Multi View
Multi-view analysis integrates data from multiple perspectives to improve accuracy and robustness in various applications, primarily aiming to overcome limitations of single-view approaches. Current research focuses on developing efficient algorithms and model architectures, such as transformers and graph neural networks, to handle high-dimensional data and address challenges like data incompleteness, view misalignment, and computational constraints. This field is significant for advancing computer vision, medical image analysis, robotics, and other domains by enabling more accurate and reliable inferences from complex, multi-faceted data.
Papers
A comparison between single-stage and two-stage 3D tracking algorithms for greenhouse robotics
David Rapado-Rincon, Akshay K. Burusa, Eldert J. van Henten, Gert Kootstra
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation
Guanhua Chen, Wenhan Yu, Lei Sha
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data
Zhenzhong Wang, Qingyuan Zeng, Wanyu Lin, Min Jiang, Kay Chen Tan
AccidentBlip2: Accident Detection With Multi-View MotionBlip2
Yihua Shao, Hongyi Cai, Xinwei Long, Weiyi Lang, Zhe Wang, Haoran Wu, Yan Wang, Jiayi Yin, Yang Yang, Yisheng Lv, Zhen Lei
Trusted Multi-view Learning with Label Noise
Cai Xu, Yilin Zhang, Ziyu Guan, Wei Zhao
Node-like as a Whole: Structure-aware Searching and Coarsening for Graph Classification
Xiaorui Qi, Qijie Bai, Yanlong Wen, Haiwei Zhang, Xiaojie Yuan
Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion
Fan Yang, Jianfeng Zhang, Yichun Shi, Bowen Chen, Chenxu Zhang, Huichao Zhang, Xiaofeng Yang, Jiashi Feng, Guosheng Lin
DreamView: Injecting View-specific Text Guidance into Text-to-3D Generation
Junkai Yan, Yipeng Gao, Qize Yang, Xihan Wei, Xuansong Xie, Ancong Wu, Wei-Shi Zheng