Web: http://arxiv.org/abs/2209.10327

Sept. 22, 2022, 1:14 a.m. | Qingbei Guo, Xiao-Jun Wu, Zhiquan Feng, Tianyang Xu, Cong Hu

cs.CV updates on arXiv.org arxiv.org

Existing multi-scale solutions lead to a risk of just increasing the
receptive field sizes while neglecting small receptive fields. Thus, it is a
challenging problem to effectively construct adaptive neural networks for
recognizing various spatial-scale objects. To tackle this issue, we first
introduce a new attention dimension, i.e., depth, in addition to existing
attention dimensions such as channel, spatial, and branch, and present a novel
selective depth attention network to symmetrically handle multi-scale objects
in various vision tasks. Specifically, the …

arxiv attention feature networks representation scale

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