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 726750 of 2262 papers

TitleStatusHype
Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group SoftmaxCode1
Coherent Reconstruction of Multiple Humans from a Single ImageCode1
GradAug: A New Regularization Method for Deep Neural NetworksCode1
VirTex: Learning Visual Representations from Textual AnnotationsCode1
Boundary-assisted Region Proposal Networks for Nucleus SegmentationCode1
D2Det: Towards High Quality Object Detection and Instance SegmentationCode1
Improving Convolutional Networks With Self-Calibrated ConvolutionsCode1
Interactive Object Segmentation With Inside-Outside GuidanceCode1
Learning Saliency Propagation for Semi-Supervised Instance SegmentationCode1
CenterMask: Real-Time Anchor-Free Instance SegmentationCode1
3D Part Guided Image Editing for Fine-Grained Object UnderstandingCode1
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance SegmentationCode1
NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy ImagesCode1
NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisationCode1
Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3Code1
AutoSweep: Recovering 3D Editable Objectsfrom a Single PhotographCode1
Attention-guided Context Feature Pyramid Network for Object DetectionCode1
Towards Streaming PerceptionCode1
Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance SegmentationCode1
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weightingCode1
Dynamic Scale Training for Object DetectionCode1
Fashionpedia: Ontology, Segmentation, and an Attribute Localization DatasetCode1
Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local ConstraintsCode1
A Transductive Approach for Video Object SegmentationCode1
Evolving Normalization-Activation LayersCode1
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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