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 1601–1650 of 2262 papers

TitleStatusHype
Weakly Supervised Instance Segmentation using Motion Information via Optical Flow—0
Efficient Video Segmentation Models with Per-frame Inference—0
Amodal Panoptic Segmentation—0
ISDA: Position-Aware Instance Segmentation with Deformable AttentionCode0
An Object Aware Hybrid U-Net for Breast Tumour Annotation—0
Learning with Free Object Segments for Long-Tailed Instance Segmentation—0
Iterative Learning for Instance Segmentation—0
Mirror-Yolo: A Novel Attention Focus, Instance Segmentation and Mirror Detection Model—0
A General Deep Learning framework for Neuron Instance Segmentation based on Efficient UNet and Morphological Post-processing—0
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection—0
STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation—0
SCR: Smooth Contour Regression with Geometric Priors—0
Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings—0
StandardSim: A Synthetic Dataset For Retail Environments—0
Automotive Parts Assessment: Applying Real-time Instance-Segmentation Models to Identify Vehicle Parts—0
CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation—0
Partial-Attribution Instance Segmentation for Astronomical Source Detection and DeblendingCode0
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband ItemsCode0
INS-Conv: Incremental Sparse Convolution for Online 3D Segmentation—0
Yeast cell segmentation in microstructured environments with deep learningCode0
ETHSeg: An Amodel Instance Segmentation Network and a Real-World Dataset for X-Ray Waste Inspection—0
A Graph Matching Perspective With Transformers on Video Instance Segmentation—0
DArch: Dental Arch Prior-Assisted 3D Tooth Instance Segmentation With Weak Annotations—0
Instance Segmentation With Mask-Supervised Polygonal Boundary Transformers—0
TWIST: Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance SegmentationCode0
Segment-Fusion: Hierarchical Context Fusion for Robust 3D Semantic Segmentation—0
Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped AttentionCode0
FourierMask: Instance Segmentation using Fourier Mapping in Implicit Neural Networks—0
Are Large-scale Datasets Necessary for Self-Supervised Pre-training?—0
A New Image Codec Paradigm for Human and Machine Uses—0
3D Instance Segmentation of MVS Buildings—0
A Simple Single-Scale Vision Transformer for Object Localization and Instance SegmentationCode0
GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal SegmentationCode0
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning—0
Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards—0
Detection of Tumour Infiltrating Lymphocytes in CD3 and CD8 Stained Histopathological Images using a Two-Phase Deep CNNCode0
Deep Level Set for Box-supervised Instance Segmentation in Aerial ImagesCode0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
DIY Graphics Tab: A Cost-Effective Alternative to Graphics Tablet for Educators—0
Learning to Detect Every Thing in an Open World—0
Hybrid Instance-aware Temporal Fusion for Online Video Instance Segmentation—0
Artificial Intelligence-driven Image Analysis of Bacterial Cells and BiofilmsCode0
Dimensions of Motion: Monocular Prediction through Flow Subspaces—0
The Second Place Solution for ICCV2021 VIPriors Instance Segmentation Challenge—0
Putting 3D Spatially Sparse Networks on a Diet—0
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency—0
DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports ScenesCode0
The MIS Check-Dam Dataset for Object Detection and Instance Segmentation Tasks—0
Point Cloud Instance Segmentation with Semi-supervised Bounding-Box MiningCode0
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware AssociationCode0
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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