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/Hoyyyaard/LSceneLLM

공식 코드

LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual Preferences

https://github.com/HuCaoFighting/Swin-Unet

공식 코드

Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

https://github.com/HuXiaoling/TopoLoss

공식 코드

Topology-Preserving Deep Image Segmentation

https://github.com/Hua-YS/Multi-Scene-Recognition

공식 코드

MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images

https://github.com/Hua-YS/Prototype-based-Memory-Network

공식 코드

Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks

https://github.com/Hydragon516/PMN

공식 코드

Unsupervised Video Object Segmentation via Prototype Memory Network

https://github.com/HzFu/AGNet

공식 코드

Attention Guided Network for Retinal Image Segmentation

https://github.com/HzFu/COVID19_imaging_AI_paper_list

공식 코드

Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation

https://github.com/ICEORY/PMF

공식 코드

EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation

https://github.com/ICMLA-SAFL/SAFL_pytorch

공식 코드

SAFL: A Self-Attention Scene Text Recognizer with Focal Loss

https://github.com/IIGROUP/PUM

공식 코드

Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generation

https://github.com/IMSY-DKFZ/self-distilled-swin

공식 코드

Self-distillation for surgical action recognition

https://github.com/ISosnovik/nn4topopt

공식 코드

Neural networks for topology optimization

https://github.com/ISosnovik/top

공식 코드

Neural networks for topology optimization

https://github.com/Inha-CVAI/MADGNet

공식 코드

Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention

https://github.com/IntelVCL/MultiObjectiveOptimization

공식 코드

Multi-Task Learning as Multi-Objective Optimization

https://github.com/IoBT-VISTEC/OCTAve

공식 코드

OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation

https://github.com/JHL-HUST/IBCLN

공식 코드

Single Image Reflection Removal through Cascaded Refinement

https://github.com/JJGO/UniverSeg

공식 코드

UniverSeg: Universal Medical Image Segmentation

https://github.com/JWFangit/LOTVS-DADA

공식 코드

DADA: Driver Attention Prediction in Driving Accident Scenarios

https://github.com/Jacobew/AutoPanoptic

공식 코드

Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation

https://github.com/JeroenBertels/optimizingdice

공식 코드

Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice

https://github.com/JiYuanFeng/MCTrans

공식 코드

Multi-Compound Transformer for Accurate Biomedical Image Segmentation

https://github.com/Jianbo-Lab/LBIE

공식 코드

Language-Based Image Editing with Recurrent Attentive Models

https://github.com/Jianf-Wang/GRCNN

공식 코드

Convolutional Neural Networks with Gated Recurrent Connections

https://github.com/JianghaoWu/FPL-UDA

공식 코드

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

https://github.com/JianqiangWan/Super-BPD

공식 코드

Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation

https://github.com/JiaxiHe/Image-and-Video-Dehazing

공식 코드

Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization

https://github.com/JimTheBarbarian/Official-TTV-Segmentation

공식 코드

An Image Segmentation Model with Transformed Total Variation

https://github.com/JingchunCheng/FAVOS

공식 코드

Fast and Accurate Online Video Object Segmentation via Tracking Parts

https://github.com/JingchunCheng/SegFlow

공식 코드

SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

https://github.com/Jingkang50/OpenPSG

공식 코드

Panoptic Scene Graph Generation

https://github.com/JoetheReindeer/multiPI_TransBTS

공식 코드

multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Information

https://github.com/JoseLGomez/Co-training_SemSeg_UDA

공식 코드

Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

https://github.com/JosephPB/XNet

공식 코드

XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets

https://github.com/Jun-Pu/UCOS-DA

공식 코드

Unsupervised Camouflaged Object Segmentation as Domain Adaptation

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