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/leixuai/imbalanced_crack_detection_with_gans

공식 코드

Revisiting Generative Adversarial Networks for Binary Semantic Segmentation on Imbalanced Datasets

https://github.com/leiyi-hu/mona

공식 코드

Adapter is All You Need for Tuning Visual Tasks

https://github.com/lelechen63/MRI-tumor-segmentation-Brats

공식 코드

MRI Tumor Segmentation with Densely Connected 3D CNN

https://github.com/lelouedec/phd_3dperception

공식 코드

3D shape sensing and deep learning-based segmentation of strawberries

https://github.com/lely475/ocelot23algo

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SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction

https://github.com/lemmersj/ground-truth-or-daer

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Ground-truth or DAER: Selective Re-query of Secondary Information

https://github.com/leofansq/CPCL

공식 코드

Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation

https://github.com/leofansq/spidermesh

공식 코드

SpiderMesh: Spatial-aware Demand-guided Recursive Meshing for RGB-T Semantic Segmentation

https://github.com/leolyj/DsCML

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Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation

https://github.com/leolyj/san-saw

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Semantic-Aware Domain Generalized Segmentation

https://github.com/leolyliu/HOI4D-Instructions

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HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction

https://github.com/leongdong/bias-decomposition-networks

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Unsupervised Decomposition Networks for Bias Field Correction in MR Image

https://github.com/leonnnop/gmmseg

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GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

https://github.com/leonnnop/locater

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Local-Global Context Aware Transformer for Language-Guided Video Segmentation

https://github.com/leoxiaobin/deep-high-resolution-net.pytorch

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High-Resolution Representations for Labeling Pixels and Regions

https://github.com/lescientifik/open_brats2020

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Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution

https://github.com/lesonly/senet

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Sharp Eyes: A Salient Object Detector Working The Same Way as Human Visual Characteristics

https://github.com/letitiabanana/pnp-ovss

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Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf Vision-Language Models

https://github.com/leungtsang/cpcdr

공식 코드

Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene Segmentation

https://github.com/levan92/occlusion-copy-paste

공식 코드

Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation

https://github.com/levayz/dinov2-based-self-supervised-learning

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DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation

https://github.com/levayz/protosam

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ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models

https://github.com/lewismmason/Thesis-Public

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K-Origins: Better Colour Quantification for Neural Networks

https://github.com/lexieyang/3d-pointzshots

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3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic Gap

https://github.com/lgnyu/vitseg

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Applying ViT in Generalized Few-shot Semantic Segmentation

https://github.com/lhaof/asa

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Attentive Symmetric Autoencoder for Brain MRI Segmentation

https://github.com/lhaof/nnmamba

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nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

https://github.com/lhaof/nudiff

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Diffusion-based Data Augmentation for Nuclei Image Segmentation

https://github.com/lhaof/nuseg

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Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework

https://github.com/lhduan/condaformer

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ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding

https://github.com/lhoangan/341n143

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Three for one and one for three: Flow, Segmentation, and Surface Normals

https://github.com/lhoangan/multas

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Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data

https://github.com/lhoyer/DAFormer

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DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

https://github.com/lhoyer/hrda

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HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation

https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth

공식 코드

Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation

https://github.com/lhoyer/mic

공식 코드

MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation

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