COMET: Concept Space Dissection of the Modality Gap in Audio-Text Multimodal Contrastive Embeddings
Abstract
Contrastive Language-Audio Pretraining (CLAP) models are widely used for audio understanding and support modality-agnostic condition swapping in many zero-shot applications. However, their performance is heavily affected by the modality gap between audio and text embeddings. Existing explanations mainly attribute this gap to the cone effect, treating it as a shift between mean embeddings, yet correcting the mean alone yields only limited improvements. Alternative hypotheses, such as information imbalance and dimensionality collapse, have also been proposed, but they remain insufficiently verified and have not been thoroughly studied in the audio domain. Meanwhile, several works attempt to decompose multimodal contrastive embeddings into interpretable concepts, but none explicitly analyze the modality gap from the perspective of concept decomposition. In this work, we introduce COMET (Concept space Organization and Modality gap Explanation with PLS-SVD Transformation), a novel partial least squares singular value decomposition (PLS-SVD) framework for CLAP that unveils a broader perspective of the modality gap. Our framework reveals that only a small, interpretable subset of axes, which captures shared concepts, contributes substantially to similarity computation, and that the mean component represents only partially the modality gap. Building on this insight, we propose a simple spectral truncation method that mitigates the modality gap in a training-free manner. The method enables zero-shot audio captioning with condition swapping to approach fully supervised performance, without requiring large auxiliary memory banks or expensive computation. At the same time, it achieves substantial embedding dimensionality reduction while preserving strong performance on retrieval and audio captioning tasks.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of COMET, a novel framework for analyzing and mitigating the modality gap in audio-text multimodal contrastive embeddings, which significantly enhances the performance of zero-shot audio captioning tasks. The comprehensive analysis and innovative methodology position this work as a meaningful advancement in the field of multimodal machine learning.
Methodology
The paper introduces a novel framework, COMET, utilizing Partial Least Squares Singular Value Decomposition (PLS-SVD) to analyze and mitigate the modality gap between audio and text embeddings in CLAP models. The methodology is well-structured, offering a fresh perspective on the decomposition of multimodal embeddings into interpretable concepts. The spectral truncation method proposed is innovative, allowing for effective dimensionality reduction while maintaining performance, which is a significant contribution to the field of multimodal contrastive learning.
Results & evaluation
The experiments are comprehensive, utilizing standard datasets like Clotho and AudioCaps for evaluation. The results demonstrate that the proposed PLSHead method achieves comparable or improved performance over the original embeddings, validating the effectiveness of the approach. The paper provides detailed metrics for retrieval tasks, showcasing the robustness of the method across different scenarios, including in-domain and cross-domain evaluations.
Reproducibility
The paper lacks explicit implementation details or code availability, which could hinder reproducibility. While the methodology is clearly described, the absence of a publicly available codebase or demo limits the ability for other researchers to replicate the findings.
Limitations
One limitation is the reliance on existing CLAP models, which may introduce biases based on their training data. Additionally, while the proposed methods show promise, the paper does not explore the potential impacts of varying the number of retained dimensions in the spectral truncation, which could affect generalization in different contexts.
Broader impact
The findings have significant implications for audio understanding and generation tasks, particularly in zero-shot scenarios. By effectively bridging the modality gap, the proposed methods could enhance the performance of multimodal applications, making them more accessible and efficient. This work could pave the way for future research in multimodal learning and its applications in real-world scenarios. The main contribution of this paper is the introduction of COMET, a novel framework for analyzing and mitigating the modality gap in audio-text multimodal contrastive embeddings, which significantly enhances the performance of zero-shot audio captioning tasks. The comprehensive analysis and innovative methodology position this work as a meaningful advancement in the field of multimodal machine learning.
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