Papers with Code 아카이브
RGB Salient Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/irenazra/melanoma_segmentation_Unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/irfanICMLL/structure_knowledge_distillation
https://github.com/it6aidl/outdoorsegmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ivboh/3d_image_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jakeret/tf_unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jakeret/unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jamiljami/Unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/javiergarciamolina/selfie-background-removal
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jbardelli/Petrographic-Image-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jellevankerk/Team-Challenge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jerichooconnell/tf_unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jerry73204/mobilenet-v3-rs
https://github.com/jiandai/mlTst
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jiaruixu/st-cgan
https://github.com/jiean001/models_m/tree/main/EGnet
https://github.com/jiwei0921/CoNet
Accurate RGB-D Salient Object Detection via Collaborative Learning
https://github.com/jiwei0921/Saliency-Evaluation-Toolbox
https://github.com/jiye-ML/unet_pytorch
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jkhadley/capstone-project
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jmjeon94/MobileNet-Pytorch
https://github.com/john-mnz/ComfyUI-Inspyrenet-Rembg
Revisiting Image Pyramid Structure for High Resolution Salient Object Detection
https://github.com/johschmidt42/pytorch-2d-3d-unet-tutorial
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jonryf/deep-learning-image-colorization-using-gan
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jovenwayfarer/pneumonia
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jqueguiner/camembert-as-a-service
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jqueguiner/image-segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jtiger958/pytorch-computer-vision-basic
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jtiger958/pytorch-computer-vision-tutorial
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/julia-bush/unet_TGS
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/justld/BisNetV1_paddle
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
https://github.com/jvanvugt/pytorch-unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/jxr326/swinmcnet
RGBT Salient Object Detection: A Large-scale Dataset and Benchmark
https://github.com/kaichoulyc/course_segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/kaland313/vitmav45-ShipSeakers
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/kannyjyk/Nested-UNet
U-Net: Convolutional Networks for Biomedical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.