{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T16:10:04Z","timestamp":1788883804181,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":65,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032374462","type":"print"},{"value":"9783032374479","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-37447-9_30","type":"book-chapter","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T15:46:44Z","timestamp":1788882404000},"page":"542-561","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["P$$^2$$Fusion: Prompt-Based Progressive Infrared-Visible Image Fusion via\u00a0Dual-Prior Distillation"],"prefix":"10.1007","author":[{"given":"Yi","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huichao","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaihui","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruitao","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizhe","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junwei","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingwen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"30_CR1","doi-asserted-by":"publisher","first-page":"3139","DOI":"10.1109\/TIP.2025.3571339","volume":"34","author":"J Chen","year":"2025","unstructured":"Chen, J., Yang, L., Yu, W., Gong, W., Cai, Z., Ma, J.: Sdsfusion: a semantic-aware infrared and visible image fusion network for degraded scenes. IEEE Trans. Image Process. 34, 3139\u20133153 (2025). https:\/\/doi.org\/10.1109\/TIP.2025.3571339","journal-title":"IEEE Trans. Image Process."},{"issue":"5","key":"30_CR2","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/JSEN.2007.894926","volume":"7","author":"N Cvejic","year":"2007","unstructured":"Cvejic, N., Bull, D., Canagarajah, N.: Region-based multimodal image fusion using ica bases. IEEE Sens. J. 7(5), 743\u2013751 (2007). https:\/\/doi.org\/10.1109\/JSEN.2007.894926","journal-title":"IEEE Sens. J."},{"key":"30_CR3","doi-asserted-by":"publisher","unstructured":"Feng, J., Wu, A., Zheng, W.S.: Shape-erased feature learning for visible-infrared person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22752\u201322761 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.02179","DOI":"10.1109\/CVPR52729.2023.02179"},{"issue":"9","key":"30_CR4","doi-asserted-by":"publisher","first-page":"4224","DOI":"10.1109\/TII.2018.2822828","volume":"14","author":"H Gao","year":"2018","unstructured":"Gao, H., Cheng, B., Wang, J., Li, K., Zhao, J., Li, D.: Object classification using cnn-based fusion of vision and lidar in autonomous vehicle environment. IEEE Trans. Industr. Inf. 14(9), 4224\u20134231 (2018). https:\/\/doi.org\/10.1109\/TII.2018.2822828","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"2","key":"30_CR5","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1109\/TCSVT.2022.3206807","volume":"33","author":"Y Gao","year":"2023","unstructured":"Gao, Y., Ma, S., Liu, J.: Dcdr-gan: a densely connected disentangled representation generative adversarial network for infrared and visible image fusion. IEEE Trans. Circuits Syst. Video Technol. 33(2), 549\u2013561 (2023). https:\/\/doi.org\/10.1109\/TCSVT.2022.3206807","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"30_CR6","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s11263-025-02627-9","volume":"134","author":"Y Gao","year":"2026","unstructured":"Gao, Y., et al.: Cosurfgs: 3d surface gaussian splatting with collaborative distributed learning for large-scale scene reconstruction. Int. J. Comput. Vision 134, 195 (2026). https:\/\/doi.org\/10.1007\/s11263-025-02627-9","journal-title":"Int. J. Comput. Vision"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Su, G., Huang, Y., He, C.Y., Z.: Cmfs: clip-guided modality interaction for mitigating noise in multi-modal image fusion and segmentation. In: Proceedings of the Thirty-four International Joint Conference on Artificial Intelligence (2025)","DOI":"10.24963\/ijcai.2025\/209"},{"key":"30_CR8","doi-asserted-by":"publisher","unstructured":"Ha, Q., Watanabe, K., Karasawa, T., Ushiku, Y., Harada, T.: Mfnet: towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes. In: Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 5108\u20135115 (2017). https:\/\/doi.org\/10.1109\/IROS.2017.8206396","DOI":"10.1109\/IROS.2017.8206396"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Huang, Z., Liu, J., Fan, X., Liu, R., Zhong, W., Luo, Z.: Reconet: recurrent correction network for fast and efficient multi-modality image fusion. In: Computer Vision \u2013 ECCV 2022, pp. 539\u2013555. