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
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?
Maximilian Ulmer, Leonard Klüpfel, Maximilian Durner, Rudolph Triebel
RANSAC Back to SOTA: A Two-stage Consensus Filtering for Real-time 3D Registration
Pengcheng Shi, Shaocheng Yan, Yilin Xiao, Xinyi Liu, Yongjun Zhang, Jiayuan Li
Dynamic Object Catching with Quadruped Robot Front Legs
André Schakkal, Guillaume Bellegarda, Auke Ijspeert
A transition towards virtual representations of visual scenes
Américo Pereira, Pedro Carvalho, Luís Côrte-Real
Multimodal Perception System for Real Open Environment
Yuyang Sha
Multi-Scale Deformable Transformers for Student Learning Behavior Detection in Smart Classroom
Zhifeng Wang, Minghui Wang, Chunyan Zeng, Longlong Li
On Feature Decorrelation in Cloth-Changing Person Re-identification
Hongjun Wang, Jiyuan Chen, Renhe Jiang, Xuan Song, Yinqiang Zheng
Systematic Literature Review of Vision-Based Approaches to Outdoor Livestock Monitoring with Lessons from Wildlife Studies
Stacey D. Scott, Zayn J. Abbas, Feerass Ellid, Eli-Henry Dykhne, Muhammad Muhaiminul Islam, Weam Ayad, Kristina Kacmorova, Dan Tulpan, Minglun Gong
PRFusion: Toward Effective and Robust Multi-Modal Place Recognition with Image and Point Cloud Fusion
Sijie Wang, Qiyu Kang, Rui She, Kai Zhao, Yang Song, Wee Peng Tay