Papers with Code 아카이브
2D Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/houssemjebari/Fruit-Detection
https://github.com/hryniewska/lung-segmentation-on-x-rays
Lung Segmentation from Chest X-rays using Variational Data Imputation
https://github.com/huggingface/transformers
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/huizhang0110/AON
https://github.com/hvak/bi-michi
Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty
https://github.com/hvak/mimrf-bfm
Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty
https://github.com/hycis/TensorGraph
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/hydrogo/rainnet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/hyunjoonbok/Unet-pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/hz-ants/DA-RNN
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks
https://github.com/iNLyze/DeepLearning-SeGAN-Segmentation
SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
https://github.com/iacercalixto/butd-image-captioning
https://github.com/iarai/landslide4sense-2022
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/iarai/weather4cast
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ibaiGorordo/ONNX-SAM2-Segment-Anything
https://github.com/ibaiGorordo/Sapiens-Pytorch-Inference
https://github.com/iceli1007/ICIAR2018_BACH_Challenge-DA-Refinenet
DA-RefineNet:A Dual Input Whole Slide Image Segmentation Algorithm Based on Attention
https://github.com/idea-research/dab-detr
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
https://github.com/idea-research/dn-detr
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
https://github.com/idea-research/grounded-sam-2
https://github.com/idilesenzulfikar/UNOVOST
UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking
https://github.com/iduta/pyconvsegnet
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/ievred/JCPOT
Optimal Transport for Multi-source Domain Adaptation under Target Shift
https://github.com/ihomeava/PaddleSeg2
EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow
https://github.com/iibrahimli/unet_sem_seg
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ikigai-aa/Face-Mask-Detector-using-MobileNetV2
https://github.com/imagesegmentation2020/indoor
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/imagine5am/ARU-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/imatge-upc/refvos
RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation
https://github.com/inder9999/ISIC2017_skin_lesion_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/indu1ge/DepthMaster
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
https://github.com/infini8-13/MaskRCNN_tryout
https://github.com/insomnia94/ARG
Adaptive ROI Generation for Video Object Segmentation Using Reinforcement Learning
https://github.com/intelligent-vehicles/bevdriver
https://github.com/intsco/am-segmentation
TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.