# Localized Feature Aggregation Module for Semantic Segmentation Source: [Localized Feature Aggregation Module for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:47:07.195Z Authors: Ryouichi Furukawa, Kazuhiro Hotta Published: 2021-12-03T04:15:00.000Z ## Abstract We propose a new information aggregation method which called Localized Feature Aggregation Module based on the similarity between the feature maps of an encoder and a decoder. The proposed method recovers positional information by emphasizing the similarity between decoder's feature maps with superior semantic information and encoder's feature maps with superior positional information. The proposed method can learn positional information more efficiently than conventional concatenation in the U-net and attention U-net. Additionally, the proposed method also uses localized attention range to reduce the computational cost. Two innovations contributed to improve the segmentation accuracy with lower computational cost. By experiments on the Drosophila cell image dataset and COVID-19 image dataset, we confirmed that our method outperformed conventional methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()