Stochastic Region Pooling: Make Attention More Expressive Article Swipe
Mingnan Luo
,
Guihua Wen
,
Yang Hu
,
Dan Dai
,
Yingxue Xu
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1904.09853
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1904.09853
Global Average Pooling (GAP) is used by default on the channel-wise attention mechanism to extract channel descriptors. However, the simple global aggregation method of GAP is easy to make the channel descriptors have homogeneity, which weakens the detail distinction between feature maps, thus affecting the performance of the attention mechanism. In this work, we propose a novel method for channel-wise attention network, called Stochastic Region Pooling (SRP), which makes the channel descriptors more representative and diversity by encouraging the feature map to have more or wider important feature responses. Also, SRP is the general method for the attention mechanisms without any additional parameters or computation. It can be widely applied to attention networks without modifying the network structure. Experimental results on image recognition datasets including CIAFR-10/100, ImageNet and three Fine-grained datasets (CUB-200-2011, Stanford Cars and Stanford Dogs) show that SRP brings the significant improvements of the performance over efficient CNNs and achieves the state-of-the-art results.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1904.09853
- https://arxiv.org/pdf/1904.09853
- OA Status
- green
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2937120648
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https://openalex.org/W2937120648Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1904.09853Digital Object Identifier
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Stochastic Region Pooling: Make Attention More ExpressiveWork title
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-04-22Full publication date if available
- Authors
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Mingnan Luo, Guihua Wen, Yang Hu, Dan Dai, Yingxue XuList of authors in order
- Landing page
-
https://arxiv.org/abs/1904.09853Publisher landing page
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https://arxiv.org/pdf/1904.09853Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1904.09853Direct OA link when available
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Pooling, Computer science, Feature (linguistics), Artificial intelligence, Computation, Channel (broadcasting), Attention network, Pattern recognition (psychology), Mechanism (biology), Machine learning, Algorithm, Linguistics, Epistemology, Philosophy, Computer networkTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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48Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.networks | 112 |
| abstract_inverted_index.results. | 154 |
| abstract_inverted_index.affecting | 43 |
| abstract_inverted_index.attention | 11, 48, 60, 97, 111 |
| abstract_inverted_index.diversity | 75 |
| abstract_inverted_index.efficient | 148 |
| abstract_inverted_index.important | 86 |
| abstract_inverted_index.including | 124 |
| abstract_inverted_index.mechanism | 12 |
| abstract_inverted_index.modifying | 114 |
| abstract_inverted_index.Stochastic | 63 |
| abstract_inverted_index.additional | 101 |
| abstract_inverted_index.mechanism. | 49 |
| abstract_inverted_index.mechanisms | 98 |
| abstract_inverted_index.parameters | 102 |
| abstract_inverted_index.responses. | 88 |
| abstract_inverted_index.structure. | 117 |
| abstract_inverted_index.aggregation | 21 |
| abstract_inverted_index.descriptors | 31, 71 |
| abstract_inverted_index.distinction | 38 |
| abstract_inverted_index.encouraging | 77 |
| abstract_inverted_index.performance | 45, 146 |
| abstract_inverted_index.recognition | 122 |
| abstract_inverted_index.significant | 142 |
| abstract_inverted_index.Experimental | 118 |
| abstract_inverted_index.Fine-grained | 129 |
| abstract_inverted_index.channel-wise | 10, 59 |
| abstract_inverted_index.computation. | 104 |
| abstract_inverted_index.descriptors. | 16 |
| abstract_inverted_index.homogeneity, | 33 |
| abstract_inverted_index.improvements | 143 |
| abstract_inverted_index.CIAFR-10/100, | 125 |
| abstract_inverted_index.(CUB-200-2011, | 131 |
| abstract_inverted_index.representative | 73 |
| abstract_inverted_index.state-of-the-art | 153 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile |