Closing the Modality Gap Aligns Group-Wise Semantics

• URL: http://arxiv.org/abs/2601.18525v1
• Date: Mon, 26 Jan 2026 14:36:04 GMT
• Title: Closing the Modality Gap Aligns Group-Wise Semantics
• Abstract summary: In multimodal learning, CLIP has been recognized as the textitde facto method for learning a shared latent space across multiple modalities.<n>Although CLIP-based losses effectively align modalities at the semantic level, the resulting latent spaces often remain only partially shared.<n>We introduce a novel method designed to consistently reduce this discrepancy in two-modal settings.
• Score: 16.991869322977077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In multimodal learning, CLIP has been recognized as the \textit{de facto} method for learning a shared latent space across multiple modalities, placing similar representations close to each other and moving them away from dissimilar ones. Although CLIP-based losses effectively align modalities at the semantic level, the resulting latent spaces often remain only partially shared, revealing a structural mismatch known as the modality gap. While the necessity of addressing this phenomenon remains debated, particularly given its limited impact on instance-wise tasks (e.g., retrieval), we prove that its influence is instead strongly pronounced in group-level tasks (e.g., clustering). To support this claim, we introduce a novel method designed to consistently reduce this discrepancy in two-modal settings, with a straightforward extension to the general $n$-modal case. Through our extensive evaluation, we demonstrate our novel insight: while reducing the gap provides only marginal or inconsistent improvements in traditional instance-wise tasks, it significantly enhances group-wise tasks. These findings may reshape our understanding of the modality gap, highlighting its key role in improving performance on tasks requiring semantic grouping.

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