From Modalities to Propositions: A Language-Centric Framework for Multimodal Intelligence
Nadine Chang, Maying Shen, Shizhe Diao, Jialiang Wang, Jingde Chen, Thomas Breuel, Pavlo Molchanov, Rafid Mahmood, Jose M. Alvarez
Why It Matters
What makes this one worth your time
This framework could improve the interpretability and compositionality of multimodal systems, which is crucial for applications like autonomous driving and complex data retrieval.
A framework for expressing multimodal data as atomic propositions to unify semantic understanding.
Summary
The paper proposes a language representation framework for multimodal data that expresses observations as atomic propositions, creating a unified semantic space for different modalities like images, videos, and text. This framework aims to enhance interpretability, reasoning, cross-modal understanding, and retrieval, demonstrated in the context of autonomous driving and open-world data.
Key contributions
- Proposes a language-centric framework for multimodal data representation.
- Introduces a global semantic codebook for unifying modalities.
- Demonstrates the framework in autonomous driving and open-world data scenarios.
Notable insights
- The use of a global semantic codebook to unify different modalities into a shared vocabulary.
- Expressing observations as atomic propositions to enhance interpretability and reasoning.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.16560v1 Announce Type: new Abstract: We propose a language representation for multimodal data in which any observation, whether image, video, or text, is expressed as a bag of atomic propositions, simple statements about the entities, actions, and relations in a scene. A global semantic codebook unifies these into a shared vocabulary of canonical atomic propositions, placing every modality and observation into one interpretable space that spans fine grained facts to high level concepts and composes into richer ones. This brings interpretability with reasoning, cross-modal understanding and retrieval, and compositionality that enables complex multimodal understanding, rich data curation and complex structured retrieval. We demonstrate the framework on autonomous driving and open-world data.