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목록머신러닝 (50)
Attention please
이번에 리뷰할 논문은 A Simple Framework for Contrastive Learning of Visual Representations 입니다. https://paperswithcode.com/paper/a-simple-framework-for-contrastive-learning Papers with Code - A Simple Framework for Contrastive Learning of Visual Representations #4 best model for Contrastive Learning on imagenet-1k (ImageNet Top-1 Accuracy metric) paperswithcode.com 일반적으로 딥러닝 모델은 라벨링된 데이터에 의해 학습을 진행하며, 이를..
이번에 리뷰할 논문은 ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video 입니다. https://paperswithcode.com/paper/esfpnet-efficient-deep-learning-architecture Papers with Code - ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchosc #2 best model for Medical Image Segmentation on ETIS-L..
이번에 리뷰할 논문은 FCN-Transformer Feature Fusion for Polyp Segmentation 입니다. https://paperswithcode.com/paper/fcn-transformer-feature-fusion-for-polyp Papers with Code - FCN-Transformer Feature Fusion for Polyp Segmentation #5 best model for Medical Image Segmentation on Kvasir-SEG (mean Dice metric) paperswithcode.com 본 논문에서 제안하는 FCBFormer모델은 대장 내시경(Colonoscopy) 영상의 대장암(colorectal cancer) 부분의 영역을 검출하..
이번에 리뷰할 논문은 PVT v2: Improved Baselines with Pyramid Vision Transformer 입니다. https://paperswithcode.com/paper/pvtv2-improved-baselines-with-pyramid-vision Papers with Code - PVT v2: Improved Baselines with Pyramid Vision Transformer #24 best model for Object Detection on COCO-O (Average mAP metric) paperswithcode.com self-attention을 기반으로 하는 transformer를 computer vision에 적용하려는 연구들이 이어졌으며, 가장 대표적으로..
이번에 리뷰할 논문은 Pyramid Vision Transformer 입니다. https://paperswithcode.com/method/pvt Papers with Code - PVT Explained PVT, or Pyramid Vision Transformer, is a type of vision transformer that utilizes a pyramid structure to make it an effective backbone for dense prediction tasks. Specifically it allows for more fine-grained inputs (4 x 4 pixels per patch) to be used, whil paperswithcode.com CNN 기반 ..
이번에 리뷰할 논문은 SSformer: A Lightweight Transformer for Semantic Segmentation 입니다. https://paperswithcode.com/paper/ssformer-a-lightweight-transformer-for Papers with Code - SSformer: A Lightweight Transformer for Semantic Segmentation Implemented in one code library. paperswithcode.com 2017년도에 NLP분야에서 transformer 모델이 출시된 이후 많은 변화가 있었습니다. computer vision 역시 마찬가지였으며, 자연어에 특화된 transformer를 변형하여 CV에서 사..