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CAT: Content-Adaptive Image Tokenization

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arxiv 2501.03120 v1 pith:RUCVRZSJ submitted 2025-01-06 cs.CV

Junhong Shen , Kushal Tirumala , Michihiro Yasunaga , Ishan Misra , Luke Zettlemoyer , Lili Yu , Chunting Zhou This is my paper · ORCID
classification cs.CV
keywords imageimagescomplexitycompressioncontentcontent-adaptivetokenstrained
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Most existing image tokenizers encode images into a fixed number of tokens or patches, overlooking the inherent variability in image complexity. To address this, we introduce Content-Adaptive Tokenizer (CAT), which dynamically adjusts representation capacity based on the image content and encodes simpler images into fewer tokens. We design a caption-based evaluation system that leverages large language models (LLMs) to predict content complexity and determine the optimal compression ratio for a given image, taking into account factors critical to human perception. Trained on images with diverse compression ratios, CAT demonstrates robust performance in image reconstruction. We also utilize its variable-length latent representations to train Diffusion Transformers (DiTs) for ImageNet generation. By optimizing token allocation, CAT improves the FID score over fixed-ratio baselines trained with the same flops and boosts the inference throughput by 18.5%.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Single-pass Adaptive Image Tokenization for Minimum Program Search

    cs.CV 2025-07 conditional novelty 7.0 of 10

    KARL conditions a tokenizer on a target reconstruction loss and learns halting probabilities that produce an adaptive token count in a single forward pass.

  2. ELT: Elastic Looped Transformers for Visual Generation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    Weight-shared looped transformers trained with intra-loop self-distillation match MaskGIT-class FID/FVD at roughly 4x fewer parameters and support any-time inference across loop counts.

  3. Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Scaling the number of interaction steps, trained via a curriculum over rollout horizon, improves web-agent task success and outperforms scaling per-step reasoning under fixed token budgets.

  4. Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with Modality-Aware Sparsity

    cs.LG 2025-01 conditional novelty 5.0 of 10

    Modality-specific projection weights let a Mamba model match dense multimodal baselines at the same loss using 25% to 65% of the training compute.

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