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 1351–1400 of 2262 papers

TitleStatusHype
Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter—0
Joint prototype and coefficient prediction for 3D instance segmentation—0
Joint Semantic Instance Segmentation on Graphs with the Semantic Mutex Watershed—0
JSMNet Improving Indoor Point Cloud Semantic and Instance Segmentation through Self-Attention and Multiscale—0
KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image—0
K-means for unsupervised instance segmentation using a self-supervised transformer—0
Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation—0
Label-Efficient LiDAR Panoptic Segmentation—0
Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation—0
LACOSTE: Exploiting stereo and temporal contexts for surgical instrument segmentation—0
Language-Based Depth Hints for Monocular Depth Estimation—0
Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation—0
Large-scale interactive object segmentation with human annotators—0
Layout Aware Inpainting for Automated Furniture Removal in Indoor Scenes—0
LDD: A Dataset for Grape Diseases Object Detection and Instance Segmentation—0
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment—0
Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation—0
Learning a unified label space—0
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation—0
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation—0
M3Act: Learning from Synthetic Human Group Activities—0
Learning Instance Activation Maps for Weakly Supervised Instance Segmentation—0
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation—0
Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images—0
Learning Multi-Object Tracking and Segmentation from Automatic Annotations—0
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation—0
Learning Degradation-Independent Representations for Camera ISP Pipelines—0
Learning Nuclei Representations with Masked Image Modelling—0
Learning Object Placement by Inpainting for Compositional Data Augmentation—0
Learning Segmentation Masks with the Independence Prior—0
Learning Shape-Aware Embedding for Scene Text Detection—0
Learning Stixel-based Instance Segmentation—0
Learning to Better Segment Objects from Unseen Classes with Unlabeled Videos—0
Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes—0
Learning to decompose for object detection and instance segmentation—0
Learning to Detect Every Thing in an Open World—0
Learning to Fuse Things and Stuff—0
Learning to Infer Kinematic Hierarchies for Novel Object Instances—0
Learning to Optimally Segment Point Clouds—0
Learning to Track Any Object—0
Learning to Track Instances without Video Annotations—0
Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation—0
Learning Video Instance Segmentation with Recurrent Graph Neural Networks—0
Learning with Free Object Segments for Long-Tailed Instance Segmentation—0
Less than Few: Self-Shot Video Instance Segmentation—0
LevelSet R-CNN: A Deep Variational Method for Instance Segmentation—0
LevelSet R-CNN: A Deep Variational Method for Instance Segmentation—0
Leverage Cross-Attention for End-to-End Open-Vocabulary Panoptic Reconstruction—0
Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring—0
Leveraging multiple datasets for deep leaf counting—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