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/leemathew1998/RG
https://github.com/leimao/DeepLab-V3
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/leimao/DeepLab_v3
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/leonardoaraujosantos/seg_atrous
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/levindabhi/cloth-segmentation
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/lewandofskee/MobileMamba
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/lhaof/Salient-Object-Detection-Papers
https://github.com/lhaof/fast-salient-object-detection
https://github.com/liminn/icnet-pytorch
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/lisilin013/ICNet-tensorflow-ros
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/ljllili23/unet_baseline
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/lrr-rachel/PCDUNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ltp1995/GCAGC-CVPR2020
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
https://github.com/ltracegeo/deep-mineralogical-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/lucidrains/imagen-pytorch
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/lurenhaothu/CWMI
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/lyffly/Segmentation_with_my_dataset
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/lyqcom/2D-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/lyqcom/icnet
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/manideep2510/eye-in-the-sky
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mariosfourn/ScienceBowl
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/massucattoj/unet-model
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/matanrein/Understanding-Clouds-from-Satellite-Images
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mateuszbuda/brain-segmentation-pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mathildor/DeepLab-v3
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/mattangus/fast-semantic-segmentation
https://github.com/mberkay0/pretrained-backbones-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mdt48/semantic-segmentation-pytorch
https://github.com/megvii-research/basecls/tree/main/zoo/public/hrnet
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/melissande/dhi-segmentation-buildings
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mfp0610/semantic-segmentaion
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/milesial/Pytorch-UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/mindspore-ai/models/blob/master/research/cv/mobilenetv3_large
https://github.com/mindspore-ai/models/tree/master/official/cv/deeplabv3
Rethinking Atrous Convolution for Semantic Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.