Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit.
Research and development investments are key to scientific and economic development and to the well-being of society.
Labeling mistakes are frequently encountered in real-world applications.
We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone.
With the deterioration of climate, the phenomenon of rain-induced flooding has become frequent.
no code implementations • 4 Nov 2022 • Yucong Lin, Jinhua Su, Yuhang Li, Yuhao Wei, Hanchao Yan, Saining Zhang, Jiaan Luo, Danni Ai, Hong Song, Jingfan Fan, Tianyu Fu, Deqiang Xiao, Feifei Wang, Jue Hou, Jian Yang
Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions.
Recently, knowledge-enhanced methods leveraging auxiliary knowledge graphs have emerged in relation extraction, surpassing traditional text-based approaches.
To discover the lead-lag relationship, we propose a jointly dynamic topic model and also develop an embedding extension to address the modeling problem of large-scale text corpus.
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.