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 2101–2150 of 2262 papers

TitleStatusHype
3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation—0
DeeperLab: Single-Shot Image Parser—0
MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance SegmentationCode0
Towards Segmenting Anything That MovesCode0
Single Network Panoptic Segmentation for Street Scene UnderstandingCode0
Instance Segmentation as Image Segmentation AnnotationCode0
US-net for robust and efficient nuclei instance segmentation—0
Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images—0
Real-world Mapping of Gaze Fixations Using Instance Segmentation for Road Construction Safety Applications—0
4D Generic Video Object ProposalsCode0
Primitive-based 3D Building Modeling, Sensor Simulation, and Estimation—0
UPSNet: A Unified Panoptic Segmentation NetworkCode0
Instance Segmentation of Fibers from Low Resolution CT Scans via 3D Deep Embedding Learning—0
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation—0
3D-SIS: 3D Semantic Instance Segmentation of RGB-D ScansCode0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation—0
Weakly Supervised Instance Segmentation Using Hybrid Network—0
Scale-aware multi-level guidance for interactive instance segmentation—0
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object UnderstandingCode0
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics—0
Learning to Fuse Things and Stuff—0
Beyond Grids: Learning Graph Representations for Visual Recognition—0
TextMountain: Accurate Scene Text Detection via Instance Segmentation—0
CCNet: Criss-Cross Attention for Semantic SegmentationCode0
Affinity Derivation and Graph Merge for Instance SegmentationCode0
GANtruth - an unpaired image-to-image translation method for driving scenarios—0
Non-local RoI for Cross-Object Perception—0
A Novel Technique for Evidence based Conditional Inference in Deep Neural Networks via Latent Feature Perturbation—0
A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization—0
Scene Text Detection with Supervised Pyramid Context NetworkCode0
Rethinking ImageNet Pre-training—0
Slum Segmentation and Change Detection : A Deep Learning ApproachCode0
Domain Randomization for Scene-Specific Car Detection and Pose EstimationCode0
Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNNCode0
Learning Segmentation Masks with the Independence Prior—0
Deep Semantic Instance Segmentation of Tree-like Structures Using Synthetic Data—0
Rain Removal in Traffic Surveillance: Does it Matter?Code0
Mask Propagation Network for Video Object Segmentation—0
Investigating Object Compositionality in Generative Adversarial Networks—0
CNN-based Preprocessing to Optimize Watershed-based Cell Segmentation in 3D Confocal Microscopy Images—0
BshapeNet: Object Detection and Instance Segmentation with Bounding Shape Masks—0
Artistic Instance-Aware Image Filtering by Convolutional Neural Networks—0
Faster Training of Mask R-CNN by Focusing on Instance BoundariesCode0
Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic DataCode0
Robust Adversarial Perturbation on Deep Proposal-based Models—0
Geometric Image Synthesis—0
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information—0
On the Importance of Visual Context for Data Augmentation in Scene Understanding—0
Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network—0
WildDash - Creating Hazard-Aware Benchmarks—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