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
Data-Driven Pixel Control: Challenges and Prospects
Saurabh Farkya, Zachary Alan Daniels, Aswin Raghavan, Gooitzen van der Wal, Michael Isnardi, Michael Piacentino, David Zhang
What could go wrong? Discovering and describing failure modes in computer vision
Gabriela Csurka, Tyler L. Hayes, Diane Larlus, Riccardo Volpi
Fairness and Bias Mitigation in Computer Vision: A Survey
Sepehr Dehdashtian, Ruozhen He, Yi Li, Guha Balakrishnan, Nuno Vasconcelos, Vicente Ordonez, Vishnu Naresh Boddeti
Tensorial template matching for fast cross-correlation with rotations and its application for tomography
Antonio Martinez-Sanchez, Ulrike Homberg, José María Almira, Harold Phelippeau
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework
Guanfeng Tang, Zhiyuan Wu, Jiahang Li, Ping Zhong, Xieyuanli Chen, Huiming Lu, Rui Fan
Mpox Detection Advanced: Rapid Epidemic Response Through Synthetic Data
Yudara Kularathne, Prathapa Janitha, Sithira Ambepitiya, Prarththanan Sothyrajah, Thanveer Ahamed, Dinuka Wijesundara