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• W3006802913 abstract "Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation problem, but at the expense of demanding design efforts. In this work, we automate the design of counting models with Neural Architecture Search (NAS) and introduce an end-to-end searched encoder-decoder architecture, Automatic Multi-Scale Network (AMSNet). Specifically, we utilize a counting-specific two-level search space. The encoder and decoder in AMSNet are composed of different cells discovered from micro-level search, while the multi-path architecture is explored through macro-level search. To solve the pixel-level isolation issue in MSE loss, AMSNet is optimized with an auto-searched Scale Pyramid Pooling Loss (SPPLoss) that supervises the multi-scale structural information. Extensive experiments on four datasets show AMSNet produces state-of-the-art results that outperform hand-designed models, fully demonstrating the efficacy of NAS-Count." @default.
• W3006802913 created "2020-03-06" @default.
• W3006802913 creator A5003875781 @default.
• W3006802913 creator A5015525872 @default.
• W3006802913 creator A5032973972 @default.
• W3006802913 creator A5038809760 @default.
• W3006802913 creator A5046605531 @default.
• W3006802913 creator A5066531533 @default.
• W3006802913 creator A5088224164 @default.
• W3006802913 date "2020-02-29" @default.
• W3006802913 modified "2023-10-16" @default.
• W3006802913 title "NAS-Count: Counting-by-Density with Neural Architecture Search" @default.
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