The article introduces Ducho, a multimodal extraction pipeline that integrates visual and textual features to tackle specific recommendation tasks. It highlights three distinct demonstrations: leveraging fashion product images and textual metadata, capturing audio-visual interactions, and delving into textual interactions for product recommendations. Each demo showcases the capability of Ducho to offer actionable insights while detailing the architecture and data processing methods implemented. By providing guidelines and access to code, the article aims to encourage users to explore and replicate these use cases across various platforms including local machines, Docker, and Google Colab.
Ducho's multimodal extraction pipeline showcases how to effectively utilize visual and textual features for practical applications such as fashion recommendations.
By leveraging models like VGG19 and Xception for images, and Sentence-BERT for text, we demonstrate robust extraction methodologies for diverse datasets.
Collection
[
|
...
]