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 101150 of 2262 papers

TitleStatusHype
BlenderProcCode2
PTQ4SAM: Post-Training Quantization for Segment AnythingCode2
Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian SplattingCode2
Rethinking Patch Dependence for Masked AutoencodersCode2
Generative Active Learning for Long-tailed Instance SegmentationCode2
RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation ModelCode2
SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything ModelCode2
Scene-Centric Unsupervised Panoptic SegmentationCode2
Global Context Vision TransformersCode2
Aerial Lifting: Neural Urban Semantic and Building Instance Lifting from Aerial ImageryCode2
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and BeyondCode2
FreeSOLO: Learning to Segment Objects without AnnotationsCode2
Scalable SoftGroup for 3D Instance Segmentation on Point CloudsCode2
SOLOv2: Dynamic and Fast Instance SegmentationCode2
Context-Aware Video Instance SegmentationCode2
Swin Transformer: Hierarchical Vision Transformer using Shifted WindowsCode2
The Missing Point in Vision Transformers for Universal Image SegmentationCode2
Context Autoencoder for Self-Supervised Representation LearningCode2
FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anythingCode2
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary SegmentationCode2
Exploring Plain Vision Transformer Backbones for Object DetectionCode2
Unsupervised Universal Image SegmentationCode2
FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation ModelsCode2
GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNsCode2
ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt TuningCode2
DreamColour: Controllable Video Colour Editing without TrainingCode2
MogaNet: Multi-order Gated Aggregation NetworkCode2
DI-MaskDINO: A Joint Object Detection and Instance Segmentation ModelCode2
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous ConvolutionCode2
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative DataCode2
A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask InpaintingCode2
Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite ImageryCode2
DaViT: Dual Attention Vision TransformersCode2
Deep Snake for Real-Time Instance SegmentationCode2
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTsCode2
DAMamba: Vision State Space Model with Dynamic Adaptive ScanCode2
DiffusionInst: Diffusion Model for Instance SegmentationCode2
Dilated Neighborhood Attention TransformerCode2
Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale DatasetCode2
Does Image Anonymization Impact Computer Vision Training?Code2
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance SegmentationCode2
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural NetworksCode2
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded ScenesCode2
FastInst: A Simple Query-Based Model for Real-Time Instance SegmentationCode2
FEC: Fast Euclidean Clustering for Point Cloud SegmentationCode2
DAT++: Spatially Dynamic Vision Transformer with Deformable AttentionCode2
Fully Convolutional Instance-aware Semantic SegmentationCode2
Beyond Self-attention: External Attention using Two Linear Layers for Visual TasksCode2
GroupMamba: Efficient Group-Based Visual State Space ModelCode2
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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