A More Word-like Image Tokenization for MLLMs
Two new research papers propose novel methods for tokenizing images to improve multimodal large language models (MLLMs). The first paper, VFMTok, uses a frozen vision foundation model as a tokenizer, achieving significant improvements in synthesis quality and token efficiency. The second paper, DiVT, clusters patch embeddings into semantic units, making visual tokens more compatible with LLMs and reducing memory costs and latency. AI
IMPACT Novel image tokenization techniques could lead to more efficient and capable multimodal AI systems.