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목록딥러닝 (53)
Attention please
![](http://i1.daumcdn.net/thumb/C150x150/?fname=https://blog.kakaocdn.net/dn/dpYMYz/btspomjANh7/x3xXJGwki1WmKbxM4ROl1K/img.png)
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..
![](http://i1.daumcdn.net/thumb/C150x150/?fname=https://blog.kakaocdn.net/dn/XTvrn/btsplaYCfG1/szUi95kI1iTIgTU4kSesw1/img.png)
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 ..
![](http://i1.daumcdn.net/thumb/C150x150/?fname=https://blog.kakaocdn.net/dn/cpPKc6/btspd3sVg9H/nFeEDvypkOfn8de2OeedC1/img.png)
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..
![](http://i1.daumcdn.net/thumb/C150x150/?fname=https://blog.kakaocdn.net/dn/cYgKHJ/btspflrOXjl/RIPfcekBqa1d7CgiV3yT3K/img.png)
이번에 리뷰할 논문은 Swin Transformer: Hierarchical Vision Transformer using Shifted Windows 입니다. https://paperswithcode.com/paper/swin-transformer-hierarchical-vision Papers with Code - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows #2 best model for Image Classification on OmniBenchmark (Average Top-1 Accuracy metric) paperswithcode.com 2017년도에 transformer 모델이 등장하면서 NLP 분야에서 큰 ..
![](http://i1.daumcdn.net/thumb/C150x150/?fname=https://blog.kakaocdn.net/dn/XOTXc/btsoRz5lEMW/v7k9VPMK0Uf40j7uRRfoV1/img.png)
이번에 리뷰할 논문은 AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE 입니다. https://paperswithcode.com/paper/an-image-is-worth-16x16-words-transformers-1 Papers with Code - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale 🏆 SOTA for Out-of-Distribution Generalization on ImageNet-W (IN-W Gap metric) paperswithcode.com ViT를 이해하기 위해서는 기본적으로 transformer에 대한 ..
![](http://i1.daumcdn.net/thumb/C150x150/?fname=https://blog.kakaocdn.net/dn/VA5sc/btsopv3kagE/umZnnM410WpLxzdhVDXw3k/img.png)
이번에 리뷰할 논문은 DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs 입니다. https://paperswithcode.com/paper/deeplab-semantic-image-segmentation-with-deep Papers with Code - DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CR #3 best model for Semantic Segmentation on Event-based Segmen..