题  目：SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks

内容简介：In this paper, we propose a conceptually simple but very effective attention module for Convolutional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial-wise attention modules, our module instead infers 3-D attention weights for the feature map in a layer without adding parameters to the original networks. Specifically, we base on some well-known neuroscience theories and propose to optimize an energy function to find the importance of each neuron. We further derive a fast closed-form solution for the energy function, and show that the solution can be implemented in less than ten lines of code. Another advantage of the module is that most of the operators are selected based on the solution to the defined energy function, avoiding too many efforts for structure tuning. Quantitative evaluations on various visual tasks demonstrate that the proposed module is flexible and effective to improve the representation ability of many ConvNets.

报告人：中山大学  杨凌霄  博士

报告人简介：博士毕业于香港理工大学，目前在中山大学从事博士后研究工作。研究方向涉及机器学习以及其应用。目前主要研究集中在如何从生物脑建立有效且可解释性强的模型。杨凌霄博士目前已发表多篇论文，包括ICML、ICCV、CVPR、 AAAI、TIP等在内的国际著名刊物和会议论文。

时  间：2021年10月28日（周四）下午14：00开始

地  点：腾讯在线会议

https://meeting.tencent.com/dm/yaSHWapo5pyh

会议 ID：329 971 320

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