ConvMAE: Masked Convolution Meets Masked Autoencoders. (arXiv:2205.03892v2 [cs.CV] UPDATED)

Vision Transformers (ViT) become widely-adopted architectures for various
vision tasks. Masked auto-encoding for feature pretraining and multi-scale
hybrid convolution-transformer architectures can further unleash the potentials
of ViT, leading to state-of-the-art performances on image classification,
detection and semantic segmentation. In this paper, our ConvMAE framework
demonstrates that multi-scale hybrid convolution-transformer can learn more
discriminative representations via the mask auto-encoding scheme. However,
directly using the original masking strategy leads to the heavy computational
cost and pretraining-finetuning discrepancy. To tackle the issue, we …

arxiv convolution cv