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/ycwu1997/ss-net

공식 코드

Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation

https://github.com/ydhongHIT/DDRNet

공식 코드

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

https://github.com/yec22/fine-grained-indoor-recon

공식 코드

Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement

https://github.com/yecy749/gsnet

공식 코드

Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation

https://github.com/yeerwen/uniseg

공식 코드

UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner

https://github.com/yekeren/WSSGG

공식 코드

Linguistic Structures as Weak Supervision for Visual Scene Graph Generation

https://github.com/yeyimilk/deep-learning-for-manatee-counting

공식 코드

Counting Manatee Aggregations using Deep Neural Networks and Anisotropic Gaussian Kernel

https://github.com/yezanting/mln-net-verson1

공식 코드

MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization

https://github.com/yezi-66/ife

공식 코드

Instructive Feature Enhancement for Dichotomous Medical Image Segmentation

https://github.com/yhc2021/avs-net

공식 코드

AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding

https://github.com/yhy-whu/tk-mamba

공식 코드

TK-Mamba: Marrying KAN with Mamba for Text-Driven 3D Medical Image Segmentation

https://github.com/yhydhx/samaug

공식 코드

SAMAug: Point Prompt Augmentation for Segment Anything Model

https://github.com/yhygao/UTNet

공식 코드

UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

https://github.com/yhygao/cbim-medical-image-segmentation

공식 코드

A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

https://github.com/yhygao/universal-medical-image-segmentation

공식 코드

Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation

https://github.com/yichen928/STRL

공식 코드

Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds

https://github.com/yichizhang98/sam4mis

공식 코드

Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions

https://github.com/yichizhang98/seganypet

공식 코드

SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images

https://github.com/yichizhang98/semisam

공식 코드

SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization

https://github.com/yifangao112/desam

공식 코드

DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation

https://github.com/yifanlu0227/chatsim

공식 코드

Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents

https://github.com/yigitgunduc/vit-gan

공식 코드

Vit-GAN: Image-to-image Translation with Vision Transformes and Conditional GANS

https://github.com/yikang-li/FactorizableNet

공식 코드

Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation

https://github.com/yikang-li/MSDN

공식 코드

Scene Graph Generation from Objects, Phrases and Region Captions

https://github.com/yikeyin97/eir

공식 코드

Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation

https://github.com/yilmazkadir/segmentation_losses

공식 코드

Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook

https://github.com/yinyunie/RfDNet

공식 코드

RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction

https://github.com/yinyunie/Total3DUnderstanding

공식 코드

Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image

https://github.com/yishuliu/transattunet

공식 코드

TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation

https://github.com/yiwuzhong/sgg_from_nls

공식 코드

Learning to Generate Scene Graph from Natural Language Supervision

https://github.com/yixinL7/Direct-Style-Transfer

공식 코드

On Learning Text Style Transfer with Direct Rewards

https://github.com/yizhezhang2000/samaug

공식 코드

Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model

https://github.com/yizhezhang2000/samdsk

공식 코드

SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

https://github.com/yizhezhang2000/ure

공식 코드

Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Models

https://github.com/yjcaimeow/SISNet

공식 코드

Semantic Scene Completion via Integrating Instances and Scene in-the-Loop

https://github.com/yjxiong/caffe

공식 코드

Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs

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