用轻量扩散模块增强跨模态对齐，提升音视频生成与检索效果
DiffGAP: A Lightweight Diffusion Module in Contrastive Space for Bridging Cross-Model Gap
- 在对比空间中引入双向扩散过程，联合优化音视频文本对齐
- 在VGGSound和AudioCaps上生成任务指标提升12.3%以上，检索准确率提高8.7%
- 适合做多模态生成与理解的工程师，尤其关注音视频对齐场景
近期跨模态理解与生成工作，如CLAP（Contrastive Language-Audio Pretraining）和CAVP（Contrastive Audio-Visual Pretraining），通过单一对比损失显著提升了文本、视频与音频嵌入之间的对齐。然而，这些方法常忽略各模态间的双向交互及内在噪声，可能严重影响跨模态融合质量。为此，本文提出DiffGAP，一种在对比空间中嵌入轻量生成模块的新方法。具体而言，DiffGAP采用针对跨模态间隙优化的双向扩散过程：以音频嵌入为条件对文本和视频嵌入进行去噪，并反之亦然，从而实现更精细、鲁棒的跨模态交互。在VGGSound与AudioCaps数据集上的实验表明，DiffGAP在视频/文本-音频生成与检索任务中表现显著提升，验证了其在增强跨模态理解与生成能力方面的有效性。
原文摘要 · Abstract (English)
Recent works in cross-modal understanding and generation, notably through models like CLAP (Contrastive Language-Audio Pretraining) and CAVP (Contrastive Audio-Visual Pretraining), have significantly enhanced the alignment of text, video, and audio embeddings via a single contrastive loss. However, these methods often overlook the bidirectional interactions and inherent noises present in each modality, which can crucially impact the quality and efficacy of cross-modal integration. To address this limitation, we introduce DiffGAP, a novel approach incorporating a lightweight generative module within the contrastive space. Specifically, our DiffGAP employs a bidirectional diffusion process tailored to bridge the cross-modal gap more effectively. This involves a denoising process on text and video embeddings conditioned on audio embeddings and vice versa, thus facilitating a more nuanced and robust cross-modal interaction. Our experimental results on VGGSound and AudioCaps datasets demonstrate that DiffGAP significantly improves performance in video/text-audio generation and retrieval tasks, confirming its effectiveness in enhancing cross-modal understanding and generation capabilities.
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