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 176–200 of 2262 papers

TitleStatusHype
Token Cropr: Faster ViTs for Quite a Few TasksCode1
LDA-AQU: Adaptive Query-guided Upsampling via Local Deformable AttentionCode0
InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception—0
A Bilayer Segmentation-Recombination Network for Accurate Segmentation of Overlapping C. elegans—0
TinyViM: Frequency Decoupling for Tiny Hybrid Vision MambaCode2
Self-supervised Video Instance Segmentation Can Boost Geographic Entity Alignment in Historical Maps—0
Any3DIS: Class-Agnostic 3D Instance Segmentation by 2D Mask Tracking—0
CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation—0
Learn from Foundation Model: Fruit Detection Model without Manual AnnotationCode1
AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks—0
CompetitorFormer: Competitor Transformer for 3D Instance Segmentation—0
Entropy Bootstrapping for Weakly Supervised Nuclei Detection—0
DIS-Mine: Instance Segmentation for Disaster-Awareness in Poor-Light Condition in Underground Mines—0
Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development—0
RETR: Multi-View Radar Detection Transformer for Indoor PerceptionCode1
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing ImageryCode0
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation—0
Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and Re-Identification using Point CloudsCode0
Data-Centric Learning Framework for Real-Time Detection of Aiming Beam in Fluorescence Lifetime Imaging Guided Surgery—0
Fast and Efficient Transformer-based Method for Bird's Eye View Instance PredictionCode1
SA3DIP: Segment Any 3D Instance with Potential 3D PriorsCode0
Tree level change detection over Ahmedabad city using very high resolution satellite images and Deep Learning—0
MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation—0
Automated Classification of Cell Shapes: A Comparative Evaluation of Shape DescriptorsCode1
MV-Adapter: Enhancing Underwater Instance Segmentation via Adaptive Channel Attention—0
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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