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-19797-0_31"},{"key":"30_CR10","doi-asserted-by":"publisher","unstructured":"Ji, D., Wang, H., Hu, H., Gan, W., Wu, W., Yan, J.: Context-aware graph convolution network for target re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1646\u20131654 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i2.16257","DOI":"10.1609\/aaai.v35i2.16257"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything (2023). arXiv:2304.02643","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"30_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2026.3701287","volume":"01","author":"H Li","year":"2026","unstructured":"Li, H., et al.: Langsurf: language-embedded surface gaussians for 3d scene understanding. IEEE Trans. Pattern Anal. Mach. Intell. 01, 1\u201313 (2026). https:\/\/doi.org\/10.1109\/TPAMI.2026.3701287","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"30_CR13","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","volume":"28","author":"H Li","year":"2019","unstructured":"Li, H., Wu, X.J.: Densefuse: a fusion approach to infrared and visible images. IEEE Trans. Image Process. 28(5), 2614\u20132623 (2019). https:\/\/doi.org\/10.1109\/TIP.2018.2887342","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR14","doi-asserted-by":"publisher","unstructured":"Li, H., Wu, X.J., Kittler, J.: Rfn-nest: an end-to-end residual fusion network for infrared and visible images. Inf. Fusion 73, 72\u201386 (2021). https:\/\/doi.org\/10.1016\/j.inffus.2021.02.023, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253521000440","DOI":"10.1016\/j.inffus.2021.02.023"},{"issue":"9","key":"30_CR15","doi-asserted-by":"publisher","first-page":"11040","DOI":"10.1109\/TPAMI.2023.3268209","volume":"45","author":"H Li","year":"2023","unstructured":"Li, H., Xu, T., Wu, X.J., Lu, J., Kittler, J.: Lrrnet: a novel representation learning guided fusion network for infrared and visible images. IEEE Trans. Pattern Anal. Mach. Intell. 45(9), 11040\u201311052 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2023.3268209","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR16","doi-asserted-by":"publisher","unstructured":"Li, J., Jiang, C., Jiang, J., Liang, P., Ma, J., Nie, L.: Towards unified semantic and controllable image fusion: a diffusion transformer approach. IEEE Trans. Pattern Anal. Mach. Intell. 1\u201318 (2025). https:\/\/doi.org\/10.1109\/TPAMI.2025.3642842","DOI":"10.1109\/TPAMI.2025.3642842"},{"issue":"6","key":"30_CR17","doi-asserted-by":"publisher","first-page":"7458","DOI":"10.1109\/JSEN.2019.2921803","volume":"21","author":"Q Li","year":"2021","unstructured":"Li, Q., et al.: Coupled gan with relativistic discriminators for infrared and visible images fusion. IEEE Sens. J. 21(6), 7458\u20137467 (2021). https:\/\/doi.org\/10.1109\/JSEN.2019.2921803","journal-title":"IEEE Sens. J."},{"key":"30_CR18","doi-asserted-by":"publisher","unstructured":"Liu, J., et al.: Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5792\u20135801 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00571","DOI":"10.1109\/CVPR52688.2022.00571"},{"issue":"1","key":"30_CR19","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/TCSVT.2021.3056725","volume":"32","author":"J Liu","year":"2022","unstructured":"Liu, J., Fan, X., Jiang, J., Liu, R., Luo, Z.: Learning a deep multi-scale feature ensemble and an edge-attention guidance for image fusion. IEEE Trans. Circuits Syst. Video Technol. 