Attention mechanism has become a widely researched method to improve the performance of convolutional neural networks (CNNs). Most of the researches focus on designing channel-wise and spatial-wise attention modules but neglect the importance of unique information on each feature, which is critical for deciding both “what” and “where” to focus. In this paper, a feature-wise attention module is proposed, which can give each feature of the input feature map an attention weight. Specifically, the module is based on the well-known surround suppression in the discipline of neuroscience, and it consists of two sub-modules, Minus-Square-Add (MSA) operation and a group of learnable non-linear mapping functions. The MSA imitates the surround suppression and defines an energy function which can be applied to each feature to measure its importance. The group of non-linear functions refines the energy calculated by the MSA to more reasonable values. By these two sub-modules, feature-wise attention can be well captured. Meanwhile, due to the simple structure and few parameters of the two sub-modules, the proposed module can easily be almost integrated into any CNN. To verify the performance and effectiveness of the proposed module, several experiments were conducted on the Cifar10, Cifar100, Cinic10, and Tiny-ImageNet datasets, respectively. The experimental results demonstrate that the proposed module is flexible and effective for CNNs to improve their performance.
. [J]. Frontiers of Computer Science, 2023, 17(6): 176338.
Shuo TAN, Lei ZHANG, Xin SHU, Zizhou WANG. A feature-wise attention module based on the difference with surrounding features for convolutional neural networks. Front. Comput. Sci., 2023, 17(6): 176338.
Deng J, Dong W, Socher R, Li L J, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. In: Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2009, 248–255
2
A Krizhevsky . Learning multiple layers of features from tiny images. Toronto: University of Toronto, 2009
3
M, Everingham Gool L, van C K I, Williams J, Winn A Zisserman . The PASCAL visual object classes (VOC) challenge. International Journal of Computer Vision, 2010, 88( 2): 303–338
4
M, Cordts M, Omran S, Ramos T, Rehfeld M, Enzweiler R, Benenson U, Franke S, Roth B Schiele . The cityscapes dataset for semantic urban scene understanding. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016, 3213–3223
5
T Y, Lin M, Maire S, Belongie J, Hays P, Perona D, Ramanan P, Dollár C L Zitnick . Microsoft COCO: common objects in context. In: Proceedings of the 13th European Conference on Computer Vision. 2014, 740–755
6
J, Hu L, Shen G Sun . Squeeze-and-excitation networks. In: Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018, 7132–7141
7
S, Woo J, Park J Y, Lee I S Kweon . CBAM: convolutional block attention module. In: Proceedings of the 15th European Conference on Computer Vision. 2018, 3–19
8
X, Wang R, Girshick A, Gupta K He . Non-local neural networks. In: Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018, 7794–7803
9
F, Wang M, Jiang C, Qian S, Yang C, Li H, Zhang X, Wang X Tang . Residual attention network for image classification. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017, 6450–6458
10
L, Yang R Y, Zhang L, Li X Xie . SimAM: a simple, parameter-free attention module for convolutional neural networks. In: Proceedings of the 38th International Conference on Machine Learning. 2021, 11863–11874
11
L, Wang L, Zhang X, Qi Z Yi . Deep attention-based imbalanced image classification. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33( 8): 3320–3330
12
L, Chen H, Zhang J, Xiao L, Nie J, Shao W, Liu T S Chua . SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017, 6298–6306
13
M Carrasco . Visual attention: the past 25 years. Vision Research, 2011, 51( 13): 1484–1525
14
N, Liu J, Han M H Yang . PiCANet: learning pixel-wise contextual attention for saliency detection. In: Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018, 3089–3098
15
