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/xiey1/Nucleus_detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/xieyufei1993/FOTS

FOTS: Fast Oriented Text Spotting with a Unified Network

https://github.com/xilin1991/ClusterNet

Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering

https://github.com/ximinng/SVGDreamer

SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG Generation

https://github.com/xinghaochen/tinysam

Segment Anything

https://github.com/xiuyu0000/new_papers_codes/tree/main/OSVOS

One-Shot Video Object Segmentation

https://github.com/xiuyu0000/papers_with_examples/blob/main/MobileNet/mobilenetv2.ipynb

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/xiuyu0000/papers_with_examples/tree/main/deeplabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/xiuyu0000/papers_with_examples/tree/main/maskrcnn

Mask R-CNN

https://github.com/xiuyu0000/tutorials/tree/main/mobilenetv2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/backbones/mobilenet_v2.py

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/xjnpark/ds

Mask R-CNN

https://github.com/xkhoanamx1/lung_crop_xray

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/xmed-lab/cardiacnet

GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation

https://github.com/xmed-lab/dfg

Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation

https://github.com/xmed-lab/m3-uda

GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation

https://github.com/xmed-lab/mssg

A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation

https://github.com/xmengli999/H-DenseUNet

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes

https://github.com/xmy0916/pytorch_crnn

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

https://github.com/xr0927/chapter5-learning_CSRNet

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

https://github.com/xuebinqin/BASNet

Boundary-Aware Segmentation Network for Mobile and Web Applications

https://github.com/xujxyang/opentrans

Global Knowledge Calibration for Fast Open-Vocabulary Segmentation

https://github.com/xunhen/py-faster-rcnn-wjc

Mask R-CNN

https://github.com/xusongpei/crnn-ctc

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

https://github.com/xxradon/IGCV3-pytorch

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/xy-guo/Learning-Monocular-Depth-by-Stereo

Learning Monocular Depth by Distilling Cross-domain Stereo Networks

https://github.com/xywei/threshseg

An efficient iterative thresholding method for image segmentation

https://github.com/xzabg/faster-rcnn-with-Caltech

Mask R-CNN

https://github.com/y-ouali/pytorch_segmentation

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/ya54/SolarUnet-magnetic-tracking

Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning

https://github.com/yakhyo/gaze-estimation

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/yakhyo/head-pose-estimation

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/yakhyo/retinaface-pytorch

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/yangchris11/samurai

SAM 2: Segment Anything in Images and Videos

https://github.com/yangxuntu/vrd

Visual Translation Embedding Network for Visual Relation Detection

https://github.com/yangysc/resinet

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

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