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Attentional Feature Fusion
Open accessAbstract
Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures. It is often implemented via simple operations, such as summation or concatenation, but this might not be the best choice. In this work, we propose a uniform and general scheme, namely attentional feature fusion, which is applicable for most common scenarios, including feature fusion induced by short and long skip connections as well as within Inception layers. To better fuse features of inconsistent semantics and scales, we propose a multiscale channel attention module, which addresses issues that arise when fusing features given at different scales. We also demonstrate that the initial integration of feature maps can become a bottleneck and that this issue can be alleviated by adding another level of attention, which we refer to as iterative attentional feature fusion. With fewer layers or parameters, our models outperform state-of-the-art networks on both CIFAR-100 and ImageNet datasets, which suggests that more sophisticated attention mechanisms for feature fusion hold great potential to consistently yield better results compared to their direct counterparts. Our codes and trained models are available online1.
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Metadata
- Authors
- Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., & Barnard, K.
- Affiliations
- Nanjing University of Aeronautics and Astronautics (CN)
- University of Copenhagen (DK)
- University of Arizona (US)
- Venue
- 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2021-01
- Pages
- 3559-3568
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W3094897602
- Semantic Scholar
- 33667aea67898e114ca100ee3e2b2a613b8a943a
- Cited by
- 1,274
- References
- 53
- Citations by year
- 2026: 142 · 2025: 358 · 2024: 386 · 2023: 231 · 2022: 120 · 2021: 37
- FWCI
- 63.17
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- National Laboratory of Pattern Recognition
- Nanjing University of Aeronautics and Astronautics
- China Association for Science and Technology
- China Scholarship Council
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- attentional feature fusion, skip connections, attention mechanism, feature fusion
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