Computer Vision
Computer vision, a field focused on enabling computers to "see" and interpret images and videos, aims to develop algorithms that can perform tasks such as object detection, image classification, and scene understanding. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and vision transformers (ViTs), often combined with techniques like multi-modal fusion (integrating data from different sensors) and transfer learning to improve efficiency and accuracy. These advancements are driving significant progress in diverse applications, including precision agriculture, robotics, medical imaging analysis, and autonomous systems, by providing automated, efficient, and objective solutions to complex visual tasks.
Papers
An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels
Duy-Kien Nguyen, Mahmoud Assran, Unnat Jain, Martin R. Oswald, Cees G. M. Snoek, Xinlei Chen
Suitability of KANs for Computer Vision: A preliminary investigation
Basim Azam, Naveed Akhtar
UruBots UAV -- Air Emergency Service Indoor Team Description Paper for FIRA 2024
Hiago Sodre, Sebastian Barcelona, Anthony Scirgalea, Brandon Macedo, Gabriel Sampson, Pablo Moraes, William Moraes, Victoria Saravia, Juan Deniz, Bruna Guterres, Andre Kelbouscas, Ricardo Grando