Papers with Code 아카이브

Semantic Segmentation

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하위 태스크

29

평가표

150

보존된 평가 기록

1,918

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/neheller/kits21

공식 코드

The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

https://github.com/neheller/labels18

공식 코드

Imperfect Segmentation Labels: How Much Do They Matter?

https://github.com/neo85824/epsnet

공식 코드

EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion

https://github.com/nerminsamet/houghnet

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HoughNet: Integrating near and long-range evidence for visual detection

https://github.com/nerminsamet/seedal

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You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation

https://github.com/nessessence/ZeroGuidanceSeg

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Zero-guidance Segmentation Using Zero Segment Labels

https://github.com/netw0rkf10w/crf

공식 코드

Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond

https://github.com/neu-real/streammos

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StreamMOS: Streaming Moving Object Segmentation with Multi-View Perception and Dual-Span Memory

https://github.com/neurai-lab/mono-pose-attack

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Adversarial Attacks on Monocular Pose Estimation

https://github.com/neurai-lab/mt-sfmlearner

공식 코드

Transformers in Unsupervised Structure-from-Motion

https://github.com/neuro-ml/domain_shift_anatomy

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Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation

https://github.com/neuro-ml/inverse_weighting

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Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation

https://github.com/neuroailab/eisen

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Unsupervised Segmentation in Real-World Images via Spelke Object Inference

https://github.com/neuronflow/blob_loss

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blob loss: instance imbalance aware loss functions for semantic segmentation

https://github.com/neuropoly/deep-active-learning

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Deep Active Learning for Axon-Myelin Segmentation on Histology Data

https://github.com/neuropoly/domainadaptation

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Unsupervised domain adaptation for medical imaging segmentation with self-ensembling

https://github.com/neuropoly/ivado-medical-imaging

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Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?

https://github.com/newbee-ML/MIFN-Velocity-Picking

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Automatic Velocity Picking Using a Multi-Information Fusion Deep Semantic Segmentation Network

https://github.com/nexer8/se3d

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SE3D: A Framework For Saliency Method Evaluation In 3D Imaging

https://github.com/ngaggion/HybridGNet

공식 코드

Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis

https://github.com/ngaggion/chest-xray-landmark-dataset

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Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis

https://github.com/ngaggion/ubd_sourcecode

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Unsupervised bias discovery in medical image segmentation

https://github.com/nguyendinhson-kaist/mmsports23-seg-autoid

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Instance Segmentation under Occlusions via Location-aware Copy-Paste Data Augmentation

https://github.com/ngzhili/syntable

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SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes

https://github.com/nhikieu/spatialvolumetricmultimodal

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General-Purpose Multimodal Transformer meets Remote Sensing Semantic Segmentation

https://github.com/nhw649/eov-seg

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EOV-Seg: Efficient Open-Vocabulary Panoptic Segmentation

https://github.com/nhw649/scsd

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Exploring Semantic Consistency and Style Diversity for Domain Generalized Semantic Segmentation

https://github.com/nian-liu/PiCANet

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PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency Detection

https://github.com/nianticlabs/footprints

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Footprints and Free Space from a Single Color Image

https://github.com/nibtehaz/MultiResUNet

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MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation

https://github.com/niccolocavagnero/pem

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PEM: Prototype-based Efficient MaskFormer for Image Segmentation

https://github.com/nickeopti/bach-contrastive-segmentation

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Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation

https://github.com/nickstheisen/hyperseg

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HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios

https://github.com/niessner/Matterport

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Matterport3D: Learning from RGB-D Data in Indoor Environments

https://github.com/nightrome/cocostuff

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COCO-Stuff: Thing and Stuff Classes in Context

https://github.com/nightrome/matconvnet-calvin

공식 코드

Region-based semantic segmentation with end-to-end training

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.