32(1), 105\u2013119 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2021.3056725","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"3","key":"30_CR20","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1109\/JAS.2024.124878","volume":"12","author":"J Liu","year":"2025","unstructured":"Liu, J., Li, X., Wang, Z., Jiang, Z., Zhong, W., Fan, W., Xu, B.: Promptfusion: harmonized semantic prompt learning for infrared and visible image fusion. IEEE\/CAA J. Automatica Sinica 12(3), 502\u2013515 (2025). https:\/\/doi.org\/10.1109\/JAS.2024.124878","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"30_CR21","doi-asserted-by":"publisher","unstructured":"Liu, J., Lin, R., Wu, G., Liu, R., Luo, Z., Fan, X.: Coconet: coupled contrastive learning network with multi-level feature ensemble for multi-modality image fusion. Int. J. Comput. Vis. 132(5), 1748\u20131775 (2023). https:\/\/doi.org\/10.1007\/s11263-023-01952-1","DOI":"10.1007\/s11263-023-01952-1"},{"key":"30_CR22","doi-asserted-by":"publisher","unstructured":"Liu, J., et al.: Multi-interactive feature learning and a full-time multi-modality benchmark for image fusion and segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8081\u20138090 (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.00745","DOI":"10.1109\/ICCV51070.2023.00745"},{"issue":"4","key":"30_CR23","doi-asserted-by":"publisher","first-page":"2349","DOI":"10.1109\/TPAMI.2024.3521416","volume":"47","author":"J Liu","year":"2025","unstructured":"Liu, J., et al.: Infrared and visible image fusion: from data compatibility to task adaption. IEEE Trans. Pattern Anal. Mach. Intell. 47(4), 2349\u20132369 (2025). https:\/\/doi.org\/10.1109\/TPAMI.2024.3521416","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR24","doi-asserted-by":"publisher","unstructured":"Liu, J., et al.: Dcevo: discriminative cross-dimensional evolutionary learning for infrared and visible image fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2226\u20132235 (2025). https:\/\/doi.org\/10.1109\/CVPR52734.2025.00213","DOI":"10.1109\/CVPR52734.2025.00213"},{"key":"30_CR25","doi-asserted-by":"publisher","unstructured":"Liu, R., Liu, Z., Liu, J., Fan, X.: Searching a hierarchically aggregated fusion architecture for fast multi-modality image fusion. In: Proceedings of the 29th ACM International Conference on Multimedia, MM 2021, pp. 1600\u20131608. ACM, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3474085.3475299","DOI":"10.1145\/3474085.3475299"},{"issue":"10","key":"30_CR26","doi-asserted-by":"publisher","first-page":"6594","DOI":"10.1109\/TPAMI.2024.3382308","volume":"46","author":"R Liu","year":"2024","unstructured":"Liu, R., Liu, Z., Liu, J., Fan, X., Luo, Z.: A task-guided, implicitly-searched and meta-initialized deep model for image fusion. IEEE Trans. Pattern Anal. Mach. Intell. 46(10), 6594\u20136609 (2024). https:\/\/doi.org\/10.1109\/TPAMI.2024.3382308","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9992\u201310002 (2021). https:\/\/api.semanticscholar.org\/CorpusID:232352874","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"30_CR28","doi-asserted-by":"publisher","unstructured":"Long, Y., Jia, H., Zhong, Y., Jiang, Y., Jia, Y.: Rxdnfuse: a aggregated residual dense network for infrared and visible image fusion. Inf. Fusion 69, 128\u2013141 (2021). https:\/\/doi.org\/10.1016\/j.inffus.2020.11.009, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253520304152","DOI":"10.1016\/j.inffus.2020.11.009"},{"key":"30_CR29","doi-asserted-by":"publisher","unstructured":"Ma, J., et al.: Infrared and visible image fusion via detail preserving adversarial learning. Inf. Fusion 54, 85\u201398 (2020). https:\/\/doi.org\/10.1016\/j.inffus.2019.07.005, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253519300314","DOI":"10.1016\/j.inffus.2019.07.005"},{"issue":"7","key":"30_CR30","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","volume":"9","author":"J Ma","year":"2022","unstructured":"Ma, J., Tang, L., Fan, F., Huang, J., Mei, X., Ma, Y.: Swinfusion: cross-domain long-range learning for general image fusion via swin transformer. IEEE\/CAA J. Automatica Sinica 9(7), 1200\u20131217 (2022). https:\/\/doi.org\/10.1109\/JAS.2022.105686","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"30_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3075747","volume":"70","author":"J Ma","year":"2021","unstructured":"Ma, J., Tang, L., Xu, M., Zhang, H., Xiao, G.: Stdfusionnet: