Machine learning methods have revolutionized the discovery process of new molecules and materials.
In this way, the proposed network aggregates the context information of a pixel from its semantic-correlated region instead of a predefined fixed region.
Ranked #11 on Semantic Segmentation on COCO-Stuff test
Furthermore, we propose a boundary-aware feature propagation (BFP) module to harvest and propagate the local features within their regions isolated by the learned boundaries in the UAG-structured image.
Ranked #30 on Semantic Segmentation on PASCAL Context
In this paper, we first propose a novel context contrasted local feature that not only leverages the informative context but also spotlights the local information in contrast to the context.
Ranked #14 on Semantic Segmentation on COCO-Stuff test