The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.
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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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Research works printed but not linked: 1
- zhang, 2017, S3FD: Single shot scale-invariant face detector, 2017 IEEE International Conference on Computer Vision (ICCV)
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Sorted by citations
Wu, Y., Chen, K., Zhang, T., Hui, Y., Berg-Kirkpatrick, T., & Dubnov, S. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10095969 · Cited by 459
“Where α = fAFF (X global, X a local) is a factor obtained by attention feature fusion (AFF) [21], a two-branch CNN model for learning the fusion factor of two inputs.”
MethodSelf-citationContext-aware Cross-level Fusion Network for Camouflaged Object Detection
Sun, Y., Chen, G., Zhou, T., Zhang, Y., & Liu, N. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/142 · Cited by 378
Changer: Feature Interaction is What You Need for Change Detection
Fang, S. E., Li, K., & Li, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3277496 · Cited by 312
Camouflaged Object Detection via Context-Aware Cross-Level Fusion
Chen, G., Liu, S.-J., Sun, Y.-J., Ji, G.-P., Wu, Y., & Zhou, T. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3178173 · Cited by 277
Self-citationI Can Find You! Boundary-Guided Separated Attention Network for Camouflaged Object Detection
Zhu, H., Li, P., Xie, H., Yan, X., Liang, D., Chen, D., Wei, M., & Qin, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20273 · Cited by 268
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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