Adaptive Length Image Tokenization via Recurrent Allocation

Current vision systems typically assign fixed-length representations to images, regardless of the information content. This contrasts with human intelligence --and even large language models--which allocate varying representational capacities based on entropy, context and familiarity. Inspired by this, we propose an approach to learn variable-length token representations for 2D images. Our encoder-decoder architecture recursively processes 2D image tokens, distilling them into 1D latent tokens over multiple iterations of recurrent rollouts. Each iteration refines the 2D tokens, updates the existing 1D latent tokens, and adaptively increases representational capacity by adding new tokens. This enables compression of images into a variable number of tokens, ranging from 32 to 256. Recurrent token processing with increasing representational capacity in each iteration shows signs of token specialization, revealing potential for object / part discovery. Representation learning (Bengio et al., 2013), which involves extracting meaningful and useful information from input observations, is crucial for decision-making. An effective representation should be compact while encoding all relevant information. However, what constitutes "relevant" information varies based on the specific task; for example, a coarse classification task may require a different latent representation compression factor for satisfactory performance compared to a task demanding perfect pixel-level reconstruction, which necessitates denser representations. Similarly, language models can describe content at various levels of abstraction depending on complexity, context (Graves, 2016; Dehghani et al., 2018), and familiarity (Baevski & Auli, 2018). In contrast, most current visual systems, such as VAEs, VQGANs, and ViTs (Kingma & Welling, 2022; Esser et al., 2020; Dosovitskiy et al., 2020), generate fixed-size representations for all images. In this work, we take a step toward learning adaptive and variable-length visual representations, emphasizing that each image requires a different representation capacity (see Sec. 4).