M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection Article Swipe
Yuan Yao
,
Pan Gao
,
Xiaoyang Tan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.08365
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.08365
Most existing salient object detection methods mostly use U-Net or feature pyramid structure, which simply aggregates feature maps of different scales, ignoring the uniqueness and interdependence of them and their respective contributions to the final prediction. To overcome these, we propose the M$^3$Net, i.e., the Multilevel, Mixed and Multistage attention network for Salient Object Detection (SOD). Firstly, we propose Multiscale Interaction Block which innovatively introduces the cross-attention approach to achieve the interaction between multilevel features, allowing high-level features to guide low-level feature learning and thus enhancing salient regions. Secondly, considering the fact that previous Transformer based SOD methods locate salient regions only using global self-attention while inevitably overlooking the details of complex objects, we propose the Mixed Attention Block. This block combines global self-attention and window self-attention, aiming at modeling context at both global and local levels to further improve the accuracy of the prediction map. Finally, we proposed a multilevel supervision strategy to optimize the aggregated feature stage-by-stage. Experiments on six challenging datasets demonstrate that the proposed M$^3$Net surpasses recent CNN and Transformer-based SOD arts in terms of four metrics. Codes are available at https://github.com/I2-Multimedia-Lab/M3Net.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.08365
- https://arxiv.org/pdf/2309.08365
- OA Status
- green
- Cited By
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386841537
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386841537Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.08365Digital Object Identifier
- Title
-
M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-15Full publication date if available
- Authors
-
Yuan Yao, Pan Gao, Xiaoyang TanList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.08365Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.08365Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2309.08365Direct OA link when available
- Concepts
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Salient, Computer science, Feature (linguistics), Block (permutation group theory), Artificial intelligence, Transformer, Pattern recognition (psychology), Data mining, Machine learning, Mathematics, Engineering, Electrical engineering, Philosophy, Linguistics, Voltage, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 4Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.introduces | 64 |
| abstract_inverted_index.multilevel | 73, 150 |
| abstract_inverted_index.prediction | 144 |
| abstract_inverted_index.respective | 30 |
| abstract_inverted_index.structure, | 12 |
| abstract_inverted_index.uniqueness | 23 |
| abstract_inverted_index.Experiments | 159 |
| abstract_inverted_index.Interaction | 60 |
| abstract_inverted_index.Multilevel, | 45 |
| abstract_inverted_index.Transformer | 94 |
| abstract_inverted_index.challenging | 162 |
| abstract_inverted_index.considering | 89 |
| abstract_inverted_index.demonstrate | 164 |
| abstract_inverted_index.interaction | 71 |
| abstract_inverted_index.overlooking | 107 |
| abstract_inverted_index.prediction. | 35 |
| abstract_inverted_index.supervision | 151 |
| abstract_inverted_index.innovatively | 63 |
| abstract_inverted_index.contributions | 31 |
| abstract_inverted_index.self-attention | 104, 123 |
| abstract_inverted_index.cross-attention | 66 |
| abstract_inverted_index.interdependence | 25 |
| abstract_inverted_index.self-attention, | 126 |
| abstract_inverted_index.stage-by-stage. | 158 |
| abstract_inverted_index.Transformer-based | 173 |
| abstract_inverted_index.https://github.com/I2-Multimedia-Lab/M3Net. | 185 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |