Linear Context Transform Block Article Swipe
Dongsheng Ruan
,
Jun Wen
,
Nenggan Zheng
,
Min Zheng
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v34i04.6007
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v34i04.6007
Squeeze-and-Excitation (SE) block presents a channel attention mechanism for modeling global context via explicitly capturing dependencies across channels. However, we are still far from understanding how the SE block works. In this work, we first revisit the SE block, and then present a detailed empirical study of the relationship between global context and attention distribution, based on which we propose a simple yet effective module, called Linear Context Transform (LCT) block. We divide all channels into different groups and normalize the globally aggregated context features within each channel group, reducing the disturbance from irrelevant channels. Through linear transform of the normalized context features, we model global context for each channel independently. The LCT block is extremely lightweight and easy to be plugged into different backbone models while with negligible parameters and computational burden increase. Extensive experiments show that the LCT block outperforms the SE block in image classification task on the ImageNet and object detection/segmentation on the COCO dataset with different backbone models. Moreover, LCT yields consistent performance gains over existing state-of-the-art detection architectures, e.g., 1.5∼1.7% APbbox and 1.0%∼1.2% APmask improvements on the COCO benchmark, irrespective of different baseline models of varied capacities. We hope our simple yet effective approach will shed some light on future research of attention-based models.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v34i04.6007
- https://ojs.aaai.org/index.php/AAAI/article/download/6007/5863
- OA Status
- diamond
- Cited By
- 19
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2997890315
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2997890315Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1609/aaai.v34i04.6007Digital Object Identifier
- Title
-
Linear Context Transform BlockWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-03Full publication date if available
- Authors
-
Dongsheng Ruan, Jun Wen, Nenggan Zheng, Min ZhengList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v34i04.6007Publisher landing page
- PDF URL
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https://ojs.aaai.org/index.php/AAAI/article/download/6007/5863Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ojs.aaai.org/index.php/AAAI/article/download/6007/5863Direct OA link when available
- Concepts
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Block (permutation group theory), Context (archaeology), Benchmark (surveying), Computer science, Channel (broadcasting), Segmentation, Artificial intelligence, Object detection, Algorithm, Pattern recognition (psychology), Mathematics, Telecommunications, Geometry, Cartography, Geography, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 6, 2023: 2, 2022: 2, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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37Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2772955562, https://openalex.org/W2550553598, https://openalex.org/W6754852571, https://openalex.org/W1677182931, https://openalex.org/W6735463952, https://openalex.org/W2898732869, https://openalex.org/W6760640297, https://openalex.org/W6730903564, https://openalex.org/W2613718673, https://openalex.org/W2284050935, https://openalex.org/W2901606971, https://openalex.org/W6749954789, https://openalex.org/W2724359148, https://openalex.org/W2798791651, https://openalex.org/W2622263826, https://openalex.org/W4288325606, https://openalex.org/W2963515894, https://openalex.org/W2997225633, https://openalex.org/W2883780447, https://openalex.org/W2884585870, https://openalex.org/W2922509574, https://openalex.org/W2502312327, https://openalex.org/W2964241181, https://openalex.org/W639708223, https://openalex.org/W2955058313, https://openalex.org/W4394666973, https://openalex.org/W2565639579, https://openalex.org/W2982220924, https://openalex.org/W2963125010, https://openalex.org/W2117539524, https://openalex.org/W2981689412, https://openalex.org/W2194775991, https://openalex.org/W1861492603, https://openalex.org/W1836465849, https://openalex.org/W2945164022, https://openalex.org/W4394645124, https://openalex.org/W2097117768 |
| referenced_works_count | 37 |
| abstract_inverted_index.a | 4, 42, 60 |
| abstract_inverted_index.In | 30 |
| abstract_inverted_index.SE | 27, 37, 143 |
| abstract_inverted_index.We | 71, 193 |
| abstract_inverted_index.be | 120 |
| abstract_inverted_index.in | 145 |
| abstract_inverted_index.is | 114 |
| abstract_inverted_index.of | 46, 98, 186, 190, 207 |
| abstract_inverted_index.on | 56, 149, 155, 181, 204 |
| abstract_inverted_index.to | 119 |
| abstract_inverted_index.we | 19, 33, 58, 103 |
| abstract_inverted_index.LCT | 112, 139, 164 |
| abstract_inverted_index.The | 111 |
| abstract_inverted_index.all | 73 |
| abstract_inverted_index.and | 39, 52, 78, 117, 130, 152, 177 |
| abstract_inverted_index.are | 20 |
| abstract_inverted_index.far | 22 |
| abstract_inverted_index.for | 8, 107 |
| abstract_inverted_index.how | 25 |
