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 1101–1150 of 2262 papers

TitleStatusHype
A Self-Supervised Method for Body Part Segmentation and Keypoint Detection of Rat Images—0
Towards general deep-learning-based tree instance segmentation models—0
From Density to Geometry: YOLOv8 Instance Segmentation for Reverse Engineering of Optimized StructuresCode0
Self-Balanced R-CNN for Instance SegmentationCode0
PM-VIS: High-Performance Box-Supervised Video Instance Segmentation—0
FisheyeDetNet: 360° Surround view Fisheye Camera based Object Detection System for Autonomous Driving—0
Nuclei Instance Segmentation of Cryosectioned H&E Stained Histological Images using Triple U-Net Architecture—0
Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery—0
Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds—0
Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose EncodingCode0
Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation—0
UruDendro, a public dataset of cross-section images of Pinus taedaCode0
NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mouse-to-Human Domain TransferCode0
AdaContour: Adaptive Contour Descriptor with Hierarchical RepresentationCode0
Structured Model Pruning for Efficient Inference in Computational Pathology—0
Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy—0
Automated National Urban Map Extraction—0
Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation—0
OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning—0
Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation—0
CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks—0
Segment Any 3D Object with Language—0
What is Point Supervision Worth in Video Instance Segmentation?—0
Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge—0
Instance-Aware Group Quantization for Vision Transformers—0
SUGAR: Pre-training 3D Visual Representations for Robotics—0
Efficient 3D Instance Mapping and Localization with Neural Fields—0
Annolid: Annotate, Segment, and Track Anything You NeedCode0
GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation—0
ParFormer: A Vision Transformer with Parallel Mixer and Sparse Channel Attention Patch Embedding—0
Language-Based Depth Hints for Monocular Depth Estimation—0
Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting—0
Circle Representation for Medical Instance Object SegmentationCode0
EffiPerception: an Efficient Framework for Various Perception Tasks—0
MISS: Memory-efficient Instance Segmentation Framework By Visual Inductive Priors Flow Propagation—0
ShapeFormer: Shape Prior Visible-to-Amodal Transformer-based Amodal Instance SegmentationCode0
Better (pseudo-)labels for semi-supervised instance segmentation—0
Augment Before Copy-Paste: Data and Memory Efficiency-Oriented Instance Segmentation Framework for Sport-scenes—0
Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation—0
Grasp Anything: Combining Teacher-Augmented Policy Gradient Learning with Instance Segmentation to Grasp Arbitrary Objects—0
Task-Specific Adaptation of Segmentation Foundation Model via Prompt Learning—0
WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity—0
Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration—0
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt DenoisingCode0
CenterDisks: Real-time instance segmentation with disk coveringCode0
MCA: Moment Channel Attention NetworksCode0
RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant Features—0
Self-Supervised Representation Learning with Meta Comprehensive Regularization—0
Boosting Box-supervised Instance Segmentation with Pseudo Depth—0
Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation—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