{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T23:13:02Z","timestamp":1778109182255,"version":"3.51.4"},"reference-count":70,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62406051"],"award-info":[{"award-number":["62406051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372077"],"award-info":[{"award-number":["62372077"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong Province","doi-asserted-by":"publisher","award":["2025CXPT084"],"award-info":[{"award-number":["2025CXPT084"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010029","name":"Taishan Scholar Foundation of Shandong Province","doi-asserted-by":"publisher","award":["tsqn202507224"],"award-info":[{"award-number":["tsqn202507224"]}],"id":[{"id":"10.13039\/501100010029","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Digital Signal Processing"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.dsp.2026.106069","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:59:16Z","timestamp":1773968356000},"page":"106069","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Masking-guided source-free domain adaptation for nighttime semantic segmentation"],"prefix":"10.1016","volume":"177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4987-4185","authenticated-orcid":false,"given":"Xichao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4210-7960","authenticated-orcid":false,"given":"Simiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5843-4675","authenticated-orcid":false,"given":"Yijia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8663-9870","authenticated-orcid":false,"given":"Mingyu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7344-8645","authenticated-orcid":false,"given":"Yunan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.dsp.2026.106069_bib0001","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/TPAMI.2021.3138829","article-title":"A one-stage domain adaptation network with image alignment for unsupervised nighttime semantic segmentation","volume":"45","author":"Wu","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"10.1016\/j.dsp.2026.106069_bib0002","doi-asserted-by":"crossref","first-page":"5449","DOI":"10.1109\/TPAMI.2024.3366769","article-title":"From simple to complex scenes: learning robust feature representations for accurate human parsing","volume":"46","author":"Liu","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"10.1016\/j.dsp.2026.106069_bib0003","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.1109\/TPAMI.2020.3045882","article-title":"Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation","volume":"44","author":"Sakaridis","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.dsp.2026.106069_bib0004","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/TPAMI.2023.3301502","article-title":"Learnability enhancement for low-light raw image denoising: a data perspective","volume":"46","author":"Feng","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.dsp.2026.106069_bib0005","series-title":"Proc. Neurips, 2021","first-page":"1167","article-title":"Learning to adapt via latent domains for adaptive semantic segmentation","author":"Liu","year":"2021"},{"key":"10.1016\/j.dsp.2026.106069_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126469","article-title":"Unsupervised domain adaptation via style adaptation and boundary enhancement for medical semantic segmentation","volume":"550","author":"Ge","year":"2023","journal-title":"Neurocomputing"},{"issue":"7","key":"10.1016\/j.dsp.2026.106069_bib0007","doi-asserted-by":"crossref","first-page":"5255","DOI":"10.1109\/TCSVT.2023.3342879","article-title":"Intermediate domain based meta learning framework for adaptive object detection","volume":"34","author":"Zhu","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106069_bib0008","doi-asserted-by":"crossref","first-page":"4540","DOI":"10.1109\/TMM.2023.3323862","article-title":"Progressive fourier adversarial domain adaptation for object classification and retrieval","volume":"26","author":"Li","year":"2024","journal-title":"IEEE Trans. Multim."},{"key":"10.1016\/j.dsp.2026.106069_bib0009","series-title":"Proc. ACM MM","first-page":"1405","article-title":"Bidirectional self-training with multiple anisotropic prototypes for domain adaptive semantic segmentation","author":"Lu","year":"2022"},{"issue":"3","key":"10.1016\/j.dsp.2026.106069_bib0010","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1109\/TPAMI.2022.3229207","article-title":"I2F: A unified image-to-feature approach for domain adaptive semantic segmentation","volume":"46","author":"Ma","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"10.1016\/j.dsp.2026.106069_bib0011","doi-asserted-by":"crossref","first-page":"14208","DOI":"10.1109\/TPAMI.2023.3298346","article-title":"CALDA: Improving multi-source time series domain adaptation with contrastive adversarial