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 1351–1400 of 2262 papers

TitleStatusHype
UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite—0
Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation—0
Self-Supervised Learning from Non-Object Centric Images with a Geometric Transformation Sensitive ArchitectureCode0
Instance-level Few-shot Learning with Class Hierarchy MiningCode0
SpectFormer: Frequency and Attention is what you need in a Vision Transformer—0
DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformer—0
HaDR: Applying Domain Randomization for Generating Synthetic Multimodal Dataset for Hand Instance Segmentation in Cluttered Industrial EnvironmentsCode0
DPPD: Deformable Polar Polygon Object Detection—0
LIGHT: Joint Individual Building Extraction and Height Estimation from Satellite Images through a Unified Multitask Learning Network—0
Video Instance Segmentation in an Open-WorldCode0
INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields—0
MobileInst: Video Instance Segmentation on the Mobile—0
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation—0
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning—0
ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation—0
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations—0
AutoKary2022: A Large-Scale Densely Annotated Dataset for Chromosome Instance SegmentationCode0
OpenInst: A Simple Query-Based Method for Open-World Instance Segmentation—0
SUDS: Scalable Urban Dynamic Scenes—0
UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes—0
A Simple and Generic Framework for Feature Distillation via Channel-wise Transformation—0
Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains—0
On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics—0
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images—0
Weakly-Supervised Text Instance Segmentation—0
Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic GraspingCode0
Panoptic One-Click Segmentation: Applied to Agricultural Data—0
OSIS: Efficient One-stage Network for 3D Instance Segmentation—0
Towards Universal Vision-language Omni-supervised Segmentation—0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
Dynamic Y-KD: A Hybrid Approach to Continual Instance Segmentation—0
Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision—0
MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance SegmentationCode0
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods—0
MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors—0
X^3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection—0
Bayesian Deep Learning for Affordance Segmentation in images—0
DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditionsCode0
BiSVP: Building Footprint Extraction via Bidirectional Serialized Vertex Prediction—0
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors—0
FPCD: An Open Aerial VHR Dataset for Farm Pond Change Detection—0
Category-level Shape Estimation for Densely Cluttered Objects—0
ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation—0
SEMI-PointRend: Improved Semiconductor Wafer Defect Classification and Segmentation as Rendering—0
Offline-to-Online Knowledge Distillation for Video Instance Segmentation—0
PUPS: Point Cloud Unified Panoptic Segmentation—0
An Efficient Instance Segmentation Approach for Extracting Fission Gas Bubbles on U-10Zr Annular Fuel—0
Using t-distributed stochastic neighbor embedding for visualization and segmentation of 3D point clouds of plants—0
PatchDCT: Patch Refinement for High Quality Instance SegmentationCode0
Top-Down Beats Bottom-Up in 3D Instance SegmentationCode0
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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