{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T07:11:05Z","timestamp":1784099465283,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819227587","type":"print"},{"value":"9789819227594","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-2759-4_31","type":"book-chapter","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:52:51Z","timestamp":1784098371000},"page":"417-429","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SWDNet: Knowledge-Driven Cross-Modal Network with\u00a0Sliding Window Difference Modeling"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1309-3056","authenticated-orcid":false,"given":"Junjie","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3874-9669","authenticated-orcid":false,"given":"Renping","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Chen, Z., Tao, M., Xie, R., Nie, H., Chen, Q.: Mdp-eec: model distillation and partitioning enhanced end-edge collaboration computing for consumer electronics. IEEE Trans. Consum. Electron. (2025)","DOI":"10.1109\/TCE.2025.3580773"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Tao, M., Su, L., Nie, H., Xie, R., Xu, X., Yu, J.: Collaborative neuro-fuzzy in edge-ai for industrial fault diagnosis with sensing data fusion. IEEE Trans. Comput. Soc. Syst. (2026)","DOI":"10.1109\/TCSS.2026.3650904"},{"key":"31_CR3","doi-asserted-by":"publisher","first-page":"108902","DOI":"10.1016\/j.patcog.2022.108902","volume":"131","author":"F Yuan","year":"2022","unstructured":"Yuan, F., Dong, Z., Zhang, L., Xia, X., Shi, J.: Cubic-cross convolutional attention and count prior embedding for smoke segmentation. Pattern Recogn. 131, 108902 (2022)","journal-title":"Pattern Recogn."},{"issue":"20","key":"31_CR4","doi-asserted-by":"publisher","first-page":"31259","DOI":"10.1007\/s11042-023-14879-z","volume":"82","author":"L Zhang","year":"2023","unstructured":"Zhang, L., Yuan, F., Xia, X.: Edge-reinforced attention network for smoke semantic segmentation. Multimedia Tools Appl. 82(20), 31259\u201331284 (2023)","journal-title":"Multimedia Tools Appl."},{"issue":"3","key":"31_CR5","first-page":"130","volume":"2","author":"FA Hossain","year":"2023","unstructured":"Hossain, F.A., Zhang, Y.: Msfired-net: a lightweight and efficient convolutional neural network for flame and smoke segmentation. J. Autom. Intell. 2(3), 130\u2013138 (2023)","journal-title":"J. Autom. Intell."},{"issue":"2","key":"31_CR6","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1109\/TII.2023.3271441","volume":"20","author":"T Jing","year":"2023","unstructured":"Jing, T., Meng, Q.H., Hou, H.R.: Smokeseger: a transformer-cnn coupled model for urban scene smoke segmentation. IEEE Trans. Industr. Inf. 20(2), 1385\u20131396 (2023)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"31_CR7","doi-asserted-by":"publisher","first-page":"111177","DOI":"10.1016\/j.patcog.2024.111177","volume":"159","author":"K Li","year":"2025","unstructured":"Li, K., Yuan, F., Wang, C.: An effective multi-scale interactive fusion network with hybrid transformer and cnn for smoke image segmentation. Pattern Recogn. 159, 111177 (2025)","journal-title":"Pattern Recogn."},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, R., Wang, J., Ma, L., Cheng, L., Yuan, H.: Dstcfuse: a method based on dual-cycled cross-awareness of structure tensor for semantic segmentation via infrared and visible image fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5558\u20135567 (2024)","DOI":"10.1109\/CVPRW63382.2024.00565"},{"issue":"9","key":"31_CR9","doi-asserted-by":"publisher","first-page":"10118","DOI":"10.1109\/TITS.2023.3268063","volume":"24","author":"G Li","year":"2023","unstructured":"Li, G., Qian, X., Qu, X.: Sosmaskfuse: an infrared and visible image fusion architecture based on salient object segmentation mask. IEEE Trans. Intell. Transp. Syst. 24(9), 10118\u201310137 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"3","key":"31_CR10","doi-asserted-by":"publisher","first-page":"2576","DOI":"10.1109\/LRA.2019.2904733","volume":"4","author":"Y Sun","year":"2019","unstructured":"Sun, Y., Zuo, W., Liu, M.: Rtfnet: Rgb-thermal fusion network for semantic segmentation of urban scenes. IEEE Robot. Autom. Lett. 4(3), 2576\u20132583 (2019)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"31_CR11","doi-asserted-by":"crossref","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. 