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/oliverquintana/UnetSegmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/oliverz17/Unet_Keras
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ooooverflow/BiSeNet
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
https://github.com/open-edge-platform/geti
https://github.com/open-edge-platform/training_extensions
https://github.com/open-mmlab/mmclassification
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmpose
https://github.com/open-mmlab/mmsegmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/orobix/retina-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/osilab-kaist/komet-benchmark-dataset
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/osmr/imgclsmob
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/overshiki/unet-pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/p-mc-grath/DMMFODS
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/parachutel/deeplabv3plus_on_Mapillary_Vistas
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/paraficial/vae_pancreas_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/park-cheol/ASR-DCUnet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/park-cheol/Speech_Enhancement-DCUnet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/park-cheol/Speech_Enhancement-DCUnet_With_Transformer
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/patharanordev/background-remover
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/pdoublerainbow/bisenet-tensorflow
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
https://github.com/pengboxiangshang/mobilenetv3_pytorch
https://github.com/perslev/MultiPlanarUNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/petko-nikolov/pysemseg
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/pideyi1025/DeepLabV3Plus-RailSem19
https://github.com/pietz/unet-keras
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/pikabite/segmentations_tf2
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/pirate0109/UNet_in_Pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/plesqui/4d-cbct
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/pmcgrath249/DMMFODS
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/pmcgrath249/DeepCVLab
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/pmcgrath249/DeepCV_Lab
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/pmcgrath249/UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/pooruss/ICNet-Paddle2.2.0rc
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/pranjalrai-iitd/Fetal-head-segmentation-and-circumference-measurement-from-ultrasound-images
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.