Homogeneous tokenizer matters: Homogeneous visual tokenizer for remote sensing image understanding

On the basis of the transformer architecture and the pretext task of “next-token prediction”, multimodal large language models (MLLMs) are revolutionizing the paradigm in the field of remote sensing image understanding. However, the tokenizer, as one of the fundamental components of MLLMs, has long been overlooked or even misunderstood in visual tasks. A key factor contributing to the great comprehension power of large language models is that natural language tokenizers utilize meaningful words or subwords as the basic elements of language. In contrast, mainstream visual tokenizers, represented by patch-based methods such as Patch Embed, rely on meaningless rectangular patches as basic elements of vision. Analogous to words or subwords in language, we define semantically independent regions (SIRs) for vision and then propose two properties that an ideal visual tokenizer should possess: (1) homogeneity, where SIRs serve as the basic elements of vision, and (2) adaptivity, which allows for a flexible number of tokens to accommodate images of any size and tasks of any granularity. On this basis, we design a simple HOmogeneous visual tOKenizer: HOOK. HOOK consists of two modules: an object perception module (OPM) and an object vectorization module (OVM). To achieve homogeneity, the OPM splits the image into 4 × 4 pixel seeds and then uses a self-attention mechanism to identify SIRs. The OVM employs cross-attention to merge seeds within the same SIR. To achieve adaptability, the OVM predefines a variable number of learnable vectors as cross-attention queries, allowing for the adjustment of the token quantity. We conducted experiments on the NWPU-RESISC45, WHU-RS19, and NaSC-TG2 classification datasets for sparse tasks and the GID5 and DGLCC segmentation datasets for dense tasks. The results show that the visual tokens obtained by HOOK correspond to individual objects, thereby verifying their homogeneity. Compared with randomly initialized or pretrained Patch Embed, which required more than one hundred tokens per image, HOOK required only 6 and 8 tokens for sparse and dense tasks, respectively, resulting in performance improvements of 2% to 10% and efficiency improvements of 1.5 to 2.8 times. The homogeneity and adaptability of the proposed approach provide new perspectives for the study of visual tokenizers. Guided by these principles, the developed HOOK has the potential to replace traditional Patch Embed. The code is available at https://github.com/GeoX-Lab/Hook.