W, Zhang C Xiao . PCAN: 3D attention map learning using contextual information for point cloud based retrieval. In: Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, 12428–12437
16
B S, Webb N T, Dhruv S G, Solomon C, Tailby P Lennie . Early and late mechanisms of surround suppression in striate cortex of macaque. Journal of Neuroscience, 2005, 25( 50): 11666–11675
17
K, Simonyan A Zisserman . Very deep convolutional networks for large-scale image recognition. 2014, arXiv preprint arXiv: 1409.1556
18
C, Szegedy W, Liu Y, Jia P, Sermanet S, Reed D, Anguelov D, Erhan V, Vanhoucke A Rabinovich . Going deeper with convolutions. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015, 1–9
19
K, He X, Zhang S, Ren J Sun . Deep residual learning for image recognition. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016, 770–778
20
S, Zagoruyko N Komodakis . Wide residual networks. In: Proceedings of British Machine Vision Conference. 2016, 87.1–87.12
21
G, Huang Z, Liu der Maaten L, van K Q Weinberger . Densely connected convolutional networks. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017, 2261–2269
22
F Chollet . Xception: deep learning with depthwise separable convolutions. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017, 1800–1807
23
S, Xie R, Girshick P, Dollár Z, Tu K He . Aggregated residual transformations for deep neural networks. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017, 5987–5995
24
T, Domhan J T, Springenberg F Hutter . Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves. In: Proceedings of the 24th International Conference on Artificial Intelligence. 2015, 3460–3468
25
D, Ha A, Dai Q V Le . Hypernetworks. 2016, arXiv preprint arXiv: 1609.09106
26
B, Zoph Q V Le . Neural architecture search with reinforcement learning. In: Proceedings of the 5th International Conference on Learning Representations. 2017
27
H, Mendoza A, Klein M, Feurer J T, Springenberg F Hutter . Towards automatically-tuned neural networks. In: Proceedings of Workshop on Automatic Machine Learning. 2016, 58–65
28
I, Bello B, Zoph V, Vasudevan Q V Le . Neural optimizer search with reinforcement learning. In: Proceedings of the 34th International Conference on Machine Learning. 2017, 459–468
29
C, Fernando D, Banarse C, Blundell Y, Zwols D, Ha A A, Rusu A, Pritzel D Wierstra . Pathnet: Evolution channels gradient descent in super neural networks. 2017, arXiv preprint arXiv: 1701.08734
30
A G, Roy N, Navab C Wachinger . Recalibrating fully convolutional networks with spatial and channel “squeeze and excitation” blocks. IEEE Transactions on Medical Imaging, 2019, 38( 2): 540–549
31
Y, Chen Y, Kalantidis J, Li S, Yan J Feng . A2-nets: double attention networks. 2018, arXiv preprint arXiv: 1810.11579
32
J, Fu J, Liu H, Tian Y, Li Y, Bao Z, Fang H Lu . Dual attention network for scene segmentation. In: Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, 3141–3149
33
Y, Cao J, Xu S, Lin F, Wei H Hu . GCNet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop. 2019, 1971–1980
34
Z, Yang L, Zhu Y, Wu Y Yang . Gated channel transformation for visual recognition. In: Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020, 11791–11800
35
S, Ioffe C Szegedy . Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning. 2015, 448–456
36
L N, Darlow E J, Crowley A, Antoniou A J Storkey . CINIC-10 is not ImageNet or CIFAR-10. 2018, arXiv preprint arXiv: 1810.03505
37
C Y, Lee S, Xie P, Gallagher Z, Zhang Z Tu . Deeply-supervised nets. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics. 2015, 562–570
38
M, Sandler A, Howard M, Zhu A, Zhmoginov L C Chen . MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018, 4510–4520
39
Q, Wang B, Wu P, Zhu P, Li W, Zuo Q Hu . ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020, 11531–11539
40
M D, Zeiler R Fergus . Visualizing and understanding convolutional networks. In: Proceedings of the 13th European Conference on Computer Vision. 2014, 818–833
41
R R, Selvaraju M, Cogswell A, Das R, Vedantam D, Parikh D Batra . Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of 2017 IEEE International Conference on Computer Vision. 2017, 618–626
