Topological Perspectives on Optimal Multimodal Embedding Spaces

TL;DR

This paper uses topological data analysis to compare the embedding spaces of CLIP and CLOOB, revealing insights into their structure, modality gaps, and impact on downstream tasks in multimodal models.

Contribution

It introduces a topological analysis framework to understand differences between CLIP and CLOOB embedding spaces, highlighting their structural distinctions and effects on performance.

Findings

Topological analysis uncovers modality gap drivers.

Clustering structures vary across dimensions.

Dimension collapse influences embedding space quality.

Abstract

Recent strides in multimodal model development have ignited a paradigm shift in the realm of text-to-image generation. Among these advancements, CLIP stands out as a remarkable achievement which is a sophisticated autoencoder adept at encoding both textual and visual information within a unified latent space. This paper delves into a comparative analysis between CLIP and its recent counterpart, CLOOB. To unravel the intricate distinctions within the embedding spaces crafted by these models, we employ topological data analysis. Our approach encompasses a comprehensive examination of the modality gap drivers, the clustering structures existing across both high and low dimensions, and the pivotal role that dimension collapse plays in shaping their respective embedding spaces. Empirical experiments substantiate the implications of our analyses on downstream performance across various contextual scenarios. Through this investigation, we aim to shed light on the nuanced intricacies that underlie the comparative efficacy of CLIP and CLOOB, offering insights into their respective strengths and weaknesses, and providing a foundation for further refinement and advancement in multimodal model research.