An infrared and visible image fusion network based on salient target detection. IEEE Trans. Instrum. Meas. 70, 1\u201313 (2021). https:\/\/doi.org\/10.1109\/TIM.2021.3075747","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"30_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.3038013","volume":"70","author":"J Ma","year":"2021","unstructured":"Ma, J., Zhang, H., Shao, Z., Liang, P., Xu, H.: Ganmcc: a generative adversarial network with multiclassification constraints for infrared and visible image fusion. IEEE Trans. Instrum. Meas. 70, 1\u201314 (2021). https:\/\/doi.org\/10.1109\/TIM.2020.3038013","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"12","key":"30_CR33","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","volume":"21","author":"A Mittal","year":"2012","unstructured":"Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695\u20134708 (2012). https:\/\/doi.org\/10.1109\/TIP.2012.2214050","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"30_CR34","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1109\/TIP.2004.823821","volume":"13","author":"V Petrovic","year":"2004","unstructured":"Petrovic, V., Xydeas, C.: Gradient-based multiresolution image fusion. IEEE Trans. Image Process. 13(2), 228\u2013237 (2004). https:\/\/doi.org\/10.1109\/TIP.2004.823821","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR35","doi-asserted-by":"publisher","unstructured":"Piella, G.: A general framework for multiresolution image fusion: from pixels to regions. Inf. Fusion 4(4), 259\u2013280 (2003). https:\/\/doi.org\/10.1016\/S1566-2535(03)00046-0, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253503000460","DOI":"10.1016\/S1566-2535(03)00046-0"},{"key":"30_CR36","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: Proceedings of the International Conference on Machine Learning (2021)"},{"key":"30_CR37","doi-asserted-by":"publisher","unstructured":"Rao, D., Xu, T., Wu, X.J.: Tgfuse: an infrared and visible image fusion approach based on transformer and generative adversarial network. IEEE Trans. Image Process. 1 (2023). https:\/\/doi.org\/10.1109\/TIP.2023.3273451","DOI":"10.1109\/TIP.2023.3273451"},{"key":"30_CR38","doi-asserted-by":"publisher","unstructured":"Sun, Y., Cao, B., Zhu, P., Hu, Q.: Drone-based rgb-infrared cross-modality vehicle detection via uncertainty-aware learning. IEEE Trans. Circ. Syst. Video Technol. 1 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2022.3168279","DOI":"10.1109\/TCSVT.2022.3168279"},{"issue":"12","key":"30_CR39","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1109\/JAS.2022.106082","volume":"9","author":"L Tang","year":"2022","unstructured":"Tang, L., Deng, Y., Ma, Y., Huang, J., Ma, J.: Superfusion: a versatile image registration and fusion network with semantic awareness. IEEE\/CAA J. Automatica Sinica 9(12), 2121\u20132137 (2022). https:\/\/doi.org\/10.1109\/JAS.2022.106082","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"30_CR40","doi-asserted-by":"publisher","unstructured":"Tang, L., Li, C., Ma, J.: Mask-difuser: a masked diffusion model for unified unsupervised image fusion. IEEE Trans. Pattern Anal. Mach. Intell. 1\u201318 (2025). https:\/\/doi.org\/10.1109\/TPAMI.2025.3609323","DOI":"10.1109\/TPAMI.2025.3609323"},{"key":"30_CR41","doi-asserted-by":"crossref","unstructured":"Tang, L., Li, C., Ma, J.: Mask-difuser: a masked diffusion model for unified unsupervised image fusion. IEEE Trans. Pattern Anal. Mach. Intell. 1\u201318 (2025)","DOI":"10.1109\/TPAMI.2025.3609323"},{"key":"30_CR42","doi-asserted-by":"publisher","unstructured":"Tang, L., Yuan, J., Ma, J.: Image fusion in the loop of high-level vision tasks: a semantic-aware real-time infrared and visible image fusion network. Inf. Fusion 82, 28\u201342 (2022). https:\/\/doi.org\/10.1016\/j.inffus.2021.12.004, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253521002542","DOI":"10.1016\/j.inffus.2021.12.004"},{"key":"30_CR43","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","volume":"83\u201384","author":"L Tang","year":"2022","unstructured":"Tang, L., Yuan, J., Zhang, H., Jiang, X., Ma, J.: Piafusion: a progressive infrared and visible image fusion network based on illumination aware. Inf. Fusion 83\u201384, 79\u201392 (2022). https:\/\/doi.org\/10.1016\/j.inffus.2022.03.007","journal-title":"Inf. Fusion"},{"key":"30_CR44","doi-asserted-by":"publisher","first-page":"5413","DOI":"10.1109\/TMM.2022.3192661","volume":"25","author":"W Tang","year":"2023","unstructured":"Tang, W., He, F., Liu, Y.: Ydtr: infrared and visible image fusion via y-shape dynamic transformer. IEEE Trans. Multimedia 25, 5413\u20135428 (2023). https:\/\/doi.org\/10.1109\/TMM.2022.3192661","journal-title":"IEEE Trans. Multimedia"},{"issue":"7","key":"30_CR45","doi-asserted-by":"publisher","first-page":"3159","DOI":"10.1109\/TCSVT.2023.3234340","volume":"33","author":"W Tang","year":"2023","unstructured":"Tang, W., He, F., Liu, Y., Duan, Y., Si, T.: Datfuse: infrared and visible image fusion via dual attention transformer. IEEE Trans. Circuits Syst. Video Technol. 33(7), 3159\u20133172 (2023). https:\/\/doi.org\/10.1109\/TCSVT.2023.3234340","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"30_CR46","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000\u20136010 (2017)"},{"key":"30_CR47","doi-asserted-by":"publisher","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u20137475 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.00721","DOI":"10.1109\/CVPR52729.2023.00721"},{"issue":"1","key":"30_CR48","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/TCSVT.2024.3460172","volume":"35","author":"Z Wang","year":"2025","unstructured":"Wang, Z., Zhang, Z., Qi, W., Yang, F., Xu, J.: Freqgan: infrared and visible image fusion via unified frequency adversarial learning. IEEE Trans. Circuits Syst. Video Technol. 35(1), 728\u2013740 (2025). https:\/\/doi.org\/10.1109\/TCSVT.2024.3460172","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"30_CR49","doi-asserted-by":"publisher","unstructured":"Wu, G., et al.: Every sam drop counts: embracing semantic priors for multi-modality image fusion and beyond. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17882\u201317891 (2025). https:\/\/doi.org\/10.1109\/CVPR52734.2025.01666","DOI":"10.1109\/CVPR52734.2025.01666"},{"issue":"1","key":"30_CR50","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1109\/TMM.2024.3521848","volume":"27","author":"Q Xiao","year":"2025","unstructured":"Xiao, Q., Jin, H., Su, H., Zhang, Y., Xiao, Z., Wang, B.: Spdfusion: A semantic prior knowledge-driven method for infrared and visible image fusion. IEEE Trans. Multimedia 27(1), 1691\u20131705 (2025). https:\/\/doi.org\/10.1109\/TMM.2024.3521848","journal-title":"IEEE Trans. Multimedia"},{"key":"30_CR51","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: simple and efficient design for semantic segmentation with transformers. In: Neural Information Processing Systems (NeurIPS) (2021)"},{"issue":"1","key":"30_CR52","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","volume":"44","author":"H Xu","year":"2022","unstructured":"Xu, H., Ma, J., Jiang, J., Guo, X., Ling, H.: U2fusion: a unified unsupervised image fusion network. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 502\u2013518 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2020.3012548","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR53","doi-asserted-by":"publisher","first-page":"7203","DOI":"10.1109\/TIP.2020.2999855","volume":"29","author":"H Xu","year":"2020","unstructured":"Xu, H., Ma, J., Zhang, X.P.: Mef-gan: multi-exposure image fusion via generative adversarial networks. IEEE Trans. Image Process. 