"Principal Components" Enable A New Language of Images
Journal:
arXiv
Published Date:
Mar 11, 2025
Abstract
We introduce a novel visual tokenization framework that embeds a provable
PCA-like structure into the latent token space. While existing visual
tokenizers primarily optimize for reconstruction fidelity, they often neglect
the structural properties of the latent space -- a critical factor for both
interpretability and downstream tasks. Our method generates a 1D causal token
sequence for images, where each successive token contributes non-overlapping
information with mathematically guaranteed decreasing explained variance,
analogous to principal component analysis. This structural constraint ensures
the tokenizer extracts the most salient visual features first, with each
subsequent token adding diminishing yet complementary information.
Additionally, we identified and resolved a semantic-spectrum coupling effect
that causes the unwanted entanglement of high-level semantic content and
low-level spectral details in the tokens by leveraging a diffusion decoder.
Experiments demonstrate that our approach achieves state-of-the-art
reconstruction performance and enables better interpretability to align with
the human vision system. Moreover, auto-regressive models trained on our token
sequences achieve performance comparable to current state-of-the-art methods
while requiring fewer tokens for training and inference.
