Involution: Inverting the Inherence of Convolution for Visual Recognition (Research Paper Explained)

#involution​ #computervision​ #attention​ Convolutional Neural Networks (CNNs) have dominated computer vision for almost a decade by applying two fundamental principles: Spatial agnosticism and channel-specific computations. Involution aims to invert these principles and presents a spatial-specific computation, which is also channel-agnostic. The resulting Involution Operator and RedNet architecture are a compromise between classic Convolutions and the newer Local Self-Attention architectures and perform favorably in terms of computation accuracy tradeoff when compared to either. OUTLINE: 0:00​ - Intro & Overview 3:00​ - Principles of Convolution 10:50​ - Towards spatial-specific computations 17:00​ - The Involution Operator 20:00​ - Comparison to Self-Attention 25:15​ - Experimental Results 30:30​ - Comments & Conclusion Paper: https://arxiv.org/abs/2103.06255​ Code: https://github.com/d-li14/involution​ Abstract: Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. In this work, we rethink the inherent principles of standard convolution for vision tasks, specifically spatial-agnostic and channel-specific. Instead, we present a novel atomic operation for deep neural networks by inverting the aforementioned design principles of convolution, coined as involution. We additionally demystify the recent popular self-attention operator and subsume it into our involution family as an over-complicated instantiation. The proposed involution operator could be leveraged as fundamental bricks to build the new generation of neural networks for visual recognition, powering different deep learning models on several prevalent benchmarks, including ImageNet classification, COCO detection and segmentation, together with Cityscapes segmentation. Our involution-based models improve the performance of convolutional baselines using ResNet-50 by up to 1.6% top-1 accuracy, 2.5% and 2.4% bounding box AP, and 4.7% mean IoU absolutely while compressing the computational cost to 66%, 65%, 72%, and 57% on the above benchmarks, respectively. Code and pre-trained models for all the tasks are available at this https URL. Authors: Duo Li, Jie Hu, Changhu Wang, Xiangtai Li, Qi She, Lei Zhu, Tong Zhang, Qifeng Chen Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick​ YouTube: https://www.youtube.com/c/yannickilcher​ Twitter: https://twitter.com/ykilcher​ Discord: https://discord.gg/4H8xxDF​ BitChute: https://www.bitchute.com/channel/yann...​ Minds: https://www.minds.com/ykilcher​ Parler: https://parler.com/profile/YannicKilcher​ LinkedIn: https://www.linkedin.com/in/yannic-ki...​ BiliBili: https://space.bilibili.com/1824646584​ If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick...​ Patreon: https://www.patreon.com/yannickilcher​ Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n