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We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks.
Gradient-based learning applied to document recognition
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A model of saliency-based visual attention for rapid scene analysis
Itti, L., Koch, C., Niebur, E.: · 1998
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The dynamic representation of scenes
Rensink, R.A.: · 2000
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Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., Shulman, G.L.: · 2002
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Learning to combine foveal glimpses with a third-order boltzmann machine
Larochelle, H., Hinton, G.E.: · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Microsoft coco: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Recurrent models of visual attention." advances in neural information processing systems
Mnih, V., Heess, N., Graves, A., et al.: · 2014
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Multiple object recognition with visual attention
Ba, J., Mnih, V., Kavukcuoglu, K.: · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., Bengio, Y.: · 2014
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Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y.: · 2015
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Draw: A recurrent neural network for image generation
Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: · 2015
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Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: · 2016
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
Bell, S., Lawrence Zitnick, C., Bala, K., Girshick, R.: · 2016
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Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: · 2017
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Deep pyramidal residual networks
Han, D., Kim, J., Kim, J.: · 2017
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Zagoruyko, S., Komodakis, N.: · 2016
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: · 2016
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Xception: Deep learning with depthwise separable convolutions
Chollet, F.: · 2016
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Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2016
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Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: · 2016
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Residual attention network for image classification
Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., Tang, X.: · 2017
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Squeeze-and-excitation networks
Hu, J., Shen, L., Sun, G.: · 2017
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Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning
Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Chua, T.S.: · 2017
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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
Zagoruyko, S., Komodakis, N.: · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
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An implementation of faster rcnn with study for region sampling
Chen, X., Gupta, A.: · 2017
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Stairnet: Top-down semantic aggregation for accurate one shot detection
Sanghyun, W., Soonmin, H., So, K.I.: · 2018
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