learning","volume":"45","author":"Wilson","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.dsp.2026.106069_bib0012","series-title":"Proc. CVPR","first-page":"1215","article-title":"Source-free domain adaptation for semantic segmentation","author":"Liu","year":"2021"},{"key":"10.1016\/j.dsp.2026.106069_bib0013","series-title":"Proc. ACM MM","first-page":"3293","article-title":"Domain adaptive semantic segmentation without source data","author":"You","year":"2021"},{"key":"10.1016\/j.dsp.2026.106069_bib0014","series-title":"Proc. ACM MM","first-page":"7638","article-title":"When masked image modeling meets source-free unsupervised domain adaptation: dual-level masked network for semantic segmentation","author":"Li","year":"2023"},{"key":"10.1016\/j.dsp.2026.106069_bib0015","series-title":"Proc. CVPR","first-page":"23416","article-title":"Stable neighbor denoising for source-free domain adaptive segmentation","author":"Zhao","year":"2024"},{"key":"10.1016\/j.dsp.2026.106069_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126921","article-title":"Source-free unsupervised domain adaptation: current research and future directions","volume":"564","author":"Zhang","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.dsp.2026.106069_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.127190","article-title":"Uncertainty-aware pseudo-label filtering for source-free unsupervised domain adaptation","volume":"575","author":"Chen","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.dsp.2026.106069_bib0018","series-title":"Proc. Neurips","first-page":"1195","article-title":"Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results","author":"Tarvainen","year":"2017"},{"key":"10.1016\/j.dsp.2026.106069_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.107940","article-title":"Latent domain knowledge distillation for nighttime semantic segmentation","volume":"132","author":"Liu","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.dsp.2026.106069_bib0020","series-title":"Proc. CVPR","first-page":"11721","article-title":"MIC: Masked image consistency for context-enhanced domain adaptation","author":"Hoyer","year":"2023"},{"issue":"6","key":"10.1016\/j.dsp.2026.106069_bib0021","doi-asserted-by":"crossref","first-page":"3260","DOI":"10.1109\/TPAMI.2020.3048039","article-title":"Self-correction for human parsing","volume":"44","author":"Li","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.dsp.2026.106069_bib0022","series-title":"Model-driven self-aware self-training framework for label noise-tolerant medical image segmentation, Signal Process","volume":"212","author":"Zhang","year":"2023"},{"key":"10.1016\/j.dsp.2026.106069_bib0023","doi-asserted-by":"crossref","first-page":"8077","DOI":"10.1109\/TMM.2024.3374594","article-title":"Multi-level label correction by distilling proximate patterns for semi-supervised semantic segmentation","volume":"26","author":"Xiao","year":"2024","journal-title":"IEEE Trans. Multim."},{"key":"10.1016\/j.dsp.2026.106069_bib0024","series-title":"Proc. AAAI, 2021","first-page":"2207","article-title":"Hierarchical information passing based noise-tolerant hybrid learning for semi-supervised human parsing","author":"Liu","year":"2021"},{"issue":"7","key":"10.1016\/j.dsp.2026.106069_bib0025","doi-asserted-by":"crossref","first-page":"8094","DOI":"10.1109\/TPAMI.2023.3236459","article-title":"Asymmetric loss functions for noise-tolerant learning: theory and applications","volume":"45","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.dsp.2026.106069_bib0026","series-title":"Proc. CVPR","first-page":"4084","article-title":"FDA: Fourier domain adaptation for semantic segmentation","author":"Yang","year":"2020"},{"key":"10.1016\/j.dsp.2026.106069_bib0027","series-title":"Proc. AAAI","first-page":"1220","article-title":"Amplitude spectrum transformation for open compound domain adaptive semantic segmentation","author":"Kundu","year":"2022"},{"issue":"1","key":"10.1016\/j.dsp.2026.106069_bib0028","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/TNNLS.2021.3093468","article-title":"Rethinking maximum mean discrepancy for visual domain adaptation","volume":"34","author":"Wang","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.dsp.2026.106069_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105580","article-title":"Quantized feature alignment for unsupervised domain adaptation in abdominal and prostate segmentation","volume":"168","author":"Wang","year":"2026","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.dsp.2026.106069_bib0030","series-title":"Dynamic classifier approximation for unsupervised domain adaptation, Signal Process","volume":"206","author":"Liu","year":"2023"},{"key":"10.1016\/j.dsp.2026.106069_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2024.104551","article-title":"Unsupervised domain adaptation for the semantic segmentation of remote sensing images via a class-aware fourier transform and a fine-grained