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

Showing 20012025 of 2262 papers

TitleStatusHype
U4D: Unsupervised 4D Dynamic Scene Understanding0
DetectFusion: Detecting and Segmenting Both Known and Unknown Dynamic Objects in Real-time SLAM0
Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding BoxesCode0
Human Extraction and Scene Transition utilizing Mask R-CNN0
Slot Based Image Augmentation System for Object Detection0
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is ComingCode0
MaskPlus: Improving Mask Generation for Instance Segmentation0
A 3D Convolutional Approach to Spectral Object Segmentation in Space and TimeCode0
Where are the Masks: Instance Segmentation with Image-level SupervisionCode0
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation0
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering BandwidthCode1
Cascade R-CNN: High Quality Object Detection and Instance SegmentationCode0
Automated crater shape retrieval using weakly-supervised deep learningCode0
3D Instance Segmentation via Multi-Task Metric Learning0
Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image0
MMDetection: Open MMLab Detection Toolbox and BenchmarkCode1
IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things0
Instance Segmentation with Point Supervision0
Learning Instance Occlusion for Panoptic SegmentationCode0
Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask TransferCode1
Learning Object Bounding Boxes for 3D Instance Segmentation on Point CloudsCode1
Data Augmentation for Object Detection via Progressive and Selective Instance-SwitchingCode0
Content-Aware Multi-Level Guidance for Interactive Instance Segmentation0
Amodal Instance Segmentation With KINS DatasetCode0
Learning Instance Activation Maps for Weakly Supervised Instance Segmentation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-HAP5080.8Unverified
2ResNeSt-200 (multi-scale)AP5070.2Unverified
3CenterMask + VoVNetV2-99 (multi-scale)AP5066.2Unverified
4CenterMask + VoVNetV2-57 (single-scale)AP5060.8Unverified
5Co-DETRmask AP57.1Unverified
6CBNetV2 (EVA02, single-scale)mask AP56.1Unverified
7ISDA (ResNet-50)APL55.7Unverified
8EVAmask AP55.5Unverified
9FD-SwinV2-Gmask AP55.4Unverified
10Mask Frozen-DETRmask AP55.3Unverified
#ModelMetricClaimedVerifiedStatus
1InternImage-BGFLOPs501Unverified
2Co-DETRmask AP56.6Unverified
3ViT-CoMer-L (Mask RCNN, DINOv2)mask AP55.9Unverified
4InternImage-Hmask AP55.4Unverified
5EVAmask AP55Unverified
6Mask Frozen-DETRmask AP54.9Unverified
7MasK DINO (SwinL, multi-scale)mask AP54.5Unverified
8ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2Unverified
9GLEE-Promask AP54.2Unverified
10SwinV2-G (HTC++)mask AP53.7Unverified