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/GATECH-EIC/Omni-Recon

공식 코드

Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields

https://github.com/GOALCLEOPATRA/MLM

공식 코드

MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities

https://github.com/GT-SALT/FormalityStyleTransfer

공식 코드

Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization

https://github.com/GatorSense/MIMRF

공식 코드

Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty

https://github.com/GeWu-Lab/MUSIC-AVQA

공식 코드

Learning to Answer Questions in Dynamic Audio-Visual Scenarios

https://github.com/Geo-Tell/AncRec

공식 코드

Shape Anchor Guided Holistic Indoor Scene Understanding

https://github.com/GewelsJI/FSNet

공식 코드

Full-Duplex Strategy for Video Object Segmentation

https://github.com/GinZhu/RDST

공식 코드

A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning

https://github.com/GiuliaDAngelo/Speckegomotion

공식 코드

Wandering around: A bioinspired approach to visual attention through object motion sensitivity

https://github.com/GradientSpaces/CrossOver

공식 코드

CrossOver: 3D Scene Cross-Modal Alignment

https://github.com/GradientSpaces/LivingScenes

공식 코드

Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments

https://github.com/GuanRunwei/Achelous

공식 코드

Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar

https://github.com/Guzaiwang/CE-Net

공식 코드

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

https://github.com/HCIILAB/Scene-Text-Recognition

공식 코드

Text Recognition in the Wild: A Survey

https://github.com/HCPLab-SYSU/ATEN

공식 코드

Adaptive Temporal Encoding Network for Video Instance-level Human Parsing

https://github.com/HKBU-HPML/IRS

공식 코드

IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimation

https://github.com/HSE-asavchenko/mobile-visual-preferences

공식 코드

Preferences Prediction using a Gallery of Mobile Device based on Scene Recognition and Object Detection

https://github.com/HUANGLIZI/ScribFormer

공식 코드

ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation

https://github.com/HYOJINPARK/Reuse_VOS

공식 코드

Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation

https://github.com/HYOJINPARK/TTVOS

공식 코드

TTVOS: Lightweight Video Object Segmentation with Adaptive Template Attention Module and Temporal Consistency Loss

https://github.com/HaoZheng94/Ensemble

공식 코드

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

https://github.com/HealthX-Lab/HGI-SAM

공식 코드

Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning

https://github.com/HealthX-Lab/MedCLIP-SAM

공식 코드

MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation

https://github.com/HeatherJiaZG/SuperGlue-pytorch

공식 코드

Physarum Powered Differentiable Linear Programming Layers and Applications

https://github.com/HelenMa9998/Selective_Uncertainty_AL

공식 코드

Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation

https://github.com/HengyiWang/SIRNet

공식 코드

Boosting Video Object Segmentation based on Scale Inconsistency

https://github.com/HenrryBryant/Scene-Recognition-in-3D

공식 코드

Indoor Scene Recognition in 3D

https://github.com/HenryJunW/TAG

공식 코드

TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation

https://github.com/Henryjiepanli/Building-Guided-Pseudo-Label-Learning-for-Cross-Modal-Building-Damage-Mapping

공식 코드

Building-Guided Pseudo-Label Learning for Cross-Modal Building Damage Mapping

https://github.com/HiLab-git/ACELoss

공식 코드

Learning Euler's Elastica Model for Medical Image Segmentation

https://github.com/HiLab-git/CA-Net

공식 코드

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

https://github.com/HiLab-git/DTC

공식 코드

Semi-supervised Medical Image Segmentation through Dual-task Consistency

https://github.com/HiLab-git/SSL4MIS

공식 코드

Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

https://github.com/HiLab-git/WSL4MIS

공식 코드

Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision

https://github.com/Holmes-Alan/FunnyNet-W

공식 코드

FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild

https://github.com/Holmeswww/PPOGAN

공식 코드

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

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