Papers with Code 아카이브

Semantic Segmentation

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전체 기법전체 데이터셋

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/ibrain-lab/pingu

공식 코드

Perivascular space Identification Nnunet for Generalised Usage (PINGU)

https://github.com/iccv2595/svdp

공식 코드

Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction

https://github.com/icputrd/SLRNet

공식 코드

SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition Images

https://github.com/icsc-spoke2-repo/fastextendedvision-deepcluster

공식 코드

Datacube segmentation via Deep Spectral Clustering

https://github.com/idea-research/maskdino

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Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

https://github.com/idea-research/mp-former

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MP-Former: Mask-Piloted Transformer for Image Segmentation

https://github.com/idea-research/openseed

공식 코드

A Simple Framework for Open-Vocabulary Segmentation and Detection

https://github.com/idealwei/ASANet

공식 코드

Affinity Space Adaptation for Semantic Segmentation Across Domains

https://github.com/idt-iti/xai-video-summaries

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An Integrated Framework for Multi-Granular Explanation of Video Summarization

https://github.com/iduta/pyconv

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Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition

https://github.com/ifnspaml/CIL_Segmentation

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Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old Labels

https://github.com/ifnspaml/SGDepth

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Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance

https://github.com/ifnspaml/UBNA

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Unsupervised BatchNorm Adaptation (UBNA): A Domain Adaptation Method for Semantic Segmentation Without Using Source Domain Representations

https://github.com/ifnspaml/advattackdet

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Detecting Adversarial Perturbations in Multi-Task Perception

https://github.com/ifnspaml/amodalcityscapes

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Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline

https://github.com/ifnspaml/cbna

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Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation

https://github.com/ifnspaml/revt

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A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation

https://github.com/ifnspaml/s-amodal

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Foundation Models for Amodal Video Instance Segmentation in Automated Driving

https://github.com/ignf/flair-2-ai-challenge

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FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery

https://github.com/ignf/myria3d

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FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes

https://github.com/ignf/pacasam

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FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes

https://github.com/ihdia/Palmira

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Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts

https://github.com/ihmegroup/lossy_ml

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The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning

https://github.com/iigroup/hvisnet

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Real-time Human-Centric Segmentation for Complex Video Scenes

https://github.com/iipl-hangzhoudianziuniversity/defn-pytorch

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DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object Segmentation

https://github.com/ika-rwth-aachen/Cam2BEV

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A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View

https://github.com/ilareina/fullyattentional

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Fully Attentional Network for Semantic Segmentation

https://github.com/ilkerkesen/bvpr

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Modulating Bottom-Up and Top-Down Visual Processing via Language-Conditional Filters

https://github.com/imadalishah/HSI_SemanticSegmentationModels_AD_ADAS

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Hyperspectral Imaging-Based Perception in Autonomous Driving Scenarios: Benchmarking Baseline Semantic Segmentation Models

https://github.com/imadalishah/sfa_unet

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SFA-UNet: More Attention to Multi-Scale Contrast and Contextual Information in Infrared Small Object Segmentation

https://github.com/imagingyeditepe/segmentation-of-teeth-in-panoramic-x-ray-image

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Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing

https://github.com/imatge-upc/medical-2017-nipsw

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Cost-Effective Active Learning for Melanoma Segmentation

https://github.com/imatge-upc/mri-braintumor-segmentation

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MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures

https://github.com/imatge-upc/rsis

공식 코드

Recurrent Neural Networks for Semantic Instance Segmentation

https://github.com/imatge-upc/rvos-mots

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Curriculum Learning for Recurrent Video Object Segmentation

https://github.com/imesluh/vocalfolds

공식 코드

A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation

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