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
Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Survey
Guoping Xu, Xiaxia Wang, Xinglong Wu, Xuesong Leng, Yongchao Xu
Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey
Rokas Gipiškis, Chun-Wei Tsai, Olga Kurasova
NeRF in Robotics: A Survey
Guangming Wang, Lei Pan, Songyou Peng, Shaohui Liu, Chenfeng Xu, Yanzi Miao, Wei Zhan, Masayoshi Tomizuka, Marc Pollefeys, Hesheng Wang
TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos
Atom Scott, Ikuma Uchida, Ning Ding, Rikuhei Umemoto, Rory Bunker, Ren Kobayashi, Takeshi Koyama, Masaki Onishi, Yoshinari Kameda, Keisuke Fujii
On Support Relations Inference and Scene Hierarchy Graph Construction from Point Cloud in Clustered Environments
Gang Ma, Hui Wei