  Multimodal Large Language Models (MLLMs) have demonstrated exceptional
capabilities in processing vision-language tasks. One of the crux of MLLMs lies
in vision tokenization, which involves efficiently transforming input visual
signals into feature representations that are most beneficial for LLMs.
However, existing vision tokenizers, essential for semantic alignment between
vision and language, remain problematic. Existing methods aggressively fragment
visual input, corrupting the visual semantic integrity. To address this, this
paper proposes a novel dynamic Semantic-Equivalent Vision Tokenizer (SeTok),
which groups visual features into semantic units via a dynamic clustering
algorithm, flexibly determining the number of tokens based on image complexity.
The resulting vision tokens effectively preserve semantic integrity and capture
both low-frequency and high-frequency visual features. The proposed MLLM
(Setokim) equipped with SeTok significantly demonstrates superior performance
across various tasks, as evidenced by our experimental results. The project
page is at https://chocowu.github.io/SeTok-web/.
