Centroid-centered Modeling for Efficient Vision Transformer Pre-training
This work addresses the need for faster and simpler pre-training in computer vision, though it is incremental as it builds on existing masked image modeling paradigms.
The paper tackles the problem of efficient self-supervised pre-training for Vision Transformers by proposing a centroid-based approach that avoids supervised tokenizer training, achieving 84.4% top-1 accuracy on ImageNet-1K with ViT-B and 86.0% with ViT-L.
Masked Image Modeling (MIM) is a new self-supervised vision pre-training paradigm using a Vision Transformer (ViT). Previous works can be pixel-based or token-based, using original pixels or discrete visual tokens from parametric tokenizer models, respectively. Our proposed centroid-based approach, CCViT, leverages k-means clustering to obtain centroids for image modeling without supervised training of the tokenizer model, which only takes seconds to create. This non-parametric centroid tokenizer only takes seconds to create and is faster for token inference. The centroids can represent both patch pixels and index tokens with the property of local invariance. Specifically, we adopt patch masking and centroid replacing strategies to construct corrupted inputs, and two stacked encoder blocks to predict corrupted patch tokens and reconstruct original patch pixels. Experiments show that our CCViT achieves 84.4% top-1 accuracy on ImageNet-1K classification with ViT-B and 86.0% with ViT-L. We also transfer our pre-trained model to other downstream tasks. Our approach achieves competitive results with recent baselines without external supervision and distillation training from other models.