Dichotomous Image Segmentation
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Currently, existing image segmentation tasks mainly focus on segmenting objects with specific characteristics, e.g., salient, camouflaged, meticulous, or specific categories. Most of them have the same input/output formats, and barely use exclusive mechanisms designed for segmenting targets in their models, which means almost all tasks are dataset-dependent. Thus, it is very promising to formulate a category-agnostic DIS task for accurately segmenting objects with different structure complexities, regardless of their characteristics. Compared with semantic segmentation, the proposed DIS task usually focuses on images with single or a few targets, from which getting richer accurate details of each target is more feasible.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/condiff-dataset/ConDiff
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/cosmic-cortex/pytorch-UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/cyrilminaeff/MobileNet
https://github.com/czarmanu/sentinel_lakeice
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/d-li14/mobilenetv3.pytorch
https://github.com/d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/daheyinyin/ICNet
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/dajes/DensePose-TorchScript
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/danielenricocahall/Keras-UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/daveboat/spp
https://github.com/daxiaHuang/brainstorm
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/dbailey00/Semantic-Segmentation-With-Seismic-Images
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/decentNick/Signals_big_lab_SPBSTU
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/dengpingfan/fsgan
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/devanshpratapsingh/U-net_for_Multiclass_Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/diasirish/mobilenetv3
https://github.com/divamgupta/image-segmentation-keras
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/dmlc/gluon-cv
https://github.com/dry01/segmentation-
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/dsandeep0138/UNet-Colorization
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/e9190712/vessel_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ekzhang/fastseg
https://github.com/emilianavt/OpenSeeFace
https://github.com/ensta-u2is/deeplabv3plus-muad-pytorch
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/eshaanagarwal/hr-net-implementation
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/ethanhou99/ml_learn
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/fenglian425/Agriculture_AI
https://github.com/fepegar/unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/filippogiruzzi/semantic_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/fregu856/deeplabv3
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/frgfm/Holocron
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/galprz/brain-tumor-segemntation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/galprz/brain-tumor-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ganeshpawar95/removebg1
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/geekswaroop/Human-Parsing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.