Topological Alignment of Shared Vision-Language Embedding Space

• URL: http://arxiv.org/abs/2510.10889v1
• Date: Mon, 13 Oct 2025 01:36:38 GMT
• Title: Topological Alignment of Shared Vision-Language Embedding Space
• Authors: Junwon You, Dasol Kang, Jae-Hun Jung,
• Abstract summary: ToMCLIP is a topology-aware framework aligning embedding spaces with topology-preserving constraints.<n>We show enhanced structural coherence of multilingual representations, higher zero-shot accuracy on the CIFAR-100, and stronger multilingual retrieval performance on the xFlickr&CO.
• Score: 5.5522557994489246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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