On the modality gap and the contrastive loss in multi-modal representation learning

• URL: http://arxiv.org/abs/2607.10698v1
• Date: Sun, 12 Jul 2026 10:41:52 GMT
• Title: On the modality gap and the contrastive loss in multi-modal representation learning
• Abstract summary: We study the modality gap in CLIP-style dual-encoder contrastive learning.<n>We find that the gap is induced by a failure of the InfoNCE formulation with independent encoders.<n>We propose a simple modification called xNCE, which uses intermodal as well as intra-modality negative contrastive pairs.
• Score: 3.249853429482705
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the modality gap in CLIP-style dual-encoder contrastive learning, where image and text embeddings remain misaligned despite being trained in a shared space. We argue that the gap is induced by a failure of the InfoNCE formulation with independent encoders. We conduct a uni-modal experiment with two independent encoders and identical initialization conditions and find that InfoNCE actively generates a gap at low temperatures. We provide a theoretical analysis of this phenomenon and show that the modality gap is indeed a mode-failure of InfoNCE, but only at low temperatures. We propose a simple modification called xNCE, which uses intermodal as well as intra-modality negative contrastive pairs. xNCE matches retrieval performance on MS-COCO while consistently reducing the gap even at low temperatures. Notably, xNCE improves zero-shot classification over the InfoNCE baseline across all benchmarks, whereas high-temperature InfoNCE and regularized InfoNCE both fail to do so, demonstrating that xNCE reduces the modality gap without sacrificing the discriminative geometry needed for transfer.

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