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

Showing 876900 of 2262 papers

TitleStatusHype
AutoBSS: An Efficient Algorithm for Block Stacking Style Search0
3D for Free: Crossmodal Transfer Learning using HD Maps0
A Unified Sequence Interface for Vision Tasks0
A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation0
iShape: A First Step Towards Irregular Shape Instance Segmentation0
Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation0
ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass0
A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision0
Contour Loss for Instance Segmentation via k-step Distance Transformation Image0
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation0
Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes0
Continental-Scale Building Detection from High Resolution Satellite Imagery0
Augment Before Copy-Paste: Data and Memory Efficiency-Oriented Instance Segmentation Framework for Sport-scenes0
A Fully Unsupervised Instance Segmentation Technique for White Blood Cell Images0
A2D2: Audi Autonomous Driving Dataset0
Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation0
Contextual Graph Reasoning Networks0
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection0
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks0
AttentionShift: Iteratively Estimated Part-Based Attention Map for Pointly Supervised Instance Segmentation0
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?0
How can we learn (more) from challenges? A statistical approach to driving future algorithm development0
Content-Aware Multi-Level Guidance for Interactive Instance Segmentation0
3D Feature Distillation with Object-Centric Priors0
1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-HAP5080.8Unverified
2ResNeSt-200 (multi-scale)AP5070.2Unverified
3CenterMask + VoVNetV2-99 (multi-scale)AP5066.2Unverified
4CenterMask + VoVNetV2-57 (single-scale)AP5060.8Unverified
5Co-DETRmask AP57.1Unverified
6CBNetV2 (EVA02, single-scale)mask AP56.1Unverified
7ISDA (ResNet-50)APL55.7Unverified
8EVAmask AP55.5Unverified
9FD-SwinV2-Gmask AP55.4Unverified
10Mask Frozen-DETRmask AP55.3Unverified
#ModelMetricClaimedVerifiedStatus
1InternImage-BGFLOPs501Unverified
2Co-DETRmask AP56.6Unverified
3ViT-CoMer-L (Mask RCNN, DINOv2)mask AP55.9Unverified
4InternImage-Hmask AP55.4Unverified
5EVAmask AP55Unverified
6Mask Frozen-DETRmask AP54.9Unverified
7MasK DINO (SwinL, multi-scale)mask AP54.5Unverified
8ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2Unverified
9GLEE-Promask AP54.2Unverified
10SwinV2-G (HTC++)mask AP53.7Unverified