Aggregating Nested Transformers

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Transformers in Vision: A Survey (2021, methodology): “, CCT [77], NesT [249]) can be trained from scratch even on”
CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention (2021, background): “The competitors are all competitive vision transformers, including DeiT (Touvron et al., 2021), PVT (Wang et al., 2021), T2T-ViT (Yuan et al., 2021), TNT (Han et al., 2021), CViT (Chen et al., 2021a), Twins (Chu et al., 2021), Swin (Liu et al., 2021b), NesT (Zhang et al., 2021b), CvT (Wu et al., 202”
Improved Transformer for High-Resolution GANs (2021, methodology): “We follow the decoder form of Nested Transformer [73] where in each stage, the input feature is first”
CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention (2021, background): “Similarly, other vision transformers (Chen et al., 2021a; Zhang et al., 2021b; Wu et al., 2021) also employ a divide-and-conquer method and approximate the vanilla self-attention at a lower cost.”
A Survey on Vision Transformer (2020, methodology): “transformer models [232], [148], [209], [61], [279], [167] including PVT [232], HVT [168], Swin Transformer [148] and PiT [92].”
Uformer: A General U-Shaped Transformer for Image Restoration (2021, background): “Rather than focusing on image classiﬁcation, recent works [10,13,22,67,82] propose a brunch of Transformer-based backbones for more general high-level vision tasks.”