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/YeLyuUT/MSDNet

공식 코드

UAVid: A Semantic Segmentation Dataset for UAV Imagery

https://github.com/YesianRohn/TextSSR

공식 코드

TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition

https://github.com/YichiZhang98/DTML

공식 코드

Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation

https://github.com/YimingXiao98/DamageCAT

공식 코드

DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization

https://github.com/YingjieYin/Directional-Deep-Embedding-and-Appearance-Learning-for-Fast-Video-Object-Segmentation

공식 코드

Directional Deep Embedding and Appearance Learning for Fast Video Object Segmentation

https://github.com/YorkUCVIL/Static-Dynamic-Interpretability

공식 코드

A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information

https://github.com/YouzhiTian/Structured-Content-Preservation-for-Unsupervised-Text-Style-Transfer

공식 코드

Structured Content Preservation for Unsupervised Text Style Transfer

https://github.com/YtongXie/CoTr

공식 코드

CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

https://github.com/YuLiu-LY/BO-QSA

공식 코드

Improving Object-centric Learning with Query Optimization

https://github.com/Yuqifan1117/CaCao

공식 코드

Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World

https://github.com/Yuxuan-Wen/Sim2real-Guidewire-Seg

공식 코드

Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray Fluoroscopy

https://github.com/ZENGXH/DMM_Net

공식 코드

DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation

https://github.com/ZJUGiveLab/UNet-Version

공식 코드

UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation

https://github.com/ZJULearning/CariFaceParsing

공식 코드

Weakly-supervised Caricature Face Parsing through Domain Adaptation

https://github.com/Zengyi-Qin/MonoGRNet

공식 코드

MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization

https://github.com/Zengyi-Qin/Weakly-Supervised-3D-Object-Detection

공식 코드

Weakly Supervised 3D Object Detection from Point Clouds

https://github.com/ZerojumpLine/OverfittingUnderClassImbalance

공식 코드

Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation

https://github.com/ZhengyuZhao/Adi-Red-Scene

공식 코드

From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition

https://github.com/ZhilingYan/Biomedical-SAM-2

공식 코드

Biomedical SAM 2: Segment Anything in Biomedical Images and Videos

https://github.com/Zhourobotics/Query-3DGS-LLM

공식 코드

Query3D: LLM-Powered Open-Vocabulary Scene Segmentation with Language Embedded 3D Gaussian

https://github.com/a-ngj/image-segmentation-cli

공식 코드

Visual Context-Aware Person Fall Detection

https://github.com/a2amir/sfs-a68

공식 코드

SFS-A68: a dataset for the segmentation of space functions in apartment buildings

https://github.com/a514514772/progfed

공식 코드

ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training

https://github.com/aa200647963/sgg-dhl

공식 코드

Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation

https://github.com/aangelopoulos/rcps

공식 코드

Distribution-Free, Risk-Controlling Prediction Sets

https://github.com/abdul2706/MaxViT-UNet

공식 코드

MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation

https://github.com/abearman/whats-the-point1

공식 코드

What's the Point: Semantic Segmentation with Point Supervision

https://github.com/abhi4ssj/few-shot-segmentation

공식 코드

'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images

https://github.com/abhinavkashyap/dct

공식 코드

So Different Yet So Alike! Constrained Unsupervised Text Style Transfer

https://github.com/abhishekdbihani/MudrockNet

공식 코드

MudrockNet: Semantic Segmentation of Mudrock SEM Images through Deep Learning

https://github.com/abourki/sota-semantically-aware-nerfs

공식 코드

Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review

https://github.com/acaglayan/CNN_randRNN

공식 코드

When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene Recognition

https://github.com/acasamitjana/uslr

공식 코드

USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR

https://github.com/acecreamu/color-constancy-googlenet

공식 코드

Artificial Color Constancy via GoogLeNet with Angular Loss Function

https://github.com/activevisionlab/awesome-llm-3d

공식 코드

When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models

https://github.com/adeline-cs/GTR

공식 코드

Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition

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