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 1401–1425 of 2262 papers

TitleStatusHype
Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification—0
Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation—0
Comparing YOLOv11 and YOLOv8 for instance segmentation of occluded and non-occluded immature green fruits in complex orchard environment—0
Comparing YOLOv8 and Mask RCNN for object segmentation in complex orchard environments—0
Comparison of UNet, ENet, and BoxENet for Segmentation of Mast Cells in Scans of Histological Slices—0
Competitive Learning for Achieving Content-specific Filters in Video Coding for Machines—0
CompetitorFormer: Competitor Transformer for 3D Instance Segmentation—0
Computation Reallocation for Object Detection—0
Computer Vision Pipeline for Automated Antarctic Krill Analysis—0
Concatenated Feature Pyramid Network for Instance Segmentation—0
Concavity-Induced Distance for Unoriented Point Cloud Decomposition—0
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models—0
Confidence Calibration for Object Detection and Segmentation—0
ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions—0
Instance Scale Normalization for image understanding—0
Consistent Video Instance Segmentation with Inter-Frame Recurrent Attention—0
Content-Aware Multi-Level Guidance for Interactive Instance Segmentation—0
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection—0
Contextual Graph Reasoning Networks—0
Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation—0
Continental-Scale Building Detection from High Resolution Satellite Imagery—0
Contour Loss for Instance Segmentation via k-step Distance Transformation Image—0
ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass—0
Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation—0
Convolutional Neural Networks based automated segmentation and labelling of the lumbar spine X-ray—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