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

内容簡介：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.

報告人：中山大學  楊淩霄  博士

報告人簡介：博士畢業于香港理工大學，目前在中山大學從事博士後研究工作。研究方向涉及機器學習以及其應用。目前主要研究集中在如何從生物腦建立有效且可解釋性強的模型。楊淩霄博士目前已發表多篇論文，包括ICML、ICCV、CVPR、 AAAI、TIP等在内的國際著名刊物和會議論文。

時  間：2021年10月28日（周四）下午14：00開始

地  點：騰訊在線會議

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

會議 ID：329 971 320

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