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 1826–1850 of 2262 papers

TitleStatusHype
Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm—0
Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod—0
TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris—0
Tree Instance Segmentation With Temporal Contour Graph—0
Tree level change detection over Ahmedabad city using very high resolution satellite images and Deep Learning—0
TreeNet: A lightweight One-Shot Aggregation Convolutional Network—0
Tuning a SAM-Based Model with Multi-Cognitive Visual Adapter to Remote Sensing Instance Segmentation—0
Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations—0
Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning—0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
U4D: Unsupervised 4D Dynamic Scene Understanding—0
UCP-Net: Unstructured Contour Points for Instance Segmentation—0
UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite—0
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation—0
Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains—0
Uncertainty Estimation in Instance Segmentation with Star-convex Shapes—0
Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation—0
Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning—0
UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentation—0
U-Net Based Multi-instance Video Object Segmentation—0
Uni3DL: Unified Model for 3D and Language Understanding—0
Uni6Dv2: Noise Elimination for 6D Pose Estimation—0
Unified Batch Normalization: Identifying and Alleviating the Feature Condensation in Batch Normalization and a Unified Framework—0
Unifying 3D Vision-Language Understanding via Promptable Queries—0
Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions—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