mlsanityData to 7 Oct 2026

DMSANet: Dual Multi Scale Attention Network

arXiv 2106.08382 ↗First posted 13 Jun 2021cs.CV cs.LGcited by 8

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Abstract (arXiv)

Attention mechanism of late has been quite popular in the computer vision community. A lot of work has been done to improve the performance of the network, although almost always it results in increased computational complexity. In this paper, we propose a new attention module that not only achieves the best performance but also has lesser parameters compared to most existing models. Our attention module can easily be integrated with other convolutional neural networks because of its lightweight nature. The proposed network named Dual Multi Scale Attention Network (DMSANet) is comprised of two parts: the first part is used to extract features at various scales and aggregate them, the second part uses spatial and channel attention modules in parallel to adaptively integrate local features with their global dependencies. We benchmark our network performance for Image Classification on ImageNet dataset, Object Detection and Instance Segmentation both on MS COCO dataset.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 29 of its 34 references that are in the map. First posted on arXiv 13 Jun 2021.

Builds on

29 of 34 references.

References · 29

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  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
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17 more
  1. 2018Dual Attention Network for Scene Segmentationcited by 1,001
  2. 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 572
  3. 2019Res2Net: A New Multi-scale Backbone Architecturecited by 468
  4. 2020ResNeSt: Split-Attention Networkscited by 445
  5. 2019Selective Kernel Networkscited by 401
  6. 2017Residual Attention Network for Image Classificationcited by 385
  7. 2019Attention Augmented Convolutional Networkscited by 363
  8. 2019GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyondcited by 331
  9. 2018Gather-Excite: Exploiting Feature Context in Convolutional Neural Networkscited by 138
  10. 2018Attention Branch Network: Learning of Attention Mechanism for Visual Explanationcited by 108
  11. 2019Squeeze-and-Attention Networks for Semantic Segmentationcited by 60
  12. 2021SA-Net: Shuffle Attention for Deep Convolutional Neural Networkscited by 54
  13. 2020Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognitioncited by 25
  14. 2018ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutionscited by 10
  15. 2020Monocular Depth Estimation Using Multi Scale Neural Network And Feature Fusioncited by 7
  16. 2020Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Carscited by 6
  17. 2020Bayesian Multi-Scale Neural Network for Crowd Countingcited by 1

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 2.

8 papers.

  1. 2024Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attentioncited by 26
  2. 2022Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesiscited by 13
  3. 2024LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagerycited by 7
  4. 2022ViTBIS: Vision Transformer for Biomedical Image Segmentationcited by 5
  5. 2023LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing imagescited by 3
  6. 2021AA3DNet: Attention Augmented Real Time 3D Object Detectioncited by 1
  7. 2021AASeg: Attention Aware Network for Real Time Semantic Segmentationcited by 1
  8. 2022Superresolution and Segmentation of OCT scans using Multi-Stage adversarial Guided Attention Trainingcited by 0