10 citations · 34 across the 12 of their papers we have counts for
13 papers
Co-Learning for Missing Arbitrary Modalities in Multi-modal Classification
Francisco Mena, Dino Ienco, Roberto Interdonato +2
Multi-modal classification leverages complementary information across diverse data sources to enhance predictive performance. However, real-world scenarios subject to operational c…
Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration
Francisco Mena, Dino Ienco, Cassio F. Dantas +2
Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality pred…
TimeSenCLIP: A Time Series Vision-Language Model for Remote Sensing
Pallavi Jain, Diego Marcos, Dino Ienco +2
Vision-language models (VLMs) have shown significant promise in remote sensing applications, particularly for land-use and land-cover (LULC) mapping via zero-shot classification an…
SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting
Pallavi Jain, Dino Ienco, Roberto Interdonato +2
Pre-trained vision-language models (VLMs), such as CLIP, demonstrate impressive zero-shot classification capabilities with free-form prompts and even show some generalization in sp…
Evaluation of Geographical Distortions in Language Models
Rémy Decoupes, Roberto Interdonato, Mathieu Roche +2
Language models now constitute essential tools for improving efficiency for many professional tasks such as writing, coding, or learning. For this reason, it is imperative to ident…
An evaluation framework for comparing epidemic intelligence systems
Nejat Arinik, Roberto Interdonato, Mathieu Roche +1
In the context of Epidemic Intelligence, many Event-Based Surveillance (EBS) systems have been proposed in the literature to promote the early identification and characterization o…