{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T23:59:30Z","timestamp":1782863970701,"version":"3.54.5"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Vision and Image Understanding"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.cviu.2026.104857","type":"journal-article","created":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T15:40:30Z","timestamp":1781970030000},"page":"104857","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Redundancy-aware memory update for improved video object segmentation"],"prefix":"10.1016","volume":"270","author":[{"given":"Nian","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubing","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengbin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianchao","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pinle","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengcheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cviu.2026.104857_b1","series-title":"European Conference on Computer Vision","first-page":"777","article-title":"Learning what to learn for video object segmentation","author":"Bhat","year":"2020"},{"key":"10.1016\/j.cviu.2026.104857_b2","doi-asserted-by":"crossref","unstructured":"Caelles, S., Maninis, K.-K., Pont-Tuset, J., Leal-Taix\u00e9, L., Cremers, D., Van Gool, L., 2017. One-shot video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 221\u2013230.","DOI":"10.1109\/CVPR.2017.565"},{"key":"10.1016\/j.cviu.2026.104857_b3","doi-asserted-by":"crossref","DOI":"10.1109\/TMM.2025.3618564","article-title":"Learning efficient and adaptive cross-channel dependencies for weakly-supervised object detection","author":"Chen","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.cviu.2026.104857_b4","unstructured":"Chen, Y., Qian, S., Tang, H., Lai, X., Liu, Z., Han, S., Jia, J., 2023. Longlora: Efficient fine-tuning of long-context large language models. arXiv preprint arXiv:2309.12307."},{"key":"10.1016\/j.cviu.2026.104857_b5","doi-asserted-by":"crossref","unstructured":"Cheng, H.K., Oh, S.W., Price, B., Lee, J.-Y., Schwing, A., 2024a. Putting the object back into video object segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3151\u20133161.","DOI":"10.1109\/CVPR52733.2024.00304"},{"key":"10.1016\/j.cviu.2026.104857_b6","doi-asserted-by":"crossref","unstructured":"Cheng, H.K., Oh, S.W., Price, B., Lee, J.-Y., Schwing, A., 2024b. Putting the object back into video object segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 3151\u20133161.","DOI":"10.1109\/CVPR52733.2024.00304"},{"key":"10.1016\/j.cviu.2026.104857_b7","first-page":"11781","article-title":"Rethinking space-time networks with improved memory coverage for efficient video object segmentation","volume":"34","author":"Cheng","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cviu.2026.104857_b8","series-title":"2025 IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"1176","article-title":"Ptq4vm: Post-training quantization for visual mamba","author":"Cho","year":"2025"},{"key":"10.1016\/j.cviu.2026.104857_b9","series-title":"European Conference on Computer Vision","first-page":"446","article-title":"Tackling background distraction in video object segmentation","author":"Cho","year":"2022"},{"key":"10.1016\/j.cviu.2026.104857_b10","series-title":"Rethinking attention with performers","author":"Choromanski","year":"2020"},{"key":"10.1016\/j.cviu.2026.104857_b11","first-page":"9355","article-title":"Twins: Revisiting the design of spatial attention in vision transformers","volume":"34","author":"Chu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cviu.2026.104857_b12","series-title":"Flashattention-2: Faster attention with better parallelism and work partitioning","author":"Dao","year":"2023"},{"key":"10.1016\/j.cviu.2026.104857_b13","doi-asserted-by":"crossref","first-page":"16344","DOI":"10.52202\/068431-1189","article-title":"Flashattention: Fast and memory-efficient exact attention with io-awareness","volume":"35","author":"Dao","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cviu.2026.104857_b14","doi-asserted-by":"crossref","unstructured":"Ding, H., Liu, C., He, S., Jiang, X., Torr, P.H., Bai, S., 2023. MOSE: A new dataset for video object segmentation in complex scenes. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 20224\u201320234.","DOI":"10.1109\/ICCV51070.2023.01850"},{"key":"10.1016\/j.cviu.2026.104857_b15","series-title":"European Conference on Computer Vision","first-page":"225","article-title":"Unified embedding alignment for open-vocabulary video instance segmentation","author":"Fang","year":"2024"},{"key":"10.1016\/j.cviu.2026.104857_b16","doi-asserted-by":"crossref","unstructured":"Ge, W., Lu, X., Shen, J., 2021. Video object segmentation using global and instance embedding learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 16836\u201316845.","DOI":"10.1109\/CVPR46437.2021.01656"},{"key":"10.1016\/j.cviu.2026.104857_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128076","article-title":"Structural transformer with region strip attention for video object segmentation","volume":"596","author":"Guan","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.cviu.2026.104857_b18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.cviu.2026.104857_b19","doi-asserted-by":"crossref","unstructured":"Hong, L., Chen, W., Liu, Z., Zhang, W., Guo, P., Chen, Z., Zhang, W., 2023. Lvos: A benchmark for long-term video object segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 13480\u201313492.","DOI":"10.1109\/ICCV51070.2023.01240"},{"key":"10.1016\/j.cviu.2026.104857_b20","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W., 2019. Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 603\u2013612.","DOI":"10.1109\/ICCV.2019.00069"},{"key":"10.1016\/j.cviu.2026.104857_b21","doi-asserted-by":"crossref","unstructured":"Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., Kalenichenko, D., 2018. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2704\u20132713.","DOI":"10.1109\/CVPR.2018.00286"},{"key":"10.1016\/j.cviu.2026.104857_b22","doi-asserted-by":"crossref","first-page":"52481","DOI":"10.52202\/079017-1663","article-title":"Minference 1.0: Accelerating pre-filling for long-context llms via dynamic sparse attention","volume":"37","author":"Jiang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cviu.2026.104857_b23","series-title":"International Conference on Machine Learning","first-page":"5156","article-title":"Transformers are rnns: Fast autoregressive transformers with linear attention","author":"Katharopoulos","year":"2020"},{"key":"10.1016\/j.cviu.2026.104857_b24","series-title":"International Conference on Machine Learning","first-page":"5506","article-title":"I-bert: Integer-only bert quantization","author":"Kim","year":"2021"},{"key":"10.1016\/j.cviu.2026.104857_b25","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.cviu.2026.104857_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109214","article-title":"Coherence-aware context aggregator for fast video object segmentation","volume":"136","author":"Lan","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.cviu.2026.104857_b27","first-page":"1205","article-title":"Learning to learn better for video object segmentation","volume":"vol. 37, no. 1","author":"Lan","year":"2023"},{"issue":"7553","key":"10.1016\/j.cviu.2026.104857_b28","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.cviu.2026.104857_b29","unstructured":"Li, X., Miao, D., He, Z., Wang, Y., Lu, H., Yang, M.-H., 2024. Learning spatial-semantic features for robust video object segmentation. In: The Thirteenth International Conference on Learning Representations."},{"key":"10.1016\/j.cviu.2026.104857_b30","series-title":"Uniformer: Unified transformer for efficient spatiotemporal representation learning","author":"Li","year":"2022"},{"key":"10.1016\/j.cviu.2026.104857_b31","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B., 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.cviu.2026.104857_b32","doi-asserted-by":"crossref","unstructured":"Liu, Q., Wang, J., Yang, Z., Li, L., Lin, K., Niethammer, M., Wang, L., 2025. Livos: Light video object segmentation with gated linear matching. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 8668\u20138678.","DOI":"10.1109\/CVPR52734.2025.00810"},{"key":"10.1016\/j.cviu.2026.104857_b33","series-title":"European Conference on Computer Vision","first-page":"661","article-title":"Video object segmentation with episodic graph memory networks","author":"Lu","year":"2020"},{"issue":"6","key":"10.1016\/j.cviu.2026.104857_b34","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1109\/TPAMI.2018.2838670","article-title":"Video object segmentation without temporal information","volume":"41","author":"Maninis","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.cviu.2026.104857_b35","series-title":"European Conference on Computer Vision","first-page":"91","article-title":"Spatial-temporal multi-level association for video object segmentation","author":"Miao","year":"2024"},{"key":"10.1016\/j.cviu.2026.104857_b36","series-title":"Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems","first-page":"84","article-title":"Pareto multi objective optimization","author":"Ngatchou","year":"2005"},{"key":"10.1016\/j.cviu.2026.104857_b37","doi-asserted-by":"crossref","unstructured":"Oh, S.W., Lee, J.-Y., Sunkavalli, K., Kim, S.J., 2018. Fast video object segmentation by reference-guided mask propagation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7376\u20137385.","DOI":"10.1109\/CVPR.2018.00770"},{"key":"10.1016\/j.cviu.2026.104857_b38","doi-asserted-by":"crossref","unstructured":"Oh, S.W., Lee, J.-Y., Xu, N., Kim, S.J., 2019. Video object segmentation using space-time memory networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 9226\u20139235.","DOI":"10.1109\/ICCV.2019.00932"},{"key":"10.1016\/j.cviu.2026.104857_b39","doi-asserted-by":"crossref","unstructured":"Pan, F., Fang, H., Li, F., Xu, Y., Li, Y., Benini, L., Lu, X., 2025. Semantic and sequential alignment for referring video object segmentation. