Communication-Inspired Tokenization for Structured Image Representations

TL;DR

This paper introduces COMiT, a communication-inspired tokenization framework that creates structured, object-centric image representations through iterative, sequential encoding, enhancing interpretability and reasoning in vision models.

Contribution

The paper proposes a novel, communication-inspired tokenization method that constructs structured, object-centric image representations via iterative encoding within a transformer architecture.

Findings

Improves object-centric interpretability of image tokens.

Enhances compositional generalization and relational reasoning.

Outperforms prior methods in structured visual representation tasks.

Abstract

Discrete image tokenizers have emerged as a key component of modern vision and multimodal systems, providing a sequential interface for transformer-based architectures. However, most existing approaches remain primarily optimized for reconstruction and compression, often yielding tokens that capture local texture rather than object-level semantic structure. Inspired by the incremental and compositional nature of human communication, we introduce COMmunication inspired Tokenization (COMiT), a framework for learning structured discrete visual token sequences. COMiT constructs a latent message within a fixed token budget by iteratively observing localized image crops and recurrently updating its discrete representation. At each step, the model integrates new visual information while refining and reorganizing the existing token sequence. After several encoding iterations, the final message conditions a flow-matching decoder that reconstructs the full image. Both encoding and decoding are implemented within a single transformer model and trained end-to-end using a combination of flow-matching reconstruction and semantic representation alignment losses. Our experiments demonstrate that while semantic alignment provides grounding, attentive sequential tokenization is critical for inducing interpretable, object-centric token structure and substantially improving compositional generalization and relational reasoning over prior methods.