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 851–900 of 2262 papers

TitleStatusHype
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model—0
Hyb-KAN ViT: Hybrid Kolmogorov-Arnold Networks Augmented Vision Transformer—0
Self-Supervised Learning for Robotic Leaf Manipulation: A Hybrid Geometric-Neural Approach—0
Segment Any RGB-Thermal Model with Language-aided Distillation—0
A Novel WaveInst-based Network for Tree Trunk Structure Extraction and Pattern Analysis in Forest Inventory—0
Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing—0
MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection—0
NVSMask3D: Hard Visual Prompting with Camera Pose Interpolation for 3D Open Vocabulary Instance Segmentation—0
Occlusion-Ordered Semantic Instance Segmentation—0
CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting—0
Single-shot Star-convex Polygon-based Instance Segmentation for Spatially-correlated Biomedical Objects—0
CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image—0
Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data GenerationCode0
S^4M: Boosting Semi-Supervised Instance Segmentation with SAM—0
BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance SegmentationCode0
APSeg: Auto-Prompt Model with Acquired and Injected Knowledge for Nuclear Instance Segmentation and Classification—0
Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology—0
RipVIS: Rip Currents Video Instance Segmentation Benchmark for Beach Monitoring and Safety—0
Pre-training with 3D Synthetic Data: Learning 3D Point Cloud Instance Segmentation from 3D Synthetic Scenes—0
Foveated Instance SegmentationCode0
Prompting Vision-Language Model for Nuclei Instance Segmentation and ClassificationCode0
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery—0
Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines—0
HiRes-FusedMIM: A High-Resolution RGB-DSM Pre-trained Model for Building-Level Remote Sensing Applications—0
EgoSurgery-HTS: A Dataset for Egocentric Hand-Tool Segmentation in Open Surgery VideosCode0
A Temporal Modeling Framework for Video Pre-Training on Video Instance Segmentation—0
Should we pre-train a decoder in contrastive learning for dense prediction tasks?—0
SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments—0
Leveraging Vision-Language Models for Open-Vocabulary Instance Segmentation and TrackingCode0
Ship Detection in Remote Sensing Imagery for Arbitrarily Oriented Object Detection—0
3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different SensorsCode0
CyclePose -- Leveraging Cycle-Consistency for Annotation-Free Nuclei Segmentation in Fluorescence MicroscopyCode0
COIN: Confidence Score-Guided Distillation for Annotation-Free Cell SegmentationCode0
Aligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives—0
SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model—0
Segment Anything, Even Occluded—0
Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches—0
S4M: Segment Anything with 4 Extreme Points—0
TomatoScanner: phenotyping tomato fruit based on only RGB imageCode0
Automatic Drywall Analysis for Progress Tracking and Quality Control in Construction—0
AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model—0
Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing—0
Label-Efficient LiDAR Panoptic Segmentation—0
Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide ImagesCode0
OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging—0
Training-Free Dataset Pruning for Instance SegmentationCode0
Ranking pre-trained segmentation models for zero-shot transferability—0
You Only Click Once: Single Point Weakly Supervised 3D Instance Segmentation for Autonomous Driving—0
CLIMB-3D: Continual Learning for Imbalanced 3D Instance SegmentationCode0
Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring—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
8GLEE-Promask AP54.2—Unverified
9ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2—Unverified
10SwinV2-G (HTC++)mask AP53.7—Unverified