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2D Semantic Segmentation task 3 (25 classes)
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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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Royalvice/DocDiff
공식 코드https://github.com/dali92002/DE-GAN
공식 코드DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
https://github.com/dali92002/SSL-OCR
공식 코드https://github.com/ispamm/naf-dpm
공식 코드NAF-DPM: A Nonlinear Activation-Free Diffusion Probabilistic Model for Document Enhancement
https://github.com/microsoft/computervision-recipes/tree/master/contrib/document_cleanup/light_weight_document_cleanup_ICDAR2021
공식 코드https://github.com/rvimlab/miccai2021_cataract_semantic_segmentation
공식 코드Effective semantic segmentation in Cataract Surgery: What matters most?
https://github.com/ispamm/img2img-sc
NAF-DPM: A Nonlinear Activation-Free Diffusion Probabilistic Model for Document Enhancement
https://github.com/manjitullal/DE-GAN
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
https://github.com/sreenivas88/LP-IOANet
LP-IOANet: Efficient High Resolution Document Shadow Removal
https://github.com/vashiegaran/DE-GAN-Implementation-Using-PyTorch
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
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