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

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Showing 1751–1775 of 2262 papers

TitleStatusHype
Lebanon Solar Rooftop Potential Assessment using Buildings Segmentation from Aerial Images—0
SOLO: A Simple Framework for Instance Segmentation—0
Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration—0
SOS: Segment Object System for Open-World Instance Segmentation With Object Priors—0
SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning—0
Spatial Attention Pyramid Network for Unsupervised Domain Adaptation—0
Spatial Sampling Network for Fast Scene Understanding—0
Spatio-temporal Human Action Localisation and Instance Segmentation in Temporally Untrimmed Videos—0
SpectFormer: Frequency and Attention is what you need in a Vision Transformer—0
Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds—0
SPPSFormer: High-quality Superpoint-based Transformer for Roof Plane Instance Segmentation from Point Clouds—0
StandardSim: A Synthetic Dataset For Retail Environments—0
Stateless actor-critic for instance segmentation with high-level priors—0
Static Segmentation by Tracking: A Frustratingly Label-Efficient Approach to Fine-Grained Segmentation—0
STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation—0
STEAM: Squeeze and Transform Enhanced Attention Module—0
Streaming Object Detection on Fisheye Cameras for Automatic Parking—0
Structured Model Pruning for Efficient Inference in Computational Pathology—0
Structure-Preserving Instance Segmentation via Skeleton-Aware Distance Transform—0
SUDS: Scalable Urban Dynamic Scenes—0
SUGAR: Pre-training 3D Visual Representations for Robotics—0
SUNet: Scale-aware Unified Network for Panoptic Segmentation—0
supervised adptive threshold network for instance segmentation—0
SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark—0
Symmetric masking strategy enhances the performance of Masked Image Modeling—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