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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>\n            Recent solutions to crowd counting problems have already achieved promising performance across various benchmarks. However, applying these approaches to real-world applications is still challenging, because they are computation intensive and lack the flexibility to meet various resource budgets. In this article, we propose an efficient crowd counting neural architecture search (ECCNAS) framework to search efficient crowd counting network structures, which can fill this research gap. A novel\n            <jats:italic>search from pre-trained<\/jats:italic>\n            strategy enables our cross-task NAS to explore the significantly large and flexible search space with less search time and get more proper network structures. Moreover, our well-designed search space can intrinsically provide candidate neural network structures with high performance and efficiency. In order to search network structures according to hardwares with different computational performance, we develop a novel latency cost estimation algorithm in our ECCNAS. Experiments show our searched models get an excellent trade-off between computational complexity and accuracy and have the potential to deploy in practical scenarios with various resource budgets. We reduce the computational cost, in terms of multiply-and-accumulate (MACs), by up to 96% with comparable accuracy. And we further designed experiments to validate the efficiency and the stability improvement of our proposed\n            <jats:italic>search from pre-trained<\/jats:italic>\n            strategy.\n          <\/jats:p>","DOI":"10.1145\/3465455","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T15:06:00Z","timestamp":1643123160000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["ECCNAS: Efficient Crowd Counting Neural Architecture Search"],"prefix":"10.1145","volume":"18","author":[{"given":"Yabin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiheng","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaowei","family":"Wang","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory, Shenzhen, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaopeng","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIoT48696.2020.9089594"},{"key":"e_1_3_1_3_2","volume-title":"AAAI Conference on Artificial Intelligence (AAAI\u201921)","author":"Abousamra Shahira","year":"2021","unstructured":"Shahira Abousamra, Minh Hoai Nguyen, Dimitris Samaras, and Chao Chen. 2021. 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