Boosting crop classification by hierarchically fusing satellite, rotational, and contextual data
This is a new approach to classify crop types using a multimodal hierarchical model combinning satellite data, crop rotation patterns, and local crop distribution. It outperforms existing approaches by leveraging the spatio-temporal context of agricultural parcels, and has shown promising results in cross-country transferability and few-shot learning settings.
Apr 1, 2024
Targeted Image Data Augmentation Increases Basic Skills Captioning Robustness
TIDA is a new data augmentation method that uses text-to-image generation to create more diverse and realistic training data, helping AI models better understand complex correlations and improve their performance on tasks like gender recognition.
Dec 1, 2023
Opinions in Interactions : New Annotations of the SEMAINE Database
We've added new opinion annotations to the SEMAINE dataset, which captures dyadic interactions between humans and virtual agents, resulting in a rich dataset with over 73,000 words and 6 hours of conversation. Our annotations and proposed baseline model using RoBERTa embeddings achieve promising results, with a F1-score of 0.72, making it a valuable resource for opinion detection in human-computer interactions.
Jun 1, 2022