Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image-dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We propose a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels' correspondence under topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via a gradient. We also propose a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code on a companion link: https://github.com/YutingHe-list/GEMINI.

Recent improvement and availability of remote satellite and IoT data offers interesting and diverse applications of artificial intelligence in precision agriculture. Soil moisture is an important component of multiple agricultural and food supply chain practices. It measures the amount of water stored in various depth of soil. Existing data driven approaches for soil moisture prediction use conventional models which fail to capture the dynamic dependency of soil moisture values in near-by locations over time. In this work, we propose to convert the problem of soil moisture prediction as a semi-supervised learning on temporal graphs. We propose a dynamic graph neural network which can use the dependency of related locations over a region to predict soil moisture. However, unlike social or information networks, graph structure is not explicitly given for soil moisture prediction. Hence, we incorporate the problem of graph structure learning in the framework of dynamic GNN. Our algorithm, referred as DGLR, provides an end-to-end learning which can predict soil moisture over multiple locations in a region over time and also update the graph structure in between. Our solution achieves state-of-the-art results on real-world soil moisture datasets compared to existing machine learning approaches.