36 citations · 36 across the 2 of their papers we have counts for
2 papers
cs.AI2024
A Modular End-to-End Multimodal Learning Method for Structured and Unstructured Data
Marco D Alessandro, Enrique Calabrés, Mikel Elkano
Multimodal learning is a rapidly growing research field that has revolutionized multitasking and generative modeling in AI. While much of the research has focused on dealing with u…
cs.CV2023★ 36 cited
Multimodal Parameter-Efficient Few-Shot Class Incremental Learning
Marco D'Alessandro, Alberto Alonso, Enrique Calabrés +1
Few-Shot Class Incremental Learning (FSCIL) is a challenging continual learning task, where limited training examples are available during several learning sessions. To succeed in…