discriminator","volume":"151","author":"Ismael","year":"2024","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.dsp.2026.106069_bib0032","series-title":"Proc. ACM MM","first-page":"2233","article-title":"Source data-free unsupervised domain adaptation for semantic segmentation","author":"Ye","year":"2021"},{"key":"10.1016\/j.dsp.2026.106069_bib0033","series-title":"Proc. ICML","first-page":"11710","article-title":"Balancing discriminability and transferability for source-free domain adaptation","author":"Kundu","year":"2022"},{"key":"10.1016\/j.dsp.2026.106069_bib0034","series-title":"Proc. Neurips","article-title":"When visual prompt tuning meets source-free domain adaptive semantic segmentation","author":"Ma","year":"2023"},{"key":"10.1016\/j.dsp.2026.106069_bib0035","series-title":"Proc. ICCV","first-page":"21729","article-title":"Crossmatch: source-free domain adaptive semantic segmentation via cross-modal consistency training","author":"Yin","year":"2023"},{"key":"10.1016\/j.dsp.2026.106069_bib0036","doi-asserted-by":"crossref","first-page":"7709","DOI":"10.1109\/TMM.2024.3370678","article-title":"Self-mining the confident prototypes for source-free unsupervised domain adaptation in image segmentation","volume":"26","author":"Tian","year":"2024","journal-title":"IEEE Trans. Multim."},{"issue":"1","key":"10.1016\/j.dsp.2026.106069_bib0037","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s11263-023-01863-1","article-title":"Crots: cross-domain teacher-student learning for source-free domain adaptive semantic segmentation","volume":"132","author":"Luo","year":"2024","journal-title":"Int. J. Comput. Vis."},{"issue":"12","key":"10.1016\/j.dsp.2026.106069_bib0038","doi-asserted-by":"crossref","first-page":"9890","DOI":"10.1109\/TPAMI.2024.3432168","article-title":"A curriculum-style self-training approach for source-free semantic segmentation","volume":"46","author":"Wang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.dsp.2026.106069_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105149","article-title":"Nighttime image semantic segmentation with retinex theory","volume":"148","author":"Sun","year":"2024","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.dsp.2026.106069_bib0040","series-title":"Proc. ITSC","first-page":"3819","article-title":"Dark model adaptation: semantic image segmentation from daytime to nighttime","author":"Dai","year":"2018"},{"key":"10.1016\/j.dsp.2026.106069_bib0041","series-title":"Proc. ICCV","first-page":"7373","article-title":"Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation","author":"Sakaridis","year":"2019"},{"key":"10.1016\/j.dsp.2026.106069_bib0042","series-title":"Proc. CVPR","first-page":"16917","article-title":"Nightlab: a dual-level architecture with hardness detection for segmentation at night","author":"Deng","year":"2022"},{"key":"10.1016\/j.dsp.2026.106069_bib0043","series-title":"Proc. ICCV Workshops","first-page":"2962","article-title":"CDAda: A curriculum domain adaptation for nighttime semantic segmentation","author":"Xu","year":"2021"},{"key":"10.1016\/j.dsp.2026.106069_bib0044","series-title":"Proc. CVPR, 2021","first-page":"15769","article-title":"DANNet: A one-stage domain adaptation network for unsupervised nighttime semantic segmentation","author":"Wu","year":"2021"},{"issue":"10","key":"10.1016\/j.dsp.2026.106069_bib0045","doi-asserted-by":"crossref","first-page":"5855","DOI":"10.1109\/TCSVT.2023.3260240","article-title":"Improving nighttime driving-scene segmentation via dual image-adaptive learnable filters","volume":"33","author":"Liu","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.dsp.2026.106069_bib0046","series-title":"Proc. CVPR","first-page":"9903","article-title":"Cross-domain correlation distillation for unsupervised domain adaptation in nighttime semantic segmentation","author":"Gao","year":"2022"},{"key":"10.1016\/j.dsp.2026.106069_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105211","article-title":"Dual-branch teacher-student with noise-tolerant learning for domain adaptive nighttime segmentation","volume":"150","author":"Chen","year":"2024","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.dsp.2026.106069_bib0048","series-title":"Proc. ICCV","first-page":"21515","article-title":"CMDA: Cross-modality domain adaptation for nighttime semantic segmentation","author":"Xia","year":"2023"},{"key":"10.1016\/j.dsp.2026.106069_bib0049","series-title":"Proc. IJCAI","first-page":"672","article-title":"ICDA: Illumination-coupled domain adaptation framework for unsupervised nighttime semantic segmentation","author":"Dong","year":"2023"},{"issue":"11","key":"10.1016\/j.dsp.2026.106069_bib0050","doi-asserted-by":"crossref","first-page":"21405","DOI":"10.1109\/TITS.2022.3177615","article-title":"SFNet-N: An improved sfnet algorithm for semantic segmentation of low-light autonomous driving road scenes","volume":"23","author":"Wang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.dsp.2026.106069_bib0051","series-title":"Proc. ICCV","first-page":"21536","article-title":"Disentangle