Involution: Inverting the Inherence of Convolution for Visual Recognition

• URL: http://arxiv.org/abs/2103.06255v1
• Date: Wed, 10 Mar 2021 18:40:46 GMT
• Title: Involution: Inverting the Inherence of Convolution for Visual Recognition
• Authors: Duo Li, Jie Hu, Changhu Wang, Xiangtai Li, Qi She, Lei Zhu, Tong Zhang, Qifeng Chen
• Abstract summary: We present a novel atomic operation for deep neural networks by inverting the principles of convolution, coined as involution. The proposed involution operator could be leveraged as fundamental bricks to build the new generation of neural networks for visual recognition. 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.
• Score: 72.88582255910835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/d-li14/involution.

Related papers

• Mechanism of feature learning in convolutional neural networks [14.612673151889615]
  We identify the mechanism of how convolutional neural networks learn from image data. We present empirical evidence for our ansatz, including identifying high correlation between covariances of filters and patch-based AGOPs. We then demonstrate the generality of our result by using the patch-based AGOP to enable deep feature learning in convolutional kernel machines.
  arXiv  Detail & Related papers  (2023-09-01T16:30:02Z)
• Evolution of Activation Functions for Deep Learning-Based Image Classification [0.0]
  Activation functions (AFs) play a pivotal role in the performance of neural networks. We propose a novel, three-population, coevolutionary algorithm to evolve AFs. Tested on four datasets -- MNIST, FashionMNIST, KMNIST, and USPS -- coevolution proves to be a performant algorithm for finding good AFs and AF architectures.
  arXiv  Detail & Related papers  (2022-06-24T05:58:23Z)
• X-volution: On the unification of convolution and self-attention [52.80459687846842]
  We propose a multi-branch elementary module composed of both convolution and self-attention operation. The proposed X-volution achieves highly competitive visual understanding improvements.
  arXiv  Detail & Related papers  (2021-06-04T04:32:02Z)
• Implementing a foveal-pit inspired filter in a Spiking Convolutional Neural Network: a preliminary study [0.0]
  We have presented a Spiking Convolutional Neural Network (SCNN) that incorporates retinal foveal-pit inspired Difference of Gaussian filters and rank-order encoding. The model is trained using a variant of the backpropagation algorithm adapted to work with spiking neurons, as implemented in the Nengo library. The network has achieved up to 90% accuracy, where loss is calculated using the cross-entropy function.
  arXiv  Detail & Related papers  (2021-05-29T15:28:30Z)
• Inception Convolution with Efficient Dilation Search [121.41030859447487]
  Dilation convolution is a critical mutant of standard convolution neural network to control effective receptive fields and handle large scale variance of objects. We propose a new mutant of dilated convolution, namely inception (dilated) convolution where the convolutions have independent dilation among different axes, channels and layers. We explore a practical method for fitting the complex inception convolution to the data, a simple while effective dilation search algorithm(EDO) based on statistical optimization is developed.
  arXiv  Detail & Related papers  (2020-12-25T14:58:35Z)
• EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight Transfer [82.28607779710066]
  We explore the application of neuroevolution, a form of neural architecture search inspired by biological evolution, in the design of 2D human pose networks. Our method produces network designs that are more efficient and more accurate than state-of-the-art hand-designed networks.
  arXiv  Detail & Related papers  (2020-11-17T05:56:16Z)
• Binarizing MobileNet via Evolution-based Searching [66.94247681870125]
  We propose a use of evolutionary search to facilitate the construction and training scheme when binarizing MobileNet. Inspired by one-shot architecture search frameworks, we manipulate the idea of group convolution to design efficient 1-Bit Convolutional Neural Networks (CNNs) Our objective is to come up with a tiny yet efficient binary neural architecture by exploring the best candidates of the group convolution.
  arXiv  Detail & Related papers  (2020-05-13T13:25:51Z)
• Dynamic Region-Aware Convolution [85.20099799084026]
  We propose a new convolution called Dynamic Region-Aware Convolution (DRConv), which can automatically assign multiple filters to corresponding spatial regions. On ImageNet classification, DRConv-based ShuffleNetV2-0.5x achieves state-of-the-art performance of 67.1% at 46M multiply-adds level with 6.3% relative improvement.
  arXiv  Detail & Related papers  (2020-03-27T05:49:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.