SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w8955072 →Countries where authors are citing SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
This map shows the geographic impact of SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks more than expected).
Fields of papers citing SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
This network shows the impact of SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks.
About SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
This paper, published in 2021, received 499 indexed citations . Written by Lingxiao Yang, Ru‐Yuan Zhang, Lida Li and Xiaohua Xie covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (252 citations), Plant Science (98 citations) and Media Technology (64 citations). Published in International Conference on Machine Learning.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w8955072.