3 citations · 4 across the 6 of their papers we have counts for
6 papers
Training-Free Pseudo-Fusion for Composed Image Retrieval with Diffusion Models and Multimodal Large Language Models
Fan Xu, Luis A. Leiva
Composed Image Retrieval (CIR) is an emerging paradigm in content-based image retrieval that enables users to formulate compositional queries by combining a reference image with an…
Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation
Bereket A. Yilma, Luis A. Leiva
Art Therapy (AT) is an established practice that facilitates emotional processing and recovery through creative expression. Recently, Visual Art Recommender Systems (VA RecSys) hav…
Multimodal Representation Alignment for Cross-modal Information Retrieval
Fan Xu, Luis A. Leiva
Different machine learning models can represent the same underlying concept in different ways. This variability is particularly valuable for in-the-wild multimodal retrieval, where…
Sparse-to-Sparse Training of Diffusion Models
Inês Cardoso Oliveira, Decebal Constantin Mocanu, Luis A. Leiva
Diffusion models (DMs) are a powerful type of generative models that have achieved state-of-the-art results in various image synthesis tasks and have shown potential in other domai…
The AI-Therapist Duo: Exploring the Potential of Human-AI Collaboration in Personalized Art Therapy for PICS Intervention
Bereket A. Yilma, Chan Mi Kim, Geke Ludden +2
Post-intensive care syndrome (PICS) is a multifaceted condition that arises from prolonged stays in an intensive care unit (ICU). While preventing PICS among ICU patients is becomi…
MOSAIC: Multimodal Multistakeholder-aware Visual Art Recommendation
Bereket A. Yilma, Luis A. Leiva
Visual art (VA) recommendation is complex, as it has to consider the interests of users (e.g. museum visitors) and other stakeholders (e.g. museum curators). We study how to effect…