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 1351–1400 of 2262 papers

TitleStatusHype
Towards Complementary Knowledge Distillation for Efficient Dense Image Prediction—0
Boundary-aware Instance Segmentation—0
Bounding Box Embedding for Single Shot Person Instance Segmentation—0
Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?—0
Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset—0
BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR—0
BoxInst: High-Performance Instance Segmentation with Box Annotations—0
Brain Cancer Segmentation Using YOLOv5 Deep Neural Network—0
Bridging Category-level and Instance-level Semantic Image Segmentation—0
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery—0
Broken News: Making Newspapers Accessible to Print-Impaired—0
BROW: Better featuRes fOr Whole slide image based on self-distillation—0
BshapeNet: Object Detection and Instance Segmentation with Bounding Shape Masks—0
Budget-aware Semi-Supervised Semantic and Instance Segmentation—0
Building Height Prediction with Instance Segmentation—0
Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework—0
CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting—0
Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite—0
Camouflaged Instance Segmentation via Explicit De-Camouflaging—0
Can we cover navigational perception needs of the visually impaired by panoptic segmentation?—0
CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image—0
CARAFE++: Unified Content-Aware ReAssembly of FEatures—0
CASNet: Common Attribute Support Network for image instance and panoptic segmentation—0
CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation—0
Category-level Shape Estimation for Densely Cluttered Objects—0
CAT: Learning to Collaborate Channel and Spatial Attention from Multi-Information Fusion—0
CellMixer: Annotation-free Semantic Cell Segmentation of Heterogeneous Cell Populations—0
CellSegmenter: unsupervised representation learning and instance segmentation of modular images—0
CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentation—0
Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images—0
CenterMask: single shot instance segmentation with point representation—0
CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation—0
Channel-wise Alignment for Adaptive Object Detection—0
Chargrid-OCR: End-to-end Trainable Optical Character Recognition for Printed Documents using Instance Segmentation—0
CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation—0
ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network—0
Classification Drives Geographic Bias in Street Scene Segmentation—0
Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation—0
ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotations—0
Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation—0
CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds—0
ClusterNet: 3D Instance Segmentation in RGB-D Images—0
ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning—0
Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection—0
CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation—0
CNN-based Preprocessing to Optimize Watershed-based Cell Segmentation in 3D Confocal Microscopy Images—0
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images—0
Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector—0
ColorRL: Reinforced Coloring for End-to-End Instance Segmentation—0
Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification—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