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 16511675 of 2262 papers

TitleStatusHype
Salient Instance Segmentation via Subitizing and Clustering0
SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences0
SAM2Auto: Auto Annotation Using FLASH0
SAM2Object: Consolidating View Consistency via SAM2 for Zero-Shot 3D Instance Segmentation0
SAM-based instance segmentation models for the automation of structural damage detection0
SAM-guided Graph Cut for 3D Instance Segmentation0
SAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation0
Sampling-based Uncertainty Estimation for an Instance Segmentation Network0
SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model0
SASO: Joint 3D Semantic-Instance Segmentation via Multi-scale Semantic Association and Salient Point Clustering Optimization0
Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping0
Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers0
Scale-aware multi-level guidance for interactive instance segmentation0
Scale Disparity of Instances in Interactive Point Cloud Segmentation0
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator0
Scaling Wide Residual Networks for Panoptic Segmentation0
Scattering Vision Transformer: Spectral Mixing Matters0
SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes0
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-Training on Indoor Segmentation?0
Layout Agnostic Scene Text Image Synthesis with Diffusion Models0
SCR: Smooth Contour Regression with Geometric Priors0
SDI-Paste: Synthetic Dynamic Instance Copy-Paste for Video Instance Segmentation0
SDOD:Real-time Segmenting and Detecting 3D Object by Depth0
SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment0
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency0
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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