Hongwei Li’s team from China University of Geosciences published a research article titled “scSCC: A swapped contrastive ...

Semantic segmentation (SS) and height estimation (HE) are two critical tasks in remote sensing scene understanding that are highly correlated with each other. To address both the tasks simultaneously, ...

Multi-scale contrastive learning enhances class similarity and separability while reducing teacher-student model similarity, encouraging diverse feature representations. Additionally, a boundary-aware ...

A novel DIPN method enhances GA segmentation by integrating ConvLSTM, projection attention, adaptive pooling, and contrastive learning modules. The method effectively utilizes B-scan images, ...

Recently, pixel-to-pixel contrastive learning in single-scale feature space has been widely studied in semantic segmentation to learn a unified feature expression for pixels of the same category.

In this paper, we applied contrastive learning strategy and proposed a semi-supervised method for echocardiographic images segmentation. This proposed method solved the above challenges effectively ...

Coarse-grained models have proven helpful for simulating complex systems over long time scales to provide molecular insights into various processes. Methodologies for systematic parametrization of the ...

Implementation of Pixel-level Contrastive Learning, proposed in the paper "Propagate Yourself", in Pytorch. In addition to doing contrastive learning on the pixel level, the online network further ...