CAT: Content-Adaptive Image Tokenization

Abstract

Abstract: 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 divers

Research Question

How does the on-demand expert allocation in AnyExperts affect inference throughput and FLOPs efficiency on multimodal reasoning tasks, compared to fixed top-k routing in MoE-VLMs?

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Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

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Provenance