Global and Local Entailment Learning for Natural World Imagery

2025-06-26

AI Summary

This paper addresses the challenge of modeling hierarchical semantic structures in vision-language models, particularly focusing on explicitly encoding and preserving transitive entailment relationships among concepts in biological taxonomies (e.g., the Tree of Life). We propose Radial Cross-Modal Embedding (RCME), the first framework to *optimizably* encode concept partial orders within a joint representation space: it employs radial geometric embedding to model directional entailment, integrated with cross-modal contrastive learning and a hierarchy-aware partial-order constraint loss, thereby ensuring consistent alignment between semantic order and geometric structure. RCME achieves significant improvements over state-of-the-art methods on hierarchical species classification and hierarchical image–text retrieval. Code and pretrained models are publicly available.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

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.

Problem

• Learning hierarchical data structure in vision-language models
• Modeling transitive nature of entailment explicitly
• Optimizing partial order of concepts in representations

Innovation

• Radial Cross-Modal Embeddings for entailment
• Explicit modeling of transitivity-enforced entailment
• Optimizes partial order of vision-language concepts