Global and Local Entailment Learning for Natural World Imagery
Authors: name: Srikumar Sastry, name: Aayush Dhakal, name: Eric Xing, name: Subash Khanal, name: Nathan Jacobs
Organization: Multimodal Vision Research Laboratory @ WashU

Learning the hierarchical structure of data in vision-language models is a
significant challenge. Previous works have attempted to address this challenge
by employing entailment learning. However, these approaches fail to model the
transitive nature of entailment explicitly, which establishes the relationship
between order and semantics within a representation space. In this work, we
introduce Radial Cross-Modal Embeddings (RCME), a framework that enables the
explicit modeling of transitivity-enforced entailment. Our proposed framework
optimizes for the partial order of concepts within vision-language models. By
leveraging our framework, we develop a hierarchical vision-language foundation
model capable of representing the hierarchy in the Tree of Life. Our
experiments on hierarchical species classification and hierarchical retrieval
tasks demonstrate the enhanced performance of our models compared to the
existing state-of-the-art models. Our code and models are open-sourced at
https://vishu26.github.io/RCME/index.html.