then parse: night-time semantic segmentation with illumination disentanglement","author":"Wei","year":"2023"},{"key":"10.1016\/j.dsp.2026.106069_bib0052","series-title":"Proc. CVPR","first-page":"15979","article-title":"Masked autoencoders are scalable vision learners","author":"He","year":"2022"},{"key":"10.1016\/j.dsp.2026.106069_bib0053","series-title":"Proc. CVPR","first-page":"24256","article-title":"Masked autoencoders enable efficient knowledge distillers","author":"Bai","year":"2023"},{"key":"10.1016\/j.dsp.2026.106069_bib0054","series-title":"Proc. ECCV","first-page":"674","article-title":"Conmatch: semi-supervised learning with confidence-guided consistency regularization","author":"Kim","year":"2022"},{"key":"10.1016\/j.dsp.2026.106069_bib0055","series-title":"Proc. CVPR","first-page":"19786","article-title":"Pseudo-label guided contrastive learning for semi-supervised medical image segmentation","author":"Basak","year":"2023"},{"key":"10.1016\/j.dsp.2026.106069_bib0056","series-title":"Proc. Neurips","first-page":"8792","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","author":"Zhang","year":"2018"},{"key":"10.1016\/j.dsp.2026.106069_bib0057","series-title":"Proc. ICCV","first-page":"322","article-title":"Symmetric cross entropy for robust learning with noisy labels","author":"Wang","year":"2019"},{"key":"10.1016\/j.dsp.2026.106069_bib0058","series-title":"Proc. ICML, 2021","first-page":"12846","article-title":"Asymmetric loss functions for learning with noisy labels","author":"Zhou","year":"2021"},{"key":"10.1016\/j.dsp.2026.106069_bib0059","series-title":"Proc. CVPR","first-page":"3213","article-title":"The cityscapes dataset for semantic urban scene understanding","author":"Cordts","year":"2016"},{"key":"10.1016\/j.dsp.2026.106069_bib0060","series-title":"Proc. ICCV, 2021","first-page":"10745","article-title":"ACDC: The adverse conditions dataset with correspondences for semantic driving scene understanding","author":"Sakaridis","year":"2021"},{"issue":"4","key":"10.1016\/j.dsp.2026.106069_bib0061","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.dsp.2026.106069_bib0062","series-title":"Proc. ECCV","first-page":"424","article-title":"Copt: unsupervised domain adaptive segmentation using domain-agnostic text embeddings","author":"Mata","year":"2024"},{"key":"10.1016\/j.dsp.2026.106069_bib0063","series-title":"Tent: Fully test-time adaptation by entropy minimization, International Conference on Learning Representations","author":"Wang","year":"2021"},{"key":"10.1016\/j.dsp.2026.106069_bib0064","series-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"7191","article-title":"Continual test-time domain adaptation","author":"Wang","year":"2022"},{"key":"10.1016\/j.dsp.2026.106069_bib0065","series-title":"Proc. CVPR","first-page":"24090","article-title":"Dynamically instance-guided adaptation: a backward-free approach for test-time domain adaptive semantic segmentation","author":"Wang","year":"2023"},{"key":"10.1016\/j.dsp.2026.106069_bib0066","series-title":"Proc. AAAI","first-page":"5550","article-title":"Efficient deformable convolutional prompt for continual test-time adaptation in medical image segmentation","author":"Liu","year":"2025"},{"key":"10.1016\/j.dsp.2026.106069_bib0067","series-title":"Proc. AAAI","first-page":"2429","article-title":"Gradient alignment improves test-time adaptation for medical image segmentation","author":"Chen","year":"2025"},{"key":"10.1016\/j.dsp.2026.106069_bib0068","series-title":"Proc. CVPR","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.dsp.2026.106069_bib0069","series-title":"Proc. Neurips","first-page":"12077","article-title":"Segformer: simple and efficient design for semantic segmentation with transformers","author":"Xie","year":"2021"},{"issue":"10","key":"10.1016\/j.dsp.2026.106069_bib0070","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep high-resolution representation learning for visual recognition","volume":"43","author":"Wang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Digital Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426001880?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426001880?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T22:25:47Z","timestamp":1778106347000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1051200426001880"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":70,"alternative-id":["S1051200426001880"],"URL":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106069","relation":{},"ISSN":["1051-2004"],"issn-type":[{"value":"1051-2004","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Masking-guided source-free domain adaptation for nighttime semantic segmentation","name":"articletitle","label":"Article Title"},{"value":"Digital Signal Processing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106069","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106069"}}