4 papers · 1 filter
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…
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…
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…
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…