iNL: Implicit non-local network
The attention mechanism of computer vision represented by a non-local network improves the performance of numerous vision tasks while bringing computational burden for deployment Wang et al. (2018). In this work, we explore to release the inference computation for non-local network by decoupling the...
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| Published in: | Neurocomputing (Amsterdam) Vol. 482; pp. 50 - 59 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
14.04.2022
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | The attention mechanism of computer vision represented by a non-local network improves the performance of numerous vision tasks while bringing computational burden for deployment Wang et al. (2018). In this work, we explore to release the inference computation for non-local network by decoupling the training/inference procedure. Specifically, we propose the implicit non-local network (iNL). During training, iNL models the dependency between features across long-range affinities like original non-local blocks; during inference, iNL could be reformulated as only two convolution layers but can rival non-local network. In this way, the computation complexity and the memory costs are reduced. In addition, we take a further step and extend our iNL into a more generalized form, which covers the attentions of different orders in computer vision tasks. iNL brings steady improvements on multiple benchmarks of different vision tasks including classification, detection, and instance segmentation. In the meantime, it provides a brand–new perspective to understand the attention mechanism in deep neural networks. |
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| AbstractList | The attention mechanism of computer vision represented by a non-local network improves the performance of numerous vision tasks while bringing computational burden for deployment Wang et al. (2018). In this work, we explore to release the inference computation for non-local network by decoupling the training/inference procedure. Specifically, we propose the implicit non-local network (iNL). During training, iNL models the dependency between features across long-range affinities like original non-local blocks; during inference, iNL could be reformulated as only two convolution layers but can rival non-local network. In this way, the computation complexity and the memory costs are reduced. In addition, we take a further step and extend our iNL into a more generalized form, which covers the attentions of different orders in computer vision tasks. iNL brings steady improvements on multiple benchmarks of different vision tasks including classification, detection, and instance segmentation. In the meantime, it provides a brand–new perspective to understand the attention mechanism in deep neural networks. |
| Author | Chen, Xi Zhang, Songjie Qi, Donglian Han, Yifeng |
| Author_xml | – sequence: 1 givenname: Yifeng surname: Han fullname: Han, Yifeng – sequence: 2 givenname: Xi surname: Chen fullname: Chen, Xi – sequence: 3 givenname: Songjie surname: Zhang fullname: Zhang, Songjie – sequence: 4 givenname: Donglian surname: Qi fullname: Qi, Donglian |
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context for semantic segmentation publication-title: International Journal of Computer Vision – start-page: 3726 year: 2020 ident: 10.1016/j.neucom.2022.01.047_b0140 article-title: Dynamic graph message passing networks – start-page: 10705 year: 2019 ident: 10.1016/j.neucom.2022.01.047_b0010 article-title: Attention branch network: Learning of attention mechanism for visual explanation – start-page: 3085 year: 2019 ident: 10.1016/j.neucom.2022.01.047_b0090 article-title: Pyramid feature attention network for saliency detection – start-page: 620 year: 2018 ident: 10.1016/j.neucom.2022.01.047_b0105 article-title: Video-based person re-identification via 3d convolutional networks and non-local attention – ident: 10.1016/j.neucom.2022.01.047_b0060 – start-page: 2219 year: 2019 ident: 10.1016/j.neucom.2022.01.047_b0065 article-title: Attention-based dropout layer for weakly supervised object localization – start-page: 603 year: 2019 ident: 10.1016/j.neucom.2022.01.047_b0025 article-title: Ccnet: Criss-cross attention for semantic segmentation – year: 2019 ident: 10.1016/j.neucom.2022.01.047_b0030 article-title: Gcnet: Non-local networks meet squeeze-excitation networks and beyond – start-page: 347 year: 2020 ident: 10.1016/j.neucom.2022.01.047_b0075 article-title: Mining cross-image semantics for weakly supervised semantic segmentation – start-page: 7794 year: 2018 ident: 10.1016/j.neucom.2022.01.047_b0005 article-title: Non-local neural networks – volume: 78 start-page: 20533 issue: 14 year: 2019 ident: 10.1016/j.neucom.2022.01.047_b0055 article-title: An attention mechanism based convolutional lstm network for video action recognition publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-019-7404-z – start-page: 3464 year: 2019 ident: 10.1016/j.neucom.2022.01.047_b0145 article-title: Local relation networks for image recognition – volume: 146 start-page: 77 issue: 1 year: 2003 ident: 10.1016/j.neucom.2022.01.047_b0015 article-title: Object-based visual attention for computer vision publication-title: Artificial intelligence doi: 10.1016/S0004-3702(02)00399-5 – start-page: 1605 year: 2021 ident: 10.1016/j.neucom.2022.01.047_b0130 article-title: Hoca: Higher-order channel attention for single image super-resolution – volume: 27 start-page: 2368 issue: 5 year: 2017 ident: 10.1016/j.neucom.2022.01.047_b0035 article-title: Deep visual attention prediction publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2017.2787612 |
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