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 851–875 of 2262 papers

TitleStatusHype
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI—0
1st Place Solution for CVPR2023 BURST Long Tail and Open World Challenges—0
Automated Measurements of Key Morphological Features of Human Embryos for IVF—0
Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey—0
A Histogram Thresholding Improvement to Mask R-CNN for Scalable Segmentation of New and Old Rural Buildings—0
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics—0
Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation—0
CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds—0
AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model—0
A2VIS: Amodal-Aware Approach to Video Instance Segmentation—0
Intelligent Debris Mass Estimation Model for Autonomous Underwater Vehicle—0
Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks—0
A2-FPN: Attention Aggregation Based Feature Pyramid Network for Instance Segmentation—0
Counting and Segmenting Sorghum Heads—0
A Graph Matching Perspective With Transformers on Video Instance Segmentation—0
Instance segmentation with the number of clusters incorporated in embedding learning—0
Instance Segmentation XXL-CT Challenge of a Historic Airplane—0
Could Giant Pretrained Image Models Extract Universal Representations?—0
AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss—0
CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks—0
Aggregation With Feature Detection—0
Instance-Specific Feature Propagation for Referring Segmentation—0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
Convolutional Neural Networks based automated segmentation and labelling of the lumbar spine X-ray—0
AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish—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