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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1676–1700 of 2262 papers

TitleStatusHype
Search3D: Hierarchical Open-Vocabulary 3D Segmentation—0
SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification—0
SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification—0
Neural Groundplans: Persistent Neural Scene Representations from a Single Image—0
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation—0
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis—0
Segment Any 3D Object with Language—0
Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation—0
Segment Any RGB-Thermal Model with Language-aided Distillation—0
Segment Anything, Even Occluded—0
Segment Anything for Microscopy—0
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping—0
SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data—0
Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration—0
Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels—0
Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis—0
Segment-Fusion: Hierarchical Context Fusion for Robust 3D Semantic Segmentation—0
Segmenting objects with Bayesian fusion of active contour models and convnet priors—0
Self-Prediction for Joint Instance and Semantic Segmentation of Point Clouds—0
Self-Supervised Image Representation Learning with Geometric Set Consistency—0
Self-Supervised Instance Segmentation by Grasping—0
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach—0
Self-supervised learning for autonomous vehicles perception: A conciliation between analytical and learning methods—0
Self-Supervised Learning for Robotic Leaf Manipulation: A Hybrid Geometric-Neural Approach—0
Self-supervised Object Motion and Depth Estimation from Video—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
8ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2—Unverified
9GLEE-Promask AP54.2—Unverified
10SwinV2-G (HTC++)mask AP53.7—Unverified