
All you need is a second look: Towards Tighter Arbitrary shape text detection—0
MOPT: Multi-Object Panoptic Tracking—0
Bidirectional Graph Reasoning Network for Panoptic Segmentation—0
A2D2: Audi Autonomous Driving Dataset—0
CenterMask: single shot instance segmentation with point representation—0
Convolutional Neural Networks based automated segmentation and labelling of the lumbar spine X-ray—0
Pixel Consensus Voting for Panoptic Segmentation—0
FGN: Fully Guided Network for Few-Shot Instance Segmentation—0
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic SegmentationCode0
EOLO: Embedded Object Segmentation only Look OnceCode0
Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting—0
Spatial Attention Pyramid Network for Unsupervised Domain Adaptation—0
Mask Encoding for Single Shot Instance Segmentation—0
Robust Medical Instrument Segmentation Challenge 2019—0
Neural Mesh Refiner for 6-DoF Pose Estimation—0
Deep Affinity Net: Instance Segmentation via Affinity—0
OccuSeg: Occupancy-aware 3D Instance Segmentation—0
PointINS: Point-based Instance Segmentation—0
A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color—0
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI—0
FLIC: Fast Lidar Image Clustering—0
3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection—0
Towards Bounding-Box Free Panoptic Segmentation—0
An End-to-End Framework for Unsupervised Pose Estimation of Occluded Pedestrians—0
Layered Embeddings for Amodal Instance SegmentationCode0
Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation—0
Weakly Supervised Instance Segmentation by Deep Community Learning—0
Virtual KITTI 2—0
Accurately identifying vertebral levels in large datasets—0
SDOD:Real-time Segmenting and Detecting 3D Object by Depth—0
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects—0
Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter—0
GraphBGS: Background Subtraction via Recovery of Graph Signals—0
Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey—0
SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark—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
8GLEE-Promask AP54.2—Unverified
9ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2—Unverified
10SwinV2-G (HTC++)mask AP53.7—Unverified