| abstract_inverted_index.our | 195 |
| abstract_inverted_index.the | 26, 36, 47, 80, 90, 99, 138, 142, 150, 156, 182 |
| abstract_inverted_index.via | 12 |
| abstract_inverted_index.yet | 62, 197 |
| abstract_inverted_index.(SE) | 1 |
| abstract_inverted_index.COCO | 157, 183 |
| abstract_inverted_index.each | 86, 108 |
| abstract_inverted_index.easy | 118 |
| abstract_inverted_index.from | 23, 92 |
| abstract_inverted_index.hope | 194 |
| abstract_inverted_index.into | 75, 122 |
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| abstract_inverted_index.some | 202 |
| abstract_inverted_index.task | 148 |
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| abstract_inverted_index.then | 40 |
| abstract_inverted_index.this | 31 |
| abstract_inverted_index.will | 200 |
| abstract_inverted_index.with | 127, 159 |
| abstract_inverted_index.(LCT) | 69 |
| abstract_inverted_index.based | 55 |
| abstract_inverted_index.block | 2, 28, 113, 140, 144 |
| abstract_inverted_index.e.g., | 174 |
| abstract_inverted_index.first | 34 |
| abstract_inverted_index.gains | 168 |
| abstract_inverted_index.image | 146 |
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| abstract_inverted_index.model | 104 |
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| abstract_inverted_index.study | 45 |
| abstract_inverted_index.which | 57 |
| abstract_inverted_index.while | 126 |
| abstract_inverted_index.work, | 32 |
| abstract_inverted_index.APbbox | 176 |
| abstract_inverted_index.APmask | 179 |
| abstract_inverted_index.Linear | 66 |
| abstract_inverted_index.across | 16 |
| abstract_inverted_index.block, | 38 |
| abstract_inverted_index.block. | 70 |
| abstract_inverted_index.burden | 132 |
| abstract_inverted_index.called | 65 |
| abstract_inverted_index.divide | 72 |
| abstract_inverted_index.future | 205 |
| abstract_inverted_index.global | 10, 50, 105 |
| abstract_inverted_index.group, | 88 |
| abstract_inverted_index.groups | 77 |
| abstract_inverted_index.linear | 96 |
| abstract_inverted_index.models | 125, 189 |
| abstract_inverted_index.object | 153 |
| abstract_inverted_index.simple | 61, 196 |
| abstract_inverted_index.varied | 191 |
| abstract_inverted_index.within | 85 |
| abstract_inverted_index.works. | 29 |
| abstract_inverted_index.yields | 165 |
| abstract_inverted_index.Context | 67 |
| abstract_inverted_index.Through | 95 |
| abstract_inverted_index.between | 49 |
| abstract_inverted_index.channel | 5, 87, 109 |
| abstract_inverted_index.context | 11, 51, 83, 101, 106 |
| abstract_inverted_index.dataset | 158 |
| abstract_inverted_index.models. | 162, 209 |
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| abstract_inverted_index.propose | 59 |
| abstract_inverted_index.revisit | 35 |
| abstract_inverted_index.However, | 18 |
| abstract_inverted_index.ImageNet | 151 |
| abstract_inverted_index.approach | 199 |
| abstract_inverted_index.backbone | 124, 161 |
| abstract_inverted_index.baseline | 188 |
| abstract_inverted_index.channels | 74 |
| abstract_inverted_index.detailed | 43 |
| abstract_inverted_index.existing | 170 |
| abstract_inverted_index.features | 84 |
| abstract_inverted_index.globally | 81 |
| abstract_inverted_index.modeling | 9 |
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| abstract_inverted_index.Extensive | 134 |
| abstract_inverted_index.Moreover, | 163 |
| abstract_inverted_index.Transform | 68 |
| abstract_inverted_index.attention | 6, 53 |
| abstract_inverted_index.capturing | 14 |
| abstract_inverted_index.channels. | 17, 94 |
| abstract_inverted_index.detection | 172 |
| abstract_inverted_index.different | 76, 123, 160, 187 |
| abstract_inverted_index.effective | 63, 198 |
| abstract_inverted_index.empirical | 44 |
| abstract_inverted_index.extremely | 115 |
| abstract_inverted_index.features, | 102 |
| abstract_inverted_index.increase. | 133 |
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| abstract_inverted_index.aggregated | 82 |
| abstract_inverted_index.benchmark, | 184 |
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| abstract_inverted_index.explicitly | 13 |
| abstract_inverted_index.irrelevant | 93 |
| abstract_inverted_index.negligible | 128 |
| abstract_inverted_index.normalized | 100 |
| abstract_inverted_index.parameters | 129 |
| abstract_inverted_index.1.0%∼1.2% | 178 |
| abstract_inverted_index.capacities. | 192 |
| abstract_inverted_index.disturbance | 91 |
| abstract_inverted_index.experiments | 135 |
| abstract_inverted_index.lightweight | 116 |
| abstract_inverted_index.outperforms | 141 |
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| abstract_inverted_index.improvements | 180 |
| abstract_inverted_index.irrespective | 185 |
| abstract_inverted_index.relationship | 48 |
| abstract_inverted_index.computational | 131 |
| abstract_inverted_index.distribution, | 54 |
| abstract_inverted_index.understanding | 24 |
| abstract_inverted_index.architectures, | 173 |
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| abstract_inverted_index.independently. | 110 |
| abstract_inverted_index.attention-based | 208 |
| abstract_inverted_index.state-of-the-art | 171 |
| abstract_inverted_index.Squeeze-and-Excitation | 0 |
| abstract_inverted_index.detection/segmentation | 154 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.80731225 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |