The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer
Abstract
This study proposes a novel general image fusion framework based on cross-domain long-range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention-guided cross-domain module is devised to achieve sufficient integration of complementary information and global interaction. More specifically, the proposed method involves an intra-domain fusion unit based on self-attention and an inter-domain fusion unit based on cross-attention, which mine and integrate long dependencies within the same domain and across domains. Through long-range dependency modeling, the network is able to fully implement domain-specific information extraction and cross-domain complementary information integration as well as maintaining the appropriate apparent intensity from a global perspective. In particular, we introduce the shifted windows mechanism into the self-attention and cross-attention, which allows our model to receive images with arbitrary sizes. On the other hand, the multi-scene image fusion problems are generalized to a unified framework with structure maintenance, detail preservation, and proper intensity control. Moreover, an elaborate loss function, consisting of SSIM loss, texture loss, and intensity loss, drives the network to preserve abundant texture details and structural information, as well as presenting optimal apparent intensity. Extensive experiments on both multi-modal image fusion and digital photography image fusion demonstrate the superiority of our SwinFusion compared to the state-of-the-art unified image fusion algorithms and task-specific alternatives. Implementation code and pre-trained weights can be accessed at https://github.com/Linfeng-Tang/SwinFusion.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · arXiv · doi:10.48550/arxiv.2005.12872 · Cited by 18,253
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,281
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
SwinIR: Image Restoration Using Swin Transformer
Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., & Timofte, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00210 · Cited by 4,734
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H. S., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00681 · Cited by 4,008 · cited 1×
“There are many Transformer-based models that have achieved impressive performance in various vision tasks, such as visual recognition [29], [63], [64], object detection [30], [65], [66], tracking [67]–[69], segmentation [31], [70], and image restoration [32], [33], [71].”
BackgroundPre-Trained Image Processing Transformer
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01212 · Cited by 2,308
Cited by
Dif-Fusion: Toward High Color Fidelity in Infrared and Visible Image Fusion With Diffusion Models
Yue, J., Fang, L., Xia, S., Deng, Y., & Ma, J. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3322046 · Cited by 304
Self-citationMetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding from Object Detection
Zhao, W., Xie, S., Zhao, F., He, Y., & Lu, H. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01341 · Cited by 259
The Static Fusion Paradox: A Degradation-Centric Survey of Multimodal Perception in Adverse Weather
Godugu, S. A. (2026) · 2026 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/iv66570.2026.11624098
Metadata
- Authors
- Ma, J., Tang, L., Fan, F., Huang, J., Mei, X., & Ma, Y.
- Venue
- IEEE/CAA Journal of Automatica Sinica
- Published
- 2022-07
- Volume
- 9
- Issue
- 7
- Pages
- 1200-1217
- ISSN
- 2329-9266 (ISSN-L)
- 2329-9274
- OpenAlex
- W4283732315
- Cited by
- 1,526
- References
- 83
- FWCI
- 106.48
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer. https://app.cochat.ai/papers/10.1109/jas.2022.105686
- The paper
- Ma, J., Tang, L., Fan, F., Huang, J., Mei, X., & Ma, Y. (2022). SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer. IEEE/CAA Journal of Automatica Sinica. https://doi.org/10.1109/jas.2022.105686
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
