{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:27:02Z","timestamp":1762954022998,"version":"3.40.3"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031736674"},{"type":"electronic","value":"9783031736681"}],"license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73668-1_24","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T02:01:35Z","timestamp":1733018495000},"page":"408-424","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploring Reliable Matching with\u00a0Phase Enhancement for\u00a0Night-Time Semantic Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8665-4886","authenticated-orcid":false,"given":"Yuwen","family":"Pan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8009-4240","authenticated-orcid":false,"given":"Rui","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1488-3081","authenticated-orcid":false,"given":"Naisong","family":"Luo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1856-9564","authenticated-orcid":false,"given":"Tianzhu","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1151-1792","authenticated-orcid":false,"given":"Yongdong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,1]]},"reference":[{"issue":"12","key":"24_CR1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1290\u20131299 (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"24_CR4","first-page":"17864","volume":"34","author":"B Cheng","year":"2021","unstructured":"Cheng, B., Schwing, A., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. Adv. Neural. Inf. Process. Syst. 34, 17864\u201317875 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"24_CR5","first-page":"4479","volume":"33","author":"L Chi","year":"2020","unstructured":"Chi, L., Jiang, B., Mu, Y.: Fast fourier convolution. Adv. Neural. Inf. Process. Syst. 33, 4479\u20134488 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Deng, X., Wang, P., Lian, X., Newsam, S.: Nightlab: a dual-level architecture with hardness detection for segmentation at night. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16938\u201316948 (2022)","DOI":"10.1109\/CVPR52688.2022.01643"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"24_CR9","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.sigpro.2016.05.031","volume":"129","author":"X Fu","year":"2016","unstructured":"Fu, X., Zeng, D., Huang, Y., Liao, Y., Ding, X., Paisley, J.: A fusion-based enhancing method for weakly illuminated images. Signal Process. 129, 82\u201396 (2016)","journal-title":"Signal Process."},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Gao, H., Guo, J., Wang, G., Zhang, Q.: Cross-domain correlation distillation for unsupervised domain adaptation in nighttime semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9913\u20139923 (2022)","DOI":"10.1109\/CVPR52688.2022.00968"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"24_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106617","volume":"213","author":"G Li","year":"2021","unstructured":"Li, G., Yang, Y., Qu, X., Cao, D., Li, K.: A deep learning based image enhancement approach for autonomous driving at night. Knowl.-Based Syst. 213, 106617 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.: Refinenet: multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1925\u20131934 (2017)","DOI":"10.1109\/CVPR.2017.549"},{"key":"24_CR14","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":"24_CR15","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"24_CR16","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Luo, N., Pan, Y., Sun, R., Zhang, T., Xiong, Z., Wu, F.: Camouflaged instance segmentation via explicit de-camouflaging. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17918\u201317927 (2023)","DOI":"10.1109\/CVPR52729.2023.01718"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Luo, N., Sun, R., Pan, Y., Zhang, T., Wu, F.: Electron microscopy images as set of fragments for mitochondrial segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 3981\u20133989 (2024)","DOI":"10.1609\/aaai.v38i4.28191"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Mai, H., Sun, R., Wang, Y., Zhang, T., Wu, F.: Pay attention to target: relation-aware temporal consistency for domain adaptive video semantic segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 4162\u20134170 (2024)","DOI":"10.1609\/aaai.v38i5.28211"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Mai, H., Sun, R., Zhang, T., Wu, F.: Rankmatch: exploring the better consistency regularization for semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3391\u20133401 (2024)","DOI":"10.1109\/CVPR52733.2024.00326"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Mai, H., Sun, R., Zhang, T., Xiong, Z., Wu, F.: Dualrel: semi-supervised mitochondria segmentation from a prototype perspective. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19617\u201319626 (2023)","DOI":"10.1109\/CVPR52729.2023.01879"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Pan, Y., et al.: Adaptive template transformer for mitochondria segmentation in electron microscopy images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21474\u201321484 (2023)","DOI":"10.1109\/ICCV51070.2023.01963"},{"key":"24_CR23","unstructured":"Paszke, A., et\u00a0al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"24_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., Gool, L.V.: Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7374\u20137383 (2019)","DOI":"10.1109\/ICCV.2019.00747"},{"issue":"6","key":"24_CR26","doi-asserted-by":"publisher","first-page":"3139","DOI":"10.1109\/TPAMI.2020.3045882","volume":"44","author":"C Sakaridis","year":"2020","unstructured":"Sakaridis, C., Dai, D., Van Gool, L.: Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3139\u20133153 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"24_CR27","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1109\/TIV.2020.3039456","volume":"6","author":"M Schutera","year":"2020","unstructured":"Schutera, M., Hussein, M., Abhau, J., Mikut, R., Reischl, M.: Night-to-day: online image-to-image translation for object detection within autonomous driving by night. IEEE Trans. Intell. Veh. 6(3), 480\u2013489 (2020)","journal-title":"IEEE Trans. Intell. Veh."