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 1201–1250 of 2262 papers

TitleStatusHype
3rd Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation—0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
A2D2: Audi Autonomous Driving Dataset—0
A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation—0
A2-FPN: Attention Aggregation Based Feature Pyramid Network for Instance Segmentation—0
A2VIS: Amodal-Aware Approach to Video Instance Segmentation—0
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI—0
A Bilayer Segmentation-Recombination Network for Accurate Segmentation of Overlapping C. elegans—0
Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition—0
Investigating Object Compositionality in Generative Adversarial Networks—0
Accelerating the creation of instance segmentation training sets through bounding box annotation—0
Accurate and efficient zero-shot 6D pose estimation with frozen foundation models—0
Accurately identifying vertebral levels in large datasets—0
ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception—0
A Closed-Loop System for Improving Annotation Quality and Efficiency—0
A closer look at network resolution for efficient network design—0
A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation—0
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects—0
A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color—0
Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products—0
Active Testing: An Efficient and Robust Framework for Estimating Accuracy—0
Actor-Action Semantic Segmentation with Region Masks—0
AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts—0
Adapting Mask-RCNN for Automatic Nucleus Segmentation—0
Adapting SAM for Volumetric X-Ray Data-sets of Arbitrary Sizes—0
Adapting Segment Anything Model for Unseen Object Instance Segmentation—0
Adapting Vision Foundation Models for Plant Phenotyping—0
Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation—0
A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing—0
A Dataset for Lane Instance Segmentation in Urban Environments—0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers—0
Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation—0
Advancing Manga Analysis: Comprehensive Segmentation Annotations for the Manga109 Dataset—0
Advancing Oyster Phenotype Segmentation with Multi-Network Ensemble and Multi-Scale mechanism—0
Advancing SEM Based Nano-Scale Defect Analysis in Semiconductor Manufacturing for Advanced IC Nodes—0
Adversarial Stain Transfer to Study the Effect of Color Variation on Cell Instance Segmentation—0
A Dynamic Feature Interaction Framework for Multi-task Visual Perception—0
A Fully Unsupervised Instance Segmentation Technique for White Blood Cell Images—0
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation—0
Aggregation With Feature Detection—0
A Graph Matching Perspective With Transformers on Video Instance Segmentation—0
AHCPTQ: Accurate and Hardware-Compatible Post-Training Quantization for Segment Anything Model—0
A Histogram Thresholding Improvement to Mask R-CNN for Scalable Segmentation of New and Old Rural Buildings—0
A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection—0
AI-based thermal bridge detection of building rooftops on district scale using aerial images—0
AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers—0
ALBERT: Advanced Localization and Bidirectional Encoder Representations from Transformers for Automotive Damage Evaluation—0
Aligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives—0
All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes—0
All You Need is a Second Look: Towards Arbitrary-Shaped Text Detection—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
8GLEE-Promask AP54.2—Unverified
9ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2—Unverified
10SwinV2-G (HTC++)mask AP53.7—Unverified