Visual Tokenizer

Visual tokenizers are methods for breaking down images into discrete, meaningful units ("tokens") analogous to words in natural language processing, enabling large language models to process and understand visual information. Current research focuses on improving the semantic richness and efficiency of these tokenizers, often employing vector quantization and incorporating contextual information from other modalities like segmentation or language. This work aims to bridge the gap between image understanding and language processing, leading to advancements in multimodal models for applications such as image captioning, object detection, and medical image analysis. The development of effective visual tokenizers is crucial for building more powerful and versatile large language models capable of handling both textual and visual data.