  Transformer-based vision models typically tokenize images into fixed-size
square patches as input units, which lacks the adaptability to image content
and overlooks the inherent pixel grouping structure. Inspired by the subword
tokenization widely adopted in language models, we propose an image tokenizer
at a subobject level, where the subobjects are represented by semantically
meaningful image segments obtained by segmentation models (e.g., segment
anything models). To implement a learning system based on subobject
tokenization, we first introduced a Direct Segment Anything Model (DirectSAM)
that efficiently produces comprehensive segmentation of subobjects, then embed
subobjects into compact latent vectors and fed them into a large language model
for vision language learning. Empirical results demonstrated that our
subobject-level tokenization significantly facilitates efficient learning of
translating images into object and attribute descriptions compared to the
traditional patch-level tokenization. Codes and models are open-sourced at
https://github.com/ChenDelong1999/subobjects.
