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 2151–2200 of 2262 papers

TitleStatusHype
DetNet: Design Backbone for Object Detection—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning—0
Traits & Transferability of Adversarial Examples against Instance Segmentation & Object Detection—0
Deep Leaf Segmentation Using Synthetic DataCode0
Semi-convolutional Operators for Instance Segmentation—0
Instance Segmentation by Deep ColoringCode0
ClusterNet: 3D Instance Segmentation in RGB-D Images—0
Actor-Action Semantic Segmentation with Region Masks—0
Bounding Box Embedding for Single Shot Person Instance Segmentation—0
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation—0
Non-local RoIs for Instance Segmentation—0
Dynamic Multimodal Instance Segmentation guided by natural language queriesCode0
Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products—0
Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network—0
Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation—0
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation—0
A Dataset for Lane Instance Segmentation in Urban Environments—0
Active Testing: An Efficient and Robust Framework for Estimating Accuracy—0
Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation—0
Learning Instance Segmentation by InteractionCode0
Instance Search via Instance Level Segmentation and Feature Representation—0
Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks—0
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images—0
Bayesian Semantic Instance Segmentation in Open Set World—0
TernausNetV2: Fully Convolutional Network for Instance SegmentationCode0
Free Supervision From Video Games—0
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines—0
Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network—0
Adapting Mask-RCNN for Automatic Nucleus Segmentation—0
Learning to See the Invisible: End-to-End Trainable Amodal Instance SegmentationCode0
MVTec D2S: Densely Segmented Supermarket Dataset—0
DetNet: A Backbone network for Object DetectionCode0
Iterative fully convolutional neural networks for automatic vertebra segmentation and identificationCode0
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human ParsingCode0
Weakly Supervised Instance Segmentation using Class Peak ResponseCode0
Predicting Future Instance Segmentation by Forecasting Convolutional FeaturesCode0
Pose2Seg: Detection Free Human Instance SegmentationCode0
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding ModelCode0
Learning to Cluster for Proposal-Free Instance SegmentationCode0
Learning deep structured active contours end-to-endCode0
Learning to Segment via Cut-and-PasteCode0
Pseudo Mask Augmented Object Detection—0
IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional NetworkCode0
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image—0
Towards End-to-End Lane Detection: an Instance Segmentation ApproachCode0
Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters—0
SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain AdaptationCode0
PixelLink: Detecting Scene Text via Instance SegmentationCode0
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network—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