Papers with Code 아카이브

Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/fudannlplab/mousi

공식 코드

MouSi: Poly-Visual-Expert Vision-Language Models

https://github.com/fumyou13/ldbe

공식 코드

Domain Adaptive Semantic Segmentation without Source Data

https://github.com/function2-llx/mmmm

공식 코드

VividMed: Vision Language Model with Versatile Visual Grounding for Medicine

https://github.com/fundamentalvision/siamese-image-modeling

공식 코드

Siamese Image Modeling for Self-Supervised Vision Representation Learning

https://github.com/funkelab/cellulus

공식 코드

Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images

https://github.com/fupiao1998/TrasformerSOD

공식 코드

Generative Transformer for Accurate and Reliable Salient Object Detection

https://github.com/fupiao1998/transformersod

공식 코드

Generative Transformer for Accurate and Reliable Salient Object Detection

https://github.com/fuying-wang/mgca

공식 코드

Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

https://github.com/fvergaracontesse/hyperion_solar_net

공식 코드

HyperionSolarNet: Solar Panel Detection from Aerial Images

https://github.com/fvisin/reseg

공식 코드

ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation

https://github.com/fwu11/mcc

공식 코드

Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation

https://github.com/fxxjuses/micformer

공식 코드

Multimodal Information Interaction for Medical Image Segmentation

https://github.com/fy-vision/diga

공식 코드

DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation

https://github.com/fy-vision/loopda

공식 코드

LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation

https://github.com/fyangneil/pavement-crack-detection

공식 코드

Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection

https://github.com/fyu/dilation

공식 코드

Multi-Scale Context Aggregation by Dilated Convolutions

https://github.com/fyu/drn

공식 코드

Dilated Residual Networks

https://github.com/fzehrauslu/tms-net

공식 코드

TMS-Net: A Segmentation Network Coupled With A Run-time Quality Control Method For Robust Cardiac Image Segmentation

https://github.com/fzi-forschungszentrum-informatik/NNAD

공식 코드

Simultaneous Object Detection and Semantic Segmentation

https://github.com/fzmi/ubdd

공식 코드

Learning Efficient Unsupervised Satellite Image-based Building Damage Detection

https://github.com/g-meghana-reddy/open-world-panoptic-segmentation

공식 코드

Lidar Panoptic Segmentation in an Open World

https://github.com/gabriel-sgama/semantic-superpoint

공식 코드

Semantic SuperPoint: A Deep Semantic Descriptor

https://github.com/gabriel-tjio/ash

공식 코드

Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation

https://github.com/gabrielhuang/awesome-few-shot-object-detection

공식 코드

A Survey of Self-Supervised and Few-Shot Object Detection

https://github.com/gaia-vision/gaia-ssl

공식 코드

DATA: Domain-Aware and Task-Aware Self-supervised Learning

https://github.com/gama-ufsc/brain-extraction-for-tumor-segmentation

공식 코드

Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?

https://github.com/gangzhang842/cfnet

공식 코드

Center Focusing Network for Real-Time LiDAR Panoptic Segmentation

https://github.com/ganlab/ostra

공식 코드

A One Stop 3D Target Reconstruction and multilevel Segmentation Method

https://github.com/ganlumomo/thermal-uda-attention

공식 코드

Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network

https://github.com/gaobb/DLDL

공식 코드

Deep Label Distribution Learning with Label Ambiguity

https://github.com/gaomingqi/track-anything

공식 코드

Track Anything: Segment Anything Meets Videos

https://github.com/gaopengcuhk/Container

공식 코드

Container: Context Aggregation Network

https://github.com/gaoyuan2/apcotta

공식 코드

APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds

https://github.com/gaozhitong/SP_guided_Noisy_Label_Seg

공식 코드

Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation

https://github.com/gaozhitong/multishiftseg

공식 코드

Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts

https://github.com/gardnery/duedl

공식 코드

DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation

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