},{"issue":"4","key":"24_CR28","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1109\/TITS.2020.3033569","volume":"23","author":"C Song","year":"2020","unstructured":"Song, C., Wu, J., Zhu, L., Zhang, M., Ling, H.: Nighttime road scene parsing by unsupervised domain adaptation. IEEE Trans. Intell. Transp. Syst. 23(4), 3244\u20133255 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Sun, R., Li, Y., Zhang, T., Mao, Z., Wu, F., Zhang, Y.: Lesion-aware transformers for diabetic retinopathy grading. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10938\u201310947 (2021)","DOI":"10.1109\/CVPR46437.2021.01079"},{"key":"24_CR30","doi-asserted-by":"crossref","unstructured":"Sun, R., et al.: Appearance prompt vision transformer for connectome reconstruction. In: IJCAI, pp. 1423\u20131431 (2023)","DOI":"10.24963\/ijcai.2023\/158"},{"key":"24_CR31","doi-asserted-by":"crossref","unstructured":"Sun, R., Wang, Y., Mai, H., Zhang, T., Wu, F.: Alignment before aggregation: trajectory memory retrieval network for video object segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1218\u20131228 (2023)","DOI":"10.1109\/ICCV51070.2023.00118"},{"key":"24_CR32","doi-asserted-by":"publisher","first-page":"9085","DOI":"10.1109\/TIP.2021.3122004","volume":"30","author":"X Tan","year":"2021","unstructured":"Tan, X., Xu, K., Cao, Y., Zhang, Y., Ma, L., Lau, R.W.: Night-time scene parsing with a large real dataset. IEEE Trans. Image Process. 30, 9085\u20139098 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"24_CR33","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"24_CR34","first-page":"31524","volume":"36","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Luo, N., Zhang, T.: Focus on query: adversarial mining transformer for few-shot segmentation. Adv. Neural. Inf. Process. Syst. 36, 31524\u201331542 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"24_CR35","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, R., Luo, N., Pan, Y., Zhang, T.: Image-to-image matching via foundation models: a new perspective for open-vocabulary semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3952\u20133963 (2024)","DOI":"10.1109\/CVPR52733.2024.00379"},{"key":"24_CR36","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, R., Zhang, T.: Rethinking the correlation in few-shot segmentation: a buoys view. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7183\u20137192 (2023)","DOI":"10.1109\/CVPR52729.2023.00694"},{"key":"24_CR37","doi-asserted-by":"publisher","unstructured":"Wang, Y., Sun, R., Zhang, Z., Zhang, T.: Adaptive agent transformer for few-shot segmentation. In: European Conference on Computer Vision, pp. 36\u201352. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-19818-2_3","DOI":"10.1007\/978-3-031-19818-2_3"},{"key":"24_CR38","doi-asserted-by":"crossref","unstructured":"Wei, Z., Chen, L., Tu, T., Ling, P., Chen, H., Jin, Y.: Disentangle then parse: night-time semantic segmentation with illumination disentanglement. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21593\u201321603 (2023)","DOI":"10.1109\/ICCV51070.2023.01974"},{"key":"24_CR39","doi-asserted-by":"crossref","unstructured":"Wu, X., Wu, Z., Guo, H., Ju, L., Wang, S.: Dannet: a one-stage domain adaptation network for unsupervised nighttime semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15769\u201315778 (2021)","DOI":"10.1109\/CVPR46437.2021.01551"},{"key":"24_CR40","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2 (2019). https:\/\/github.com\/facebookresearch\/detectron2"},{"key":"24_CR41","doi-asserted-by":"crossref","unstructured":"Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 418\u2013434 (2018)","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"24_CR42","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: simple and efficient design for semantic segmentation with transformers. Adv. Neural. Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"24_CR43","doi-asserted-by":"crossref","unstructured":"Xiong, G., et al.: Aggregation and purification: dual enhancement network for point cloud few-shot segmentation. In: Elkind, E. (ed.) Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24. International Joint Conferences on Artificial Intelligence Organization (2024)","DOI":"10.24963\/ijcai.2024\/164"},{"key":"24_CR44","doi-asserted-by":"crossref","unstructured":"Yang, Y., Soatto, S.: Fda: Fourier domain adaptation for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4085\u20134095 (2020)","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"24_CR45","doi-asserted-by":"crossref","unstructured":"Yu, F., et al.: Bdd100k: a diverse driving dataset for heterogeneous multitask learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2636\u20132645 (2020)","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"24_CR46","unstructured":"Yuan, Y., Huang, L., Guo, J., Zhang, C., Chen, X., Wang, J.: Ocnet: object context network for scene parsing. arXiv preprint arXiv:1809.00916 (2018)"},{"key":"24_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73668-1_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T02:13:00Z","timestamp":1733019180000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73668-1_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,1]]},"ISBN":["9783031736674","9783031736681"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73668-1_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,1]]},"assertion":[{"value":"1 December 2024","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}