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
LLMs in Political Science: Heralding a New Era of Visual Analysis
Yu Wang
HyenaPixel: Global Image Context with Convolutions
Julian Spravil, Sebastian Houben, Sven Behnke
VEnvision3D: A Synthetic Perception Dataset for 3D Multi-Task Model Research
Jiahao Zhou, Chen Long, Yue Xie, Jialiang Wang, Boheng Li, Haiping Wang, Zhe Chen, Zhen Dong
A Study of Shape Modeling Against Noise
Cheng Long, Adrian Barbu
Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review
Opeyemi Bamigbade, John Sheppard, Mark Scanlon
Descripci\'on autom\'atica de secciones delgadas de rocas: una aplicaci\'on Web
Stalyn Paucar, Christian Mejía-Escobar y Víctor Collaguazo
Designing High-Performing Networks for Multi-Scale Computer Vision
Cédric Picron
Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges
Daniel Jakab, Brian Michael Deegan, Sushil Sharma, Eoin Martino Grua, Jonathan Horgan, Enda Ward, Pepijn Van De Ven, Anthony Scanlan, Ciarán Eising