5802\u20135811 (2022)","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Salamati, N., Larlus, D., Csurka, G., S\u00fcsstrunk, S.: Semantic image segmentation using visible and near-infrared channels. In: European Conference on Computer Vision, pp. 461\u2013471. Springer (2012)","DOI":"10.1007\/978-3-642-33868-7_46"},{"key":"31_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.future.2023.03.012","volume":"145","author":"M Tao","year":"2023","unstructured":"Tao, M.: Semantic ontology enabled modeling, retrieval and inference for incomplete mobile trajectory data. Futur. Gener. Comput. Syst. 145, 1\u201311 (2023)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"31_CR14","doi-asserted-by":"publisher","first-page":"100565","DOI":"10.1016\/j.imu.2021.100565","volume":"23","author":"T Haryanto","year":"2021","unstructured":"Haryanto, T., Suhartanto, H., Arymurthy, A.M., Kusmardi, K.: Conditional sliding windows: an approach for handling data limitation in colorectal histopathology image classification. Inf. Med. Unlocked 23, 100565 (2021)","journal-title":"Inf. Med. Unlocked"},{"issue":"10","key":"31_CR15","doi-asserted-by":"publisher","first-page":"5470","DOI":"10.1109\/TCSVT.2023.3256414","volume":"33","author":"Y Feng","year":"2023","unstructured":"Feng, Y., Meng, X., Zhou, F., Lin, W., Su, Z.: Real-world non-homogeneous haze removal by sliding self-attention wavelet network. IEEE Trans. Circuits Syst. Video Technol. 33(10), 5470\u20135485 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Krystalakos, O., Nalmpantis, C., Vrakas, D.: Sliding window approach for online energy disaggregation using artificial neural networks. In: Proceedings of the 10th Hellenic Conference on Artificial Intelligence, pp. 1\u20136. (2018)","DOI":"10.1145\/3200947.3201011"},{"issue":"4","key":"31_CR18","doi-asserted-by":"publisher","first-page":"4185","DOI":"10.1109\/TIE.2021.3070521","volume":"69","author":"Y Qin","year":"2021","unstructured":"Qin, Y., Yan, Y., Ji, H., Wang, Y.: Recursive correlative statistical analysis method with sliding windows for incipient fault detection. IEEE Trans. Industr. Electron. 69(4), 4185\u20134194 (2021)","journal-title":"IEEE Trans. Industr. Electron."},{"key":"31_CR19","doi-asserted-by":"crossref","unstructured":"Taranco, R., Arnau, J.M., Gonz\u00e1lez, A.: Slidex: a novel architecture for sliding window processing. In: Proceedings of the 38th ACM International Conference on Supercomputing, pp. 312\u2013323 (2024)","DOI":"10.1145\/3650200.3656613"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, 5\u20139 October 2015, proceedings, Part III 18. pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"31_CR21","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"31_CR22","doi-asserted-by":"crossref","unstructured":"Xie, R., Xu, H., Tao, M., He, C., Chen, M.: Data-driven smoke segmentation and removal for visible image. In: 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), pp. 631\u2013636. IEEE (2024)","DOI":"10.1109\/ISPA63168.2024.00086"},{"key":"31_CR23","doi-asserted-by":"crossref","unstructured":"Jain, J., et al.: Semask: semantically masked transformers for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 752\u2013761 (2023)","DOI":"10.1109\/ICCVW60793.2023.00083"},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiong, Z., Bhattacharyya, S.P.: Pidnet: a real-time semantic segmentation network inspired by pid controllers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19529\u201319539 (2023)","DOI":"10.1109\/CVPR52729.2023.01871"},{"issue":"7","key":"31_CR25","doi-asserted-by":"publisher","first-page":"9380","DOI":"10.1109\/TNNLS.2022.3233089","volume":"35","author":"S Zhao","year":"2023","unstructured":"Zhao, S., Liu, Y., Jiao, Q., Zhang, Q., Han, J.: Mitigating modality discrepancies for rgb-t semantic segmentation. IEEE Trans. Neural Netw. Learn. Syst. 35(7), 9380\u20139394 (2023)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"31_CR26","doi-asserted-by":"publisher","first-page":"6348","DOI":"10.1109\/TMM.2023.3349072","volume":"26","author":"Y Lv","year":"2024","unstructured":"Lv, Y., Liu, Z., Li, G.: Context-aware interaction network for rgb-t semantic segmentation. IEEE Trans. Multimedia 26, 6348\u20136360 (2024)","journal-title":"IEEE Trans. Multimedia"},{"issue":"12","key":"31_CR27","doi-asserted-by":"publisher","first-page":"7737","DOI":"10.1109\/TCSVT.2023.3281419","volume":"33","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Li, G., Liu, Z.: Sgfnet: semantic-guided fusion network for rgb-thermal semantic segmentation. IEEE Trans. Circuits Syst. Video Technol. 33(12), 7737\u20137748 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"7","key":"31_CR28","doi-asserted-by":"publisher","first-page":"4060","DOI":"10.1109\/LRA.2023.3272269","volume":"8","author":"M Liang","year":"2023","unstructured":"Liang, M., Hu, J., Bao, C., Feng, H., Deng, F., Lam, T.L.: Explicit attention-enhanced fusion for rgb-thermal perception tasks. IEEE Robot. Autom. Lett. 8(7), 4060\u20134067 (2023)","journal-title":"IEEE Robot. Autom. Lett."}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-2759-4_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:52:53Z","timestamp":1784098373000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-2759-4_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"ISBN":["9789819227587","9789819227594"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-2759-4_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]},"assertion":[{"value":"16 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"17 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 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":"ksem2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ksem2026.rosc.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}