Attentional Feature Fusion

Dai, Yimian; Gieseke, Fabian; Oehmcke, Stefan; Wu, Yiquan; Barnard, Kobus

doi:10.1109/wacv48630.2021.00360

Cited by 1,274Advanced Neural Network ApplicationsVisual Attention and Saliency DetectionDomain Adaptation and Few-Shot Learning

Attentional Feature Fusion

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Dai, Yimian, Gieseke, Fabian, Oehmcke, Stefan, Wu, Yiquan, Barnard, Kobus·2021·2021 IEEE Winter Conference on Applications of Computer Vision (WACV)

DOI 10.1109/wacv48630.2021.00360

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Abstract

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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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)
ORCID: Dai, Yimian
Gieseke, Fabian
Oehmcke, Stefan
Wu, Yiquan
Barnard, Kobus
Venue: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
Publisher: IEEE
Published: 2021-01
Pages: 3559-3568
ISSN: 2472-6737 (ISSN-L)
DOI: 10.1109/wacv48630.2021.00360
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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