Quantised Global Autoencoder: A Holistic Approach to Representing Visual Data
<|reference_start|>Quantised Global Autoencoder: A Holistic Approach to Representing Visual Data: In quantised autoencoders, images are usually split into local patches, each encoded by one token. This representation is redundant in the sense that the same number of tokens is spend per region, regardless of the visual information content in that region. Adaptive discretisation schemes like quadtrees are applied to allocate tokens for patches with varying sizes, but this just varies the region of influence for a token which nevertheless remains a local descriptor. Modern architectures add an attention mechanism to the autoencoder which infuses some degree of global information into the local tokens. Despite the global context, tokens are still associated with a local image region. In contrast, our method is inspired by spectral decompositions which transform an input signal into a superposition of global frequencies. Taking the data-driven perspective, we learn custom basis functions corresponding to the codebook entries in our VQ-VAE setup. Furthermore, a decoder combines these basis functions in a non-linear fashion, going beyond the simple linear superposition of spectral decompositions. We can achieve this global description with an efficient transpose operation between features and channels and demonstrate our performance on compression.<|reference_end|>
@article{elsner2024quantised,
    title={Quantised Global Autoencoder: A Holistic Approach to Representing Visual
  Data},
    author={Tim Elsner, Paula Usinger, Victor Czech, Gregor Kobsik, Yanjiang He,
  Isaak Lim, Leif Kobbelt},
    journal={arXiv preprint arXiv:2407.11913},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11913},
    primaryClass={cs.CV cs.LG}
}