When Vision Models Meet Parameter Efficient Look-Aside Adapters Without Large-Scale Audio Pretraining

Recent studies show that pretrained vision models can boost performance in audio downstream tasks. To enhance the performance further, an additional pretraining stage with large scale audio data is typically required to infuse audio specific knowledge into the vision model. However, such approaches...

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Bibliographic Details
Published in:arXiv.org 2024-12
Main Authors: Yeo, Juan, Jang, Jinkwan, Chae, Kyubyung, Mun, Seongkyu, Kim, Taesup
Format: Article
Language:English
Subjects:
Adapters
Audio data
Performance enhancement
Online Access:Get full text
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Summary:Recent studies show that pretrained vision models can boost performance in audio downstream tasks. To enhance the performance further, an additional pretraining stage with large scale audio data is typically required to infuse audio specific knowledge into the vision model. However, such approaches require extensive audio data and a carefully designed objective function. In this work, we propose bypassing the pretraining stage by directly fine-tuning the vision model with our Look Aside Adapter (LoAA) designed for efficient audio understanding. Audio spectrum data is represented across two heterogeneous dimensions time and frequency and we refine adapters to facilitate interactions between tokens across these dimensions. Our experiments demonstrate that our adapters allow vision models to reach or surpass the performance of pretrained audio models in various audio and speech tasks, offering a resource efficient and effective solution for leveraging vision models in audio applications.
ISSN:2331-8422