Exploring Efficient-Tuned Learning Audio Representation Method from BriVL
This work addresses multimodal learning for audio-to-image generation, but it appears incremental as it builds on existing Bridging-Vision-and-Language frameworks.
The paper tackles the problem of learning audio representations for multimodal applications by proposing WavBriVL, a method that projects audio, image, and text into a shared embedded space, enabling image generation from audio with effective results.
Recently, researchers have gradually realized that in some cases, the self-supervised pre-training on large-scale Internet data is better than that of high-quality/manually labeled data sets, and multimodal/large models are better than single or bimodal/small models. In this paper, we propose a robust audio representation learning method WavBriVL based on Bridging-Vision-and-Language (BriVL). WavBriVL projects audio, image and text into a shared embedded space, so that multi-modal applications can be realized. We demonstrate the qualitative evaluation of the image generated from WavBriVL as a shared embedded space, with the main purposes of this paper:(1) Learning the correlation between audio and image;(2) Explore a new way of image generation, that is, use audio to generate pictures. Experimental results show that this method can effectively generate appropriate images from audio.