Review — Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
RepLKNet: Using Up To 31×31 Large Kernel Convolutions —  Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs,
RepLKNet, by Tsinghua University, MEGVII Technology, and Aberystwyth University,
2022 CVPR, Over 130 Citations (Sik-Ho Tsang @ Medium)
Image Classification, Vision Transformer, ViT, Swin Transformer 1.1. Image Classification
1989 … 2022 [ConvNeXt] [PVTv2] [ViT-G] [AS-MLP] [ResTv2] [CSWin Transformer] [Pale…
Review — Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs RepLKNet: Using Up To 31×31 Large Kernel Convolutions —  Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs,
RepLKNet, by Tsinghua University, MEGVII Technology, and Aberystwyth University,
2022 CVPR, Over 130 Citations (Sik-Ho Tsang @ Medium)
Image Classification, Vision Transformer, ViT, Swin Transformer 1.1. Image Classification
1989 … 2022 [ConvNeXt] [PVTv2] [ViT-G] [AS-MLP] [ResTv2] [CSWin Transformer] [Pale… deep-learning
Review — Scaling Kernels 31x31 Revisiting Large Kernel Design CNNs RepLKNet Using 31×31 Large Kernel Convolutions — Scaling Kernels 31x31 Revisiting Large Kernel Design CNNs RepLKNet Tsinghua University MEGVII Technology Aberystwyth University 2022 CVPR 130 Citations SikHo Tsang Medium Image Classification Vision Transformer ViT Swin Transformer 11 Image Classification 1989 … 2022 ConvNeXt PVTv2 ViTG ASMLP ResTv2 CSWin Transformer Pale… deeplearning