Funnel Activation for Visual Recognition
Abstract
We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition. The forms of ReLU and PReLU are y = max(x, 0) and y = max(x, px), respectively, while FReLU is in the form of y = max(x,T(x)), where T(·) is the 2D spatial condition. Moreover, the spatial condition achieves a pixel-wise modeling capacity in a simple way, capturing complicated visual layouts with regular convolutions. We conduct experiments on ImageNet, COCO detection, and semantic segmentation tasks, showing great improvements and robustness of FReLU in the visual recognition tasks.
Cite
Text
Ma et al. "Funnel Activation for Visual Recognition." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58621-8_21Markdown
[Ma et al. "Funnel Activation for Visual Recognition." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/ma2020eccv-funnel/) doi:10.1007/978-3-030-58621-8_21BibTeX
@inproceedings{ma2020eccv-funnel,
title = {{Funnel Activation for Visual Recognition}},
author = {Ma, Ningning and Zhang, Xiangyu and Sun, Jian},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58621-8_21},
url = {https://mlanthology.org/eccv/2020/ma2020eccv-funnel/}
}