462 citations · 890 across the 23 of their papers we have counts for
23 papers
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot
Lucas Prado Osco, Qiusheng Wu, Eduardo Lopes de Lemos +4
Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentat…
MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture
Diogo Nunes Goncalves, Jose Marcato Junior, Pedro Zamboni +4
Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with indepe…
The Potential of Visual ChatGPT For Remote Sensing
Lucas Prado Osco, Eduardo Lopes de Lemos, Wesley Nunes Gonçalves +2
Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown s…
RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
Leonardo Scabini, Kallil M. Zielinski, Lucas C. Ribas +3
Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied. Most approaches are based on building feature agg…
Semantic Segmentation with Labeling Uncertainty and Class Imbalance
Patrik Olã Bressan, José Marcato Junior, José Augusto Correa Martins +8
Recently, methods based on Convolutional Neural Networks (CNN) achieved impressive success in semantic segmentation tasks. However, challenges such as the class imbalance and the u…
Counting and Locating High-Density Objects Using Convolutional Neural Network
Mauro dos Santos de Arruda, Lucas Prado Osco, Plabiany Rodrigo Acosta +8
This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object…