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
A preliminary study on continual learning in computer vision using Kolmogorov-Arnold Networks
Alessandro Cacciatore, Valerio Morelli, Federica Paganica, Emanuele Frontoni, Lucia Migliorelli, Daniele Berardini
Formula-Supervised Visual-Geometric Pre-training
Ryosuke Yamada, Kensho Hara, Hirokatsu Kataoka, Koshi Makihara, Nakamasa Inoue, Rio Yokota, Yutaka Satoh
A Deep Learning Approach for Pixel-level Material Classification via Hyperspectral Imaging
Savvas Sifnaios, George Arvanitakis, Fotios K. Konstantinidis, Georgios Tsimiklis, Angelos Amditis, Panayiotis Frangos
Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?
Jae Hee Lee, Georgii Mikriukov, Gesina Schwalbe, Stefan Wermter, Diedrich Wolter
Enhancing Fruit and Vegetable Detection in Unconstrained Environment with a Novel Dataset
Sandeep Khanna, Chiranjoy Chattopadhyay, Suman Kundu
Evolution and challenges of computer vision and deep learning technologies for analysing mixed construction and demolition waste
Adrian Langley, Matthew Lonergan, Tao Huang, Mostafa Rahimi Azghadi
Accelerating AI and Computer Vision for Satellite Pose Estimation on the Intel Myriad X Embedded SoC
Vasileios Leon, Panagiotis Minaidis, George Lentaris, Dimitrios Soudris
Investigating the Impact of Randomness on Reproducibility in Computer Vision: A Study on Applications in Civil Engineering and Medicine
Bahadır Eryılmaz, Osman Alperen Koraş, Jörg Schlötterer, Christin Seifert
Robotic Optimization of Powdered Beverages Leveraging Computer Vision and Bayesian Optimization
Emilia Szymanska, Josie Hughes
STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking
Jianbo Ma, Chuanming Tang, Fei Wu, Can Zhao, Jianlin Zhang, Zhiyong Xu
Estimating the distribution of numerosity and non-numerical visual magnitudes in natural scenes using computer vision
Kuinan Hou, Marco Zorzi, Alberto Testolin
GatedUniPose: A Novel Approach for Pose Estimation Combining UniRepLKNet and Gated Convolution
Liang Feng, Ming Xu, Lihua Wen, Zhixuan Shen
Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities
Aaryan Panda, Damodar Panigrahi, Shaswata Mitra, Sudip Mittal, Shahram Rahimi
Deep Learning for Video Anomaly Detection: A Review
Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Peng Wang, Yanning Zhang
Rethinking the Atmospheric Scattering-driven Attention via Channel and Gamma Correction Priors for Low-Light Image Enhancement
Shyang-En Weng, Cheng-Yen Hsiao, Shaou-Gang Miaou, Ricky Christanto