Topological Alignment of Shared Vision-Language Embedding Space

Junwon You; Dasol Kang; Jae-Hun Jung

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arXiv · 2510.10889

Topological Alignment of Shared Vision-Language Embedding Space

Junwon You·Dasol Kang·Jae-Hun Jung

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Abstract

Contrastive Vision-Language Models (VLMs) have demonstrated strong zero-shot capabilities. However, their cross-modal alignment remains biased toward English due to limited multilingual multimodal data. Recent multilingual extensions have alleviated this gap but enforce instance-level alignment while neglecting the global geometry of the shared embedding space. We address this problem by introducing ToMCLIP (Topological Alignment for Multilingual CLIP), a topology-aware framework aligning embedding spaces with topology-preserving constraints. The proposed method applies persistent homology to define a topological alignment loss and approximates persistence diagram with theoretical error bounds using graph sparsification strategy. This work validates the proposed approach, showing enhanced structural coherence of multilingual representations, higher zero-shot accuracy on the CIFAR-100, and stronger multilingual retrieval performance on the xFlickr&CO. Beyond VLMs, the proposed approach provides a general method for incorporating topological alignment into representation learning. Code is available at https://github.com/junwon0/ToMCLIP.git.

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Junwon You, Dasol Kang, Jae-Hun Jung. 2025-10-13. Topological Alignment of Shared Vision-Language Embedding Space. https://arxiv.org/abs/2510.10889

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