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 14261450 of 2262 papers

TitleStatusHype
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale AttentionCode1
Improving Video Instance Segmentation via Temporal Pyramid RoutingCode1
Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)0
Continental-Scale Building Detection from High Resolution Satellite Imagery0
Contextual Transformer Networks for Visual RecognitionCode1
Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images0
Rank & Sort Loss for Object Detection and Instance SegmentationCode1
CycleMLP: A MLP-like Architecture for Dense PredictionCode1
Improving Mask R-CNN for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological ImagesCode1
Instance Segmentation of Multiple Myeloma Cells via Hybrid Task Cascade0
Exploring Set Similarity for Dense Self-supervised Representation Learning0
Dynamic Convolution for 3D Point Cloud Instance SegmentationCode1
Unsupervised Anomaly Instance Segmentation for Baggage Threat RecognitionCode0
Amodal segmentation just like doing a jigsaw0
MeNToS: Tracklets Association with a Space-Time Memory Network0
Deep Learning based Food Instance Segmentation using Synthetic DataCode1
Visual Parser: Representing Part-whole Hierarchies with TransformersCode1
NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter ScaleCode1
Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context TermsCode1
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical RegionsCode0
SE-PSNet: Silhouette-based Enhancement Feature for Panoptic Segmentation Network0
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation0
Capturing, Reconstructing, and Simulating: the UrbanScene3D DatasetCode1
HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor0
Learning Stixel-based 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