EDTC: enhance depth of text comprehension in automated audio captioning
<|reference_start|>EDTC: enhance depth of text comprehension in automated audio captioning: Modality discrepancies have perpetually posed significant challenges within the realm of Automated Audio Captioning (AAC) and across all multi-modal domains. Facilitating models in comprehending text information plays a pivotal role in establishing a seamless connection between the two modalities of text and audio. While recent research has focused on closing the gap between these two modalities through contrastive learning, it is challenging to bridge the difference between both modalities using only simple contrastive loss. This paper introduces Enhance Depth of Text Comprehension (EDTC), which enhances the model's understanding of text information from three different perspectives. First, we propose a novel fusion module, FUSER, which aims to extract shared semantic information from different audio features through feature fusion. We then introduced TRANSLATOR, a novel alignment module designed to align audio features and text features along the tensor level. Finally, the weights are updated by adding momentum to the twin structure so that the model can learn information about both modalities at the same time. The resulting method achieves state-of-the-art performance on AudioCaps datasets and demonstrates results comparable to the state-of-the-art on Clotho datasets.<|reference_end|>
@article{tan2024edtc:,
    title={EDTC: enhance depth of text comprehension in automated audio captioning},
    author={Liwen Tan, Yin Cao, Yi Zhou},
    journal={arXiv preprint arXiv:2402.17259},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17259},
    primaryClass={cs.SD eess.AS}
}