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 1851–1900 of 2262 papers

TitleStatusHype
UNIT: Unsupervised Online Instance Segmentation through Time—0
UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes—0
Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation—0
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision—0
Unsupervised Pre-Training for 3D Leaf Instance Segmentation—0
Unsupervised Spiking Instance Segmentation on Event Data using STDP—0
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation—0
CamoFA: A Learnable Fourier-based Augmentation for Camouflage Segmentation—0
UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation—0
Using depth information and colour space variations for improving outdoor robustness for instance segmentation of cabbage—0
Using t-distributed stochastic neighbor embedding for visualization and segmentation of 3D point clouds of plants—0
US-net for robust and efficient nuclei instance segmentation—0
UVIS: Unsupervised Video Instance Segmentation—0
Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network—0
Vehicle Occurrence-based Parking Space Detection—0
VertDetect: Fully End-to-End 3D Vertebral Instance Segmentation Model—0
VFX Creator: Animated Visual Effect Generation with Controllable Diffusion Transformer—0
Video Instance Segmentation by Instance Flow Assembly—0
Video Instance Segmentation Tracking With a Modified VAE Architecture—0
Video Prediction Models as General Visual Encoders—0
Virtual KITTI 2—0
Virtual Worlds as Proxy for Multi-Object Tracking Analysis—0
Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images—0
Vision Foundation Model Embedding-Based Semantic Anomaly Detection—0
Vision Transformers Are Good Mask Auto-Labelers—0
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information—0
VizWiz-FewShot: Locating Objects in Images Taken by People With Visual Impairments—0
Vocabulary-Free 3D Instance Segmentation with Vision and Language Assistant—0
Volume and leaf area calculation of cabbage with a neural network-based instance segmentation—0
VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding—0
VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning—0
Weakly Supervised 3D Instance Segmentation without Instance-level Annotations—0
Weakly Supervised Airway Orifice Segmentation in Video Bronchoscopy—0
Weakly Supervised Instance Segmentation by Deep Community Learning—0
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances—0
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency—0
Weakly Supervised Instance Segmentation Using Hybrid Network—0
Weakly Supervised Instance Segmentation using Motion Information via Optical Flow—0
Salient Instance Segmentation with Region and Box-level Annotations—0
Weakly Supervised Multi-Object Tracking and Segmentation—0
Weakly-Supervised Text Instance Segmentation—0
WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity—0
What is Point Supervision Worth in Video Instance Segmentation?—0
What Makes for Good Views for Contrastive Learning?—0
What Makes for Hierarchical Vision Transformer?—0
What's in my Room? Object Recognition on Indoor Panoramic Images—0
When 3D Bounding-Box Meets SAM: Point Cloud Instance Segmentation with Weak-and-Noisy Supervision—0
WildDash - Creating Hazard-Aware Benchmarks—0
WISH: Weakly Supervised Instance Segmentation using Heterogeneous Labels—0
X^3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D 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