29, 7203\u20137216 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2999855","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"30_CR54","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1109\/TIM.2009.2026612","volume":"59","author":"B Yang","year":"2010","unstructured":"Yang, B., Li, S.: Multifocus image fusion and restoration with sparse representation. IEEE Trans. Instrum. Meas. 59(4), 884\u2013892 (2010). https:\/\/doi.org\/10.1109\/TIM.2009.2026612","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"30_CR55","doi-asserted-by":"publisher","unstructured":"Yang, K., Xiang, W., Chen, Z., Zhang, J., Liu, Y.: A review on infrared and visible image fusion algorithms based on neural networks. J. Vis. Commun. Image Representation 101, 104179 (2024). https:\/\/doi.org\/10.1016\/j.jvcir.2024.104179, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1047320324001342","DOI":"10.1016\/j.jvcir.2024.104179"},{"key":"30_CR56","doi-asserted-by":"publisher","unstructured":"Yi, X., Xu, H., Zhang, H., Tang, L., Ma, J.: Text-if: leveraging semantic text guidance for degradation-aware and interactive image fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 27016\u201327025 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.02552","DOI":"10.1109\/CVPR52733.2024.02552"},{"key":"30_CR57","doi-asserted-by":"crossref","unstructured":"Yi, X., Zhang, Y., Xiang, X., Yan, Q., Xu, H., Ma, J.: Lut-fuse: towards extremely fast infrared and visible image fusion via distillation to learnable look-up tables. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2025)","DOI":"10.1109\/ICCV51701.2025.01351"},{"key":"30_CR58","doi-asserted-by":"publisher","unstructured":"Yuan, B., Sun, H., Guo, Y., Liu, Q., Zhan, X.: Explainable analysis of infrared and visible light image fusion based on deep learning. Sci. Rep. 15(1), 2223 (2025). https:\/\/doi.org\/10.1038\/s41598-024-79684-6","DOI":"10.1038\/s41598-024-79684-6"},{"key":"30_CR59","doi-asserted-by":"publisher","unstructured":"Zhang, H., Zuo, X., Jiang, J., Guo, C., Ma, J.: Mrfs: mutually reinforcing image fusion and segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 26964\u201326973 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.02547","DOI":"10.1109\/CVPR52733.2024.02547"},{"issue":"9","key":"30_CR60","doi-asserted-by":"publisher","first-page":"8040","DOI":"10.1109\/TPAMI.2025.3572599","volume":"47","author":"W Zhao","year":"2025","unstructured":"Zhao, W., Cui, H., Wang, H., He, Y., Lu, H.: Freefusion: infrared and visible image fusion via cross reconstruction learning. IEEE Trans. Pattern Anal. Mach. Intell. 47(9), 8040\u20138056 (2025). https:\/\/doi.org\/10.1109\/TPAMI.2025.3572599","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR61","doi-asserted-by":"publisher","unstructured":"Zhao, W., Xie, S., Zhao, F., He, Y., Lu, H.: Metafusion: infrared and visible image fusion via meta-feature embedding from object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13955\u201313965 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01341","DOI":"10.1109\/CVPR52729.2023.01341"},{"key":"30_CR62","doi-asserted-by":"publisher","unstructured":"Zhao, Z., et al.: Cddfuse: correlation-driven dual-branch feature decomposition for multi-modality image fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5906\u20135916 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.00572","DOI":"10.1109\/CVPR52729.2023.00572"},{"key":"30_CR63","doi-asserted-by":"publisher","unstructured":"Zhao, Z., et al.: Equivariant multi-modality image fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 25912\u201325921 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.02448","DOI":"10.1109\/CVPR52733.2024.02448"},{"key":"30_CR64","doi-asserted-by":"crossref","unstructured":"Zhao, Z., et al.: Ddfm: denoising diffusion model for multi-modality image fusion. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8082\u20138093 (2023)","DOI":"10.1109\/ICCV51070.2023.00742"},{"key":"30_CR65","doi-asserted-by":"publisher","unstructured":"Zheng, N., et al.: Probing synergistic high-order interaction in infrared and visible image fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 26374\u201326385 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.02492","DOI":"10.1109\/CVPR52733.2024.02492"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37447-9_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T15:46:48Z","timestamp":1788882408000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37447-9_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032374462","9783032374479"],"references-count":65,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37447-9_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"9 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}