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 21762200 of 2262 papers

TitleStatusHype
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance SegmentationCode0
Deeply Shape-guided Cascade for Instance SegmentationCode0
Where are the Masks: Instance Segmentation with Image-level SupervisionCode0
ShapeFormer: Shape Prior Visible-to-Amodal Transformer-based Amodal Instance SegmentationCode0
Two-Level Temporal Relation Model for Online Video Instance SegmentationCode0
A Feasible Framework for Arbitrary-Shaped Scene Text RecognitionCode0
4D Generic Video Object ProposalsCode0
Zero-Shot Enhancement of Low-Light Image Based on Retinex DecompositionCode0
A Dataset for Analysing Complex Document Layouts in the Digital Humanities and Its Evaluation with Krippendorff’s AlphaCode0
Deep Spectral Improvement for Unsupervised Image Instance SegmentationCode0
Learning to Segment via Cut-and-PasteCode0
Deep Level Set for Box-supervised Instance Segmentation in Aerial ImagesCode0
Learning to Segment Every ThingCode0
Associatively Segmenting Instances and Semantics in Point CloudsCode0
Signature and Log-signature for the Study of Empirical Distributions Generated with GANsCode0
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video DecompositionCode0
Learning to See the Invisible: End-to-End Trainable Amodal Instance SegmentationCode0
Associative Embedding: End-to-End Learning for Joint Detection and GroupingCode0
Learning to Cluster for Proposal-Free Instance SegmentationCode0
Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak LabelsCode0
Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance SegmentationCode0
Uncertainty Calibration and its Application to Object DetectionCode0
Learning Rich Features from RGB-D Images for Object Detection and SegmentationCode0
Assessment of Cell Nuclei AI Foundation Models in Kidney PathologyCode0
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human ParsingCode0
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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