The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model label dependencies to improve recognition performance. To capture and explore such important information, we propose Graph Convolutional Networks based models for multi-label recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier-Learning-GCN to map class-level semantic representations (\eg, word embedding) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label-
Cipla Doubles Remdesivir Production In India For Unprecedented Demand
The ability of artificial intelligence (AI) and machine learning (ML) in furthering medical research has become indisputable. Adding credence to their application, a new ML algorithm has helped researchers identify over 160 new genes that are responsible for the development of numerous cancers.
Developed by scientists from the Max Planck Institute for Molecular Genetics (MPIMG) and Institute of Computational Biology (ICB), the algorithm helped identify 165 unknown cancer genes. The new genes are said to closely interact with previously known cancer-causing genes and found to be vital in the survival of cancer cells in cell culture experiments.