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 5175 of 2262 papers

TitleStatusHype
Segment Anything for HistopathologyCode2
iFormer: Integrating ConvNet and Transformer for Mobile ApplicationCode2
RelationField: Relate Anything in Radiance FieldsCode2
MaskTerial: A Foundation Model for Automated 2D Material Flake DetectionCode2
DreamColour: Controllable Video Colour Editing without TrainingCode2
TinyViM: Frequency Decoupling for Tiny Hybrid Vision MambaCode2
DI-MaskDINO: A Joint Object Detection and Instance Segmentation ModelCode2
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary SegmentationCode2
One missing piece in Vision and Language: A Survey on Comics UnderstandingCode2
Image Segmentation in Foundation Model Era: A SurveyCode2
CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile ApplicationsCode2
PartGLEE: A Foundation Model for Recognizing and Parsing Any ObjectsCode2
GroupMamba: Efficient Group-Based Visual State Space ModelCode2
Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded ScenesCode2
Adaptive Parametric ActivationCode2
Training-free CryoET Tomogram SegmentationCode2
Context-Aware Video Instance SegmentationCode2
Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale DatasetCode2
Generative Active Learning for Long-tailed Instance SegmentationCode2
Adapting Pre-Trained Vision Models for Novel Instance Detection and SegmentationCode2
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative DataCode2
GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNsCode2
PTQ4SAM: Post-Training Quantization for Segment AnythingCode2
ViM-UNet: Vision Mamba for Biomedical SegmentationCode2
ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt TuningCode2
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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