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[2412.07909] Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning
Summary
This paper explores the modality gap in contrastive multimodal learning, analyzing its causes and proposing methods to mitigate it for improved model performance.
Why It Matters
Understanding and addressing the modality gap is crucial for enhancing the effectiveness of multimodal models like CLIP. This research provides insights into the underlying mechanics of these models, which can lead to better performance in tasks such as image-text retrieval and contribute to advancements in AI applications that rely on multimodal learning.
Key Takeaways
- The modality gap occurs when different modalities occupy distinct areas in a shared representation space.
- Mismatched data pairs and temperature parameters are significant contributors to the modality gap.
- Strategies such as temperature scheduling and modality swapping can effectively mitigate the gap.
- Closing the modality gap enhances performance in tasks like image-text retrieval.
- The findings are validated through experiments on practical CLIP models.
Computer Science > Machine Learning arXiv:2412.07909 (cs) [Submitted on 10 Dec 2024 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning Authors:Can Yaras, Siyi Chen, Peng Wang, Qing Qu View a PDF of the paper titled Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning, by Can Yaras and 3 other authors View PDF HTML (experimental) Abstract:Multimodal learning has recently gained significant popularity, demonstrating impressive performance across various zero-shot classification tasks and a range of perceptive and generative applications. Models such as Contrastive Language-Image Pretraining (CLIP) are designed to bridge different modalities, such as images and text, by learning a shared representation space through contrastive learning. Despite their success, the working mechanisms underlying multimodal learning are not yet well understood. Notably, these models often exhibit a modality gap, where different modalities occupy distinct regions within the shared representation space. In this work, we conduct an in-depth analysis of the emergence of modality gap by characterizing the gradient flow learning dynamics. Specifically, we identify the critical roles of mismatched data pairs and a learnable temperature parameter in causing and perpetuating the modality gap during training. Furthermore, our theoretical insights are validated through experiments on practical CLIP ...
