Cross-Modal Denoising: A Novel Training Paradigm for Enhancing Speech-Image Retrieval

• URL: http://arxiv.org/abs/2408.13705v2
• Date: Wed, 11 Sep 2024 07:22:58 GMT
• Title: Cross-Modal Denoising: A Novel Training Paradigm for Enhancing Speech-Image Retrieval
• Authors: Lifeng Zhou, Yuke Li, Rui Deng, Yuting Yang, Haoqi Zhu,
• Abstract summary: We introduce an effective framework and a novel learning task named cross-modal denoising (CMD) to enhance cross-modal interaction. Specifically, CMD is a denoising task designed to reconstruct semantic features from noisy features within one modality by interacting features from another modality. The experimental results demonstrate that our framework outperforms the state-of-the-art method by 2.0% in mean R@1 on the Flickr8k dataset and by 1.7% in mean R@1 on the SpokenCOCO dataset.
• Score: 16.968343177634015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of speech-image retrieval relies on establishing an effective alignment between speech and image. Existing methods often model cross-modal interaction through simple cosine similarity of the global feature of each modality, which fall short in capturing fine-grained details within modalities. To address this issue, we introduce an effective framework and a novel learning task named cross-modal denoising (CMD) to enhance cross-modal interaction to achieve finer-level cross-modal alignment. Specifically, CMD is a denoising task designed to reconstruct semantic features from noisy features within one modality by interacting features from another modality. Notably, CMD operates exclusively during model training and can be removed during inference without adding extra inference time. The experimental results demonstrate that our framework outperforms the state-of-the-art method by 2.0% in mean R@1 on the Flickr8k dataset and by 1.7% in mean R@1 on the SpokenCOCO dataset for the speech-image retrieval tasks, respectively. These experimental results validate the efficiency and effectiveness of our framework.

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