Papers with Code 아카이브

2D Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/nachifur/LLPC

공식 코드

Local Label Point Correction for Edge Detection of Overlapping Cervical Cells

https://github.com/nadir121/Sat_DA

공식 코드

Improving land cover segmentation across satellites using domain adaptation

https://github.com/nadja1611/lifting-based-variational-multiclass-segmentation

공식 코드

Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging

https://github.com/naisops/CTranS

공식 코드

ConvTransSeg: A Multi-resolution Convolution-Transformer Network for Medical Image Segmentation

https://github.com/nankepan/VIPMT

공식 코드

Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation

https://github.com/nanyomy/cmmas

공식 코드

Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion

https://github.com/nasim-aust/braintumor-segmentation-3dunet-brats

공식 코드

Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

https://github.com/nataliiamolch/interpret-lesion-unc

공식 코드

Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis

https://github.com/naver-ai/egtr

공식 코드

EGTR: Extracting Graph from Transformer for Scene Graph Generation

https://github.com/naver-ai/maskris

공식 코드

MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation

https://github.com/naver-ai/zim

공식 코드

ZIM: Zero-Shot Image Matting for Anything

https://github.com/naver/oasis

공식 코드

On the Road to Online Adaptation for Semantic Image Segmentation

https://github.com/nayutayuki/lightweight-nmode-decoders-for-u-like-networks

공식 코드

A Lightweight U-like Network Utilizing Neural Memory Ordinary Differential Equations for Slimming the Decoder

https://github.com/nbalepur/text-fact-transfer

공식 코드

Text Fact Transfer

https://github.com/ncclabsustech/ifcis-55

공식 코드

Immunofluorescence Capillary Imaging Segmentation: Cases Study

https://github.com/neerakara/test-time-adaptable-neural-networks-for-domain-generalization

공식 코드

Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

https://github.com/negin-ghamsarian/transformation-invariant-self-training-miccai23

공식 코드

Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training

https://github.com/neo85824/epsnet

공식 코드

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

https://github.com/neurai-lab/cude-monodepthcl

공식 코드

Continual Learning of Unsupervised Monocular Depth from Videos

https://github.com/neurai-lab/mimdepth

공식 코드

Image Masking for Robust Self-Supervised Monocular Depth Estimation

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

공식 코드

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

공식 코드

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

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

공식 코드

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

https://github.com/neuroailab/eisen

공식 코드

Unsupervised Segmentation in Real-World Images via Spelke Object Inference

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

공식 코드

Deep Active Learning for Axon-Myelin Segmentation on Histology Data

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

공식 코드

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

https://github.com/ngaggion/HybridGNet

공식 코드

Hybrid graph convolutional neural networks for landmark-based anatomical segmentation

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

공식 코드

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

https://github.com/ngaggion/ubd_sourcecode

공식 코드

Unsupervised bias discovery in medical image segmentation

https://github.com/nianticlabs/monodepth2

공식 코드

Digging Into Self-Supervised Monocular Depth Estimation

https://github.com/nibtehaz/MultiResUNet

공식 코드

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

https://github.com/niccolocavagnero/pem

공식 코드

PEM: Prototype-based Efficient MaskFormer for Image Segmentation

https://github.com/nicholasli1995/s-3d-rcnn

공식 코드

Joint stereo 3D object detection and implicit surface reconstruction

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

공식 코드

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

https://github.com/nickhaidos/scenir-icml2025

공식 코드

SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval

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