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/tashchowdhury/FloodNet-Supervised_v1.0
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/tatigabru/kaggle-lyft
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/tawald/nnunet
Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?
https://github.com/team-data-science/azure-data-engineering
https://github.com/tejaswigowda/semseg-pytorch
https://github.com/tencentyouturesearch/personretrieval-ivt
https://github.com/tensorflow/models/tree/master/official/vision
https://github.com/tensorflow/models/tree/master/research/deeplab
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
https://github.com/tensorflow/models/tree/master/research/feelvos
FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
https://github.com/tensorflow/models/tree/master/research/object_detection
https://github.com/tensorflow/models/tree/master/research/slim
https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN
https://github.com/ternaus/TernausNetV2
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ternaus/angiodysplasia-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/tfboys-lzz/fobs
https://github.com/the-veeraraghavan-lab/smit
Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)
https://github.com/theia-4869/semantic-poss-api
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
https://github.com/theodorakontogianni/interobject3d
https://github.com/theostoican/Guided-Research/blob/main/finetuning_odin_resnet.ipynb
https://github.com/theziqi/dccc
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/thimabru1010/ResUnet-a_multitasking
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data
https://github.com/thuml/Transfer-Learning-Library
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/thusiyuan/holistic_scene_parsing
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image
https://github.com/tiangexiang/BiX-NAS
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation
https://github.com/tianrun-chen/sam-adapter-pytorch
https://github.com/tianyuan168326/videosemanticcompression-pytorch
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
https://github.com/tiendatnguyen-vision/UNet-Image-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/tileb1/adasim
CrOC: Cross-View Online Clustering for Dense Visual Representation Learning
https://github.com/timmeinhardt/trackformer
https://github.com/timy90022/VEGAN
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects
https://github.com/timy90022/WEGAN
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects
https://github.com/titu1994/MobileNetworks
https://github.com/tkuanlun350/Tensorflow-SegNet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/tobiasvanderwerff/MetaHTR
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.