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DESCRIPTION:Image tokenizers map images to sequences of discrete tokens\, and are a crucial component of autoregressive transformer-based image generation. The tokens are typically associated with spatial locations in the input image\, arranged in raster scan order\, which is not ideal for autoregressive modeling. In this paper\, we propose to tokenize the image spectrum instead\, obtained from a discrete wavelet transform (DWT)\, such that the sequence of tokens represents the image in a coarse-to-fine fashion. Our tokenizer brings several advantages: 1) it leverages that natural images are more compressible at high frequencies\, 2) it can take and reconstruct images of different resolutions without retraining\, 3) it improves the conditioning for next-token prediction -- instead of conditioning on a partial line-by-line reconstruction of the image\, it takes a coarse reconstruction of the full image\, 4) it enables partial decoding where the first few generated tokens can reconstruct a coarse version of the image\, 5) it enables autoregressive models to be used for image upsampling. We evaluate the tokenizer reconstruction metrics as well as multiscale image generation\, text-guided image upsampling and editing.\n\n------\n\nPowered by addevent.com \nShare your next event with us!\n
X-ALT-DESC;FMTTYPE=text/html:<span data-sheets-root="1">Image tokenizers map images to sequences of discrete tokens, and are a crucial component of autoregressive transformer-based image generation. The tokens are typically associated with spatial locations in the input image, arranged in raster scan order, which is not ideal for autoregressive modeling. In this paper, we propose to tokenize the image spectrum instead, obtained from a discrete wavelet transform (DWT), such that the sequence of tokens represents the image in a coarse-to-fine fashion. Our tokenizer brings several advantages: 1) it leverages that natural images are more compressible at high frequencies, 2) it can take and reconstruct images of different resolutions without retraining, 3) it improves the conditioning for next-token prediction -- instead of conditioning on a partial line-by-line reconstruction of the image, it takes a coarse reconstruction of the full image, 4) it enables partial decoding where the first few generated tokens can reconstruct a coarse version of the image, 5) it enables autoregressive models to be used for image upsampling. We evaluate the tokenizer reconstruction metrics as well as multiscale image generation, text-guided image upsampling and editing.</span><br /><br />------<br /><br />Powered by addevent.com <br>Share your next event with us!<br>
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SUMMARY:[C4AI] Carlos Esteves - Spectral Image Tokenizer (Asia)
DTSTART;TZID=America/Los_Angeles:20250122T080000
DTEND;TZID=America/Los_Angeles:20250122T090000
DTSTAMP:20260803T220132Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:0
LOCATION:https://meet.google.com/yhv-tiir-ava?hs=122&authuser=0
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