Papers with Code 아카이브

2D Semantic Segmentation

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전체 기법전체 데이터셋

하위 태스크

17

평가표

11

보존된 평가 기록

42

코드

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

https://github.com/anok3111/loco

공식 코드

Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image Segmentation

https://github.com/anonymous4545/jers

공식 코드

One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

https://github.com/anpc849/QMaxViT-Unet

공식 코드

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images

https://github.com/anthonimes/superpixels-community-detection

공식 코드

Superpixels algorithms through network community detection

https://github.com/antoinehabis/Deep-ContourFlow

공식 코드

Deep ContourFlow: Advancing Active Contours with Deep Learning

https://github.com/antonotnawang/naviairway

공식 코드

NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline

https://github.com/anyloc/revisit-anything

공식 코드

Revisit Anything: Visual Place Recognition via Image Segment Retrieval

https://github.com/aod321/STN-iCNN

공식 코드

End-to-End Face Parsing via Interlinked Convolutional Neural Networks

https://github.com/aolius/semi-fst

공식 코드

Semi-Supervised Formality Style Transfer with Consistency Training

https://github.com/aounon/center-smoothing

공식 코드

Center Smoothing: Certified Robustness for Networks with Structured Outputs

https://github.com/apchenstu/sofgan

공식 코드

SofGAN: A Portrait Image Generator with Dynamic Styling

https://github.com/aplesner/Breaking-reCAPTCHAv2

공식 코드

Breaking reCAPTCHAv2

https://github.com/apple/ARKitScenes

공식 코드

ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data

https://github.com/apple/ml-autofocusformer

공식 코드

AutoFocusFormer: Image Segmentation off the Grid

https://github.com/apple/ml-dms-dataset

공식 코드

A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing

https://github.com/apple/ml-hypersim

공식 코드

Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

https://github.com/apple/ml-mobileone

공식 코드

MobileOne: An Improved One millisecond Mobile Backbone

https://github.com/apple1986/LeViT_UNet

공식 코드

LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation

https://github.com/apple1986/samatch

공식 코드

A SAM-guided and Match-based Semi-Supervised Segmentation Framework for Medical Imaging

https://github.com/appletea233/al-ref-sam2

공식 코드

Unleashing the Temporal-Spatial Reasoning Capacity of GPT for Training-Free Audio and Language Referenced Video Object Segmentation

https://github.com/applied-ai-lab/genesis

공식 코드

GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement

https://github.com/arcadelab/fastsam3d_slicer

공식 코드

FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification

https://github.com/arcadelab/fluorosam

공식 코드

FluoroSAM: A Language-aligned Foundation Model for X-ray Image Segmentation

https://github.com/ardamamur/egoexor

공식 코드

EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding

https://github.com/arnab39/FewShot_GAN-Unet3D

공식 코드

Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

https://github.com/arnab39/Semi-supervised-segmentation-cycleGAN

공식 코드

Revisiting CycleGAN for semi-supervised segmentation

https://github.com/arnaudjudge/rl4seg

공식 코드

Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning

https://github.com/arplaboratory/PENet

공식 코드

PENet: A Joint Panoptic Edge Detection Network

https://github.com/arplaboratory/coped

공식 코드

CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World Environments

https://github.com/arron2003/rgbd2full3d

공식 코드

Complete 3D Scene Parsing from an RGBD Image

https://github.com/arthurdouillard/CVPR2021_PLOP

공식 코드

PLOP: Learning without Forgetting for Continual Semantic Segmentation

https://github.com/artonson/autoinst

공식 코드

AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D Scans

https://github.com/aryan-at-ul/electronic-imaging-2025-paper-4492

공식 코드

Image Segmentation: Inducing graph-based learning

https://github.com/asaakyan/explain-st

공식 코드

ICLEF: In-Context Learning with Expert Feedback for Explainable Style Transfer

https://github.com/asbjrnmunk/mdd-unet

공식 코드

MDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of Concept

https://github.com/ashawkey/dimr

공식 코드

Point Scene Understanding via Disentangled Instance Mesh Reconstruction

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