"Principal Components" Enable A New Language of Images
This addresses the need for interpretable and efficient visual tokenization for downstream tasks in computer vision.
The paper tackles the problem of visual tokenization by introducing a framework that embeds a PCA-like structure into the latent token space, ensuring tokens capture non-overlapping information with decreasing explained variance. The method achieves state-of-the-art reconstruction performance and enables autoregressive models to match current SOTA methods while requiring fewer tokens for training and inference.
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, autoregressive models trained on our token sequences achieve performance comparable to current state-of-the-art methods while requiring fewer tokens for training and inference.