https://github.com/Jo-dsa/SemanticSeg
U-Net: Convolutional Networks for Biomedical Image Segmentation
Papers with Code 아카이브
RGB Salient Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 13
- 보존된 평가 기록
- 159
RGB Salient object detection is a task-based on a visual attention mechanism, in which algorithms aim to explore objects or regions more attentive than the surrounding areas on the scene or RGB images.
<span style="color:grey; opacity: 0.6">( Image credit: Attentive Feedback Network for Boundary-Aware Salient Object Detection )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JoelPendleton/Edge-Detection-CNN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JoelPendleton/Object-Contour-Detection-CNN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/JosephPB/XNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/K-Mike/Automatic-salt-deposits-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/KPMG-wiseuniv/AI
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/KUR-creative/SickZil-Machine
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/KarthikDulam/U-NetCNN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/KeyG518/Unet_Cell_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Kinpzz/RCRNet-Pytorch
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels
https://github.com/Kissonis/Quantum_test
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/LeeJunHyun/Image_Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Li-Chongyi/cmMS-ECCV20
RGB-D Salient Object Detection with Cross-Modality Modulation and Selection
https://github.com/LilyHu/image_segmentation_chromosomes
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Lkruitwagen/remote-sensing-solar-pv
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/LuanAdemi/VisualGo
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/LucaHermes/graph-unet-traffic-prediction
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Lxrd-AJ/Advanced_ML
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/res2net
https://github.com/MS-Mind/MS-Code-06/tree/main/PSPNet
https://github.com/MS-Mind/MS-Code-08/tree/main/mobilenetV3_small_x1_0
https://github.com/MS-Mind/MS-Code-08/tree/main/mobilenetv3_large
https://github.com/MargeryLab/Pytorch-UNet-copy
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/MatthewQi2002/Unet_mindspore
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Maveric4/pr19aaw01
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/MdAli1993/Multiclass_Semantic_Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Media-Smart/vedaseg
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Miltos-90/UNet_Biomedical_Image_Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Mind23-2/MindCode-101/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Mind23-2/MindCode-101/tree/main/mobilenetV3_small_x1_0
https://github.com/Mind23-2/MindCode-101/tree/main/mobilenetv3_large
https://github.com/Mind23-2/MindCode-102
https://github.com/Mind23-2/MindCode-114
https://github.com/Mind23-2/MindCode-3/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Mind23-2/MindCode-3/tree/main/ICNet
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/Mind23-2/MindCode-3/tree/main/mobilenetv3_large
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.