no code implementations • 13 Nov 2024 • Tiago F. Tavares, Fabio Ayres, Paris Smaragdis
These experiments are based on specific assumptions about the geometry of embedding spaces, which allow finding paired items by extrapolating the positional relationships between embedding pairs in the training dataset, allowing for tasks such as finding new analogies, and multimodal zero-shot classification.
no code implementations • 23 Aug 2024 • Tiago Tavares, Fabio Ayres, Zhepei Wang, Paris Smaragdis
Recent advances in audio-text cross-modal contrastive learning have shown its potential towards zero-shot learning.
1 code implementation • 3 May 2023 • Zhepei Wang, Cem Subakan, Krishna Subramani, Junkai Wu, Tiago Tavares, Fabio Ayres, Paris Smaragdis
In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio.