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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1875 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
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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