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
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning
Michael Munn, Benoit Dherin, Javier Gonzalvo
Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object Recognition
Ajay John Alex, Chloe M. Barnes, Pedro Machado, Isibor Ihianle, Gábor Markó, Martin Bencsik, Jordan J. Bird
Scalable Visual State Space Model with Fractal Scanning
Lv Tang, HaoKe Xiao, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Bo Li
Multi-Scale VMamba: Hierarchy in Hierarchy Visual State Space Model
Yuheng Shi, Minjing Dong, Chang Xu
RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
Jiawei Du, Jia Guo, Weihang Zhang, Shengzhu Yang, Hanruo Liu, Huiqi Li, Ningli Wang
Optimizing Curvature Learning for Robust Hyperbolic Deep Learning in Computer Vision
Ahmad Bdeir, Niels Landwehr
Affine-based Deformable Attention and Selective Fusion for Semi-dense Matching
Hongkai Chen, Zixin Luo, Yurun Tian, Xuyang Bai, Ziyu Wang, Lei Zhou, Mingmin Zhen, Tian Fang, David McKinnon, Yanghai Tsin, Long Quan
On Image Registration and Subpixel Estimation
Serap A. Savari
Influence of Water Droplet Contamination for Transparency Segmentation
Volker Knauthe, Paul Weitz, Thomas Pöllabauer, Tristan Wirth, Arne Rak, Arjan Kuijper, Dieter W. Fellner
Automating Attendance Management in Human Resources: A Design Science Approach Using Computer Vision and Facial Recognition
Bao-Thien Nguyen-Tat, Minh-Quoc Bui, Vuong M. Ngo
A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation
Sushmita Sarker, Prithul Sarker, Gunner Stone, Ryan Gorman, Alireza Tavakkoli, George Bebis, Javad Sattarvand
Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography
Shantanu Ghosh, Clare B. Poynton, Shyam Visweswaran, Kayhan Batmanghelich