GMS-CAVP: Improving Audio-Video Correspondence with Multi-Scale Contrastive and Generative Pretraining
Abstract
Recent advances in video-audio (V-A) understanding and generation have increasingly relied on joint V-A embeddings, which serve as the foundation for tasks such as cross-modal retrieval and generation. While prior methods like CAVP effectively model semantic and temporal correspondences between modalities using contrastive objectives, their performance remains suboptimal. A key limitation is the insufficient modeling of the dense, multi-scale nature of both video and audio signals, correspondences often span fine- to coarse-grained spatial-temporal structures, which are underutilized in existing frameworks. To this end, we propose GMS-CAVP, a novel framework that combines Multi-Scale Video-Audio Alignment and Multi-Scale Spatial-Temporal Diffusion-based pretraining objectives to enhance V-A correspondence modeling. First, GMS-CAVP introduces a multi-scale contrastive learning strategy that captures semantic and temporal relations across varying granularities. Second, we go beyond traditional contrastive learning by incorporating a diffusion-based generative objective, enabling modality translation and synthesis between video and audio. This unified discriminative-generative formulation facilitates deeper cross-modal understanding and paves the way for high-fidelity generation. Extensive experiments on VGGSound, AudioSet, and Panda70M demonstrate that GMS-CAVP outperforms previous methods in generation and retrieval.
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 the GMS-CAVP framework, which significantly improves video-audio correspondence modeling through a novel combination of multi-scale contrastive learning and diffusion-based generative objectives. This work represents a meaningful step forward in the field of cross-modal understanding and generation, addressing critical limitations of existing methods and demonstrating substantial improvements in performance on benchmark datasets.
Methodology
The proposed GMS-CAVP framework introduces a multi-scale contrastive learning strategy that effectively captures semantic and temporal relations across different granularities. The incorporation of a diffusion-based generative objective is particularly noteworthy, as it enhances the model's ability to perform modality translation and synthesis. This dual approach of combining discriminative and generative learning paradigms is innovative and addresses the limitations of existing methods, which primarily focus on contrastive learning without considering the dense, multi-scale nature of audio and video signals.
Results & evaluation
The experiments conducted on well-established datasets such as VGGSound, AudioSet, and Panda70M provide a robust evaluation of the proposed method. The results demonstrate significant improvements over previous state-of-the-art methods in both generation and retrieval tasks. The extensive nature of the experiments strengthens the claims made by the authors regarding the effectiveness of their approach.
Reproducibility
The paper lacks detailed implementation specifics, which may hinder reproducibility. While the methodology is well-outlined, the absence of a publicly available code repository or demo URL limits the ability of other researchers to replicate the results or build upon this work.
Limitations
One notable limitation is the potential computational complexity associated with the multi-scale approach, which may not be feasible for real-time applications. Additionally, the paper does not address the scalability of the model when applied to larger datasets or in practical scenarios.
Broader impact
The advancements made in video-audio correspondence modeling have significant implications for various applications, including multimedia content generation, cross-modal retrieval systems, and improved accessibility features for audio-visual content. The ability to generate high-fidelity audio from video could enhance user experiences in entertainment and education. The main contribution of this paper is the introduction of the GMS-CAVP framework, which significantly improves video-audio correspondence modeling through a novel combination of multi-scale contrastive learning and diffusion-based generative objectives. This work represents a meaningful step forward in the field of cross-modal understanding and generation, addressing critical limitations of existing methods and demonstrating substantial improvements in performance on benchmark datasets.
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