Hyperbolic Image-Text Representations (2304.09172v3)

Abstract: Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual concept "dog" entails all images that contain dogs. Despite being intuitive, current large-scale vision and LLMs such as CLIP do not explicitly capture such hierarchy. We propose MERU, a contrastive model that yields hyperbolic representations of images and text. Hyperbolic spaces have suitable geometric properties to embed tree-like data, so MERU can better capture the underlying hierarchy in image-text datasets. Our results show that MERU learns a highly interpretable and structured representation space while being competitive with CLIP's performance on standard multi-modal tasks like image classification and image-text retrieval. Our code and models are available at https://www.github.com/facebookresearch/meru

• The paper presents MERU, a novel contrastive model that leverages hyperbolic geometry to capture semantic hierarchies in image-text data.
• MERU achieves competitive results in image classification and retrieval tasks, rivaling traditional Euclidean models like CLIP.
• The study demonstrates that hyperbolic embeddings use space efficiently and incorporate entailment loss to enhance multimodal representation.

Hyperbolic Image-Text Representations

The paper "Hyperbolic Image-Text Representations" presents a novel approach to improving the representation of visual and linguistic data by employing hyperbolic geometry, which is adept at embedding hierarchical information. The work introduces a model called MERU, designed to construct hyperbolic embeddings for image-text datasets, thereby capturing the inherent semantic hierarchy that often exists in such multimodal datasets.

Core Contributions

1. Introduction of Hyperbolic Representations: The authors propose MERU, a contrastive model that learns hyperbolic representations using hyperbolic spaces with a focus on the Lorentz model. These spaces naturally suit the embedding of hierarchical data, such as the rich semantic relationships found in image-text pairs.
2. Competitive Performance: While embedding data in a hyperbolic space, MERU achieves performance competitive with that of prior Euclidean-based models like CLIP, especially in image classification and image-text retrieval tasks. This demonstrates that hyperbolic representations are not only theoretically appealing but also practically effective.
3. Learning and Structural Insights: MERU incorporates an entailment loss which helps in structuring the representation space by imposing a partial order between text and images—encouraging text representations to be more generic and image representations to be more specific.
4. Adaptation to Resource Constraints: Hyperbolic embeddings are shown to use space more efficiently, which could allow for reduced embedding dimensionality without significant loss of representational power, particularly useful in resource-constrained deployments.

Experimental Evidence

The evaluation conducted includes zero-shot image classification and text retrieval across several datasets, demonstrating the model's strong performance compared to established baselines. MERU displays advantage in retrieval tasks due to the structured nature of hyperbolic spaces, enabling better capture of relationships inherent in the data.

Theoretical and Practical Implications

• Theoretical: The use of hyperbolic spaces presents an advantageous framework for embedding hierarchical structures, yielding insights into the potential for geometric approaches to improve representation learning in AI.
• Practical: By optimizing space usage, hyperbolic embeddings pave the way for more efficient algorithms that can operate under hardware constraints typical in real-world applications.

Future Directions

The exploration of hyperbolic geometry in large-scale datasets highlights several avenues for future research:

• Further refinement of hyperbolic models to fully leverage their capacity for capturing detailed semantic hierarchies.
• Investigating the applications of hyperbolic representations in more complex multimodal scenarios beyond image-text tasks.
• Enhanced understanding of the interplay between hyperbolic space structure, dataset size, and representational quality.

Conclusion

This research offers a promising shift towards the use of Riemannian manifolds, specifically hyperbolic geometries, providing substantial evidence that they offer competitive advantages over traditional Euclidean embeddings in multimodal contexts. As computational resources continue to be a critical factor, exploring these efficient and rich representational spaces offers a valuable path forward for both theoretical inquiry and practical implementation in AI systems.