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Improving Audio-Language Learning with MixGen and Multi-Level Test-Time Augmentation

2022-10-31 08:50:52
Eungbeom Kim, Jinhee Kim, Yoori Oh, Kyungsu Kim, Minju Park, Jaeheon Sim, Jinwoo Lee, Kyogu Lee
arXiv_CL
         
Abstract

In this paper, we propose two novel augmentation methods 1) audio-language MixGen (AL-MixGen) and 2) multi-level test-time augmentation (Multi-TTA) for audio-language learning. Inspired by MixGen, which is originally applied to vision-language learning, we introduce an augmentation method for the audio-language domain. We also explore the impact of test-time augmentations and present Multi-TTA which generalizes test-time augmentation over multiple layers of a deep learning model. Incorporating AL-MixGen and Multi-TTA into the baseline achieves 47.5 SPIDEr on audio captioning, which is an +18.2% over the baseline and outperforms the state-of-the-art approach with a 5x smaller model. In audio-text retrieval, the proposed methods surpass the baseline performance as well.

Abstract (translated)

URL

https://arxiv.org/abs/2210.17143

PDF

https://arxiv.org/pdf/2210.17143.pdf

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