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 19067\u201319076.","DOI":"10.1109\/CVPR52734.2025.01776"},{"issue":"8","key":"10.1016\/j.cviu.2026.104857_b40","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.cviu.2026.104857_b41","series-title":"The 2017 davis challenge on video object segmentation","author":"Pont-Tuset","year":"2017"},{"key":"10.1016\/j.cviu.2026.104857_b42","series-title":"Sam 2: Segment anything in images and videos","author":"Ravi","year":"2024"},{"key":"10.1016\/j.cviu.2026.104857_b43","doi-asserted-by":"crossref","unstructured":"Robinson, A., Lawin, F.J., Danelljan, M., Khan, F.S., Felsberg, M., 2020. Learning fast and robust target models for video object segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 7406\u20137415.","DOI":"10.1109\/CVPR42600.2020.00743"},{"key":"10.1016\/j.cviu.2026.104857_b44","series-title":"2007 15th European Signal Processing Conference","first-page":"606","article-title":"The effective rank: A measure of effective dimensionality","author":"Roy","year":"2007"},{"key":"10.1016\/j.cviu.2026.104857_b45","series-title":"European Conference on Computer Vision","first-page":"629","article-title":"Kernelized memory network for video object segmentation","author":"Seong","year":"2020"},{"key":"10.1016\/j.cviu.2026.104857_b46","doi-asserted-by":"crossref","first-page":"68658","DOI":"10.52202\/079017-2193","article-title":"Flashattention-3: Fast and accurate attention with asynchrony and low-precision","volume":"37","author":"Shah","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cviu.2026.104857_b47","doi-asserted-by":"crossref","unstructured":"Tokmakov, P., Li, J., Gaidon, A., 2023. Breaking the\u201d object\u201d in video object segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 22836\u201322845.","DOI":"10.1109\/CVPR52729.2023.02187"},{"key":"10.1016\/j.cviu.2026.104857_b48","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cviu.2026.104857_b49","series-title":"Skip-attention: Improving vision transformers by paying less attention","author":"Venkataramanan","year":"2023"},{"key":"10.1016\/j.cviu.2026.104857_b50","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., Chen, L.-C., 2019. Feelvos: Fast end-to-end embedding learning for video object segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9481\u20139490.","DOI":"10.1109\/CVPR.2019.00971"},{"key":"10.1016\/j.cviu.2026.104857_b51","series-title":"Online adaptation of convolutional neural networks for video object segmentation","author":"Voigtlaender","year":"2017"},{"key":"10.1016\/j.cviu.2026.104857_b52","series-title":"Linformer: Self-attention with linear complexity","author":"Wang","year":"2020"},{"key":"10.1016\/j.cviu.2026.104857_b53","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L., 2021. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 568\u2013578.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"10.1016\/j.cviu.2026.104857_b54","series-title":"International Conference on Machine Learning","first-page":"38087","article-title":"Smoothquant: Accurate and efficient post-training quantization for large language models","author":"Xiao","year":"2023"},{"key":"10.1016\/j.cviu.2026.104857_b55","series-title":"Efficient streaming language models with attention sinks","author":"Xiao","year":"2023"},{"key":"10.1016\/j.cviu.2026.104857_b56","doi-asserted-by":"crossref","first-page":"119638","DOI":"10.52202\/079017-3801","article-title":"Infllm: Training-free long-context extrapolation for llms with an efficient context memory","volume":"37","author":"Xiao","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cviu.2026.104857_b57","doi-asserted-by":"crossref","unstructured":"Xie, H., Yao, H., Zhou, S., Zhang, S., Sun, W., 2021. Efficient regional memory network for video object segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 1286\u20131295.","DOI":"10.1109\/CVPR46437.2021.00134"},{"key":"10.1016\/j.cviu.2026.104857_b58","first-page":"2946","article-title":"Reliable propagation-correction modulation for video object segmentation","volume":"vol. 36, no. 3","author":"Xu","year":"2022"},{"key":"10.1016\/j.cviu.2026.104857_b59","doi-asserted-by":"crossref","unstructured":"Xu, N., Yang, L., Fan, Y., Yang, J., Yue, D., Liang, Y., Price, B., Cohen, S., Huang, T., 2018. Youtube-vos: Sequence-to-sequence video object segmentation. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 585\u2013601.","DOI":"10.1007\/978-3-030-01228-1_36"},{"issue":"9","key":"10.1016\/j.cviu.2026.104857_b60","doi-asserted-by":"crossref","first-page":"6247","DOI":"10.1109\/TPAMI.2024.3383592","article-title":"Scalable video object segmentation with identification mechanism","volume":"46","author":"Yang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.cviu.2026.104857_b61","series-title":"European Conference on Computer Vision","first-page":"332","article-title":"Collaborative video object segmentation by foreground-background integration","author":"Yang","year":"2020"},{"issue":"9","key":"10.1016\/j.cviu.2026.104857_b62","first-page":"4701","article-title":"Collaborative video object segmentation by multi-scale foreground-background integration","volume":"44","author":"Yang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.cviu.2026.104857_b63","doi-asserted-by":"crossref","first-page":"36324","DOI":"10.52202\/068431-2632","article-title":"Decoupling features in hierarchical propagation for video object segmentation","volume":"35","author":"Yang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cviu.2026.104857_b64","doi-asserted-by":"crossref","unstructured":"Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y., Wang, X., Feng, J., Yan, S., 2022. Metaformer is actually what you need for vision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 10819\u201310829.","DOI":"10.1109\/CVPR52688.2022.01055"},{"key":"10.1016\/j.cviu.2026.104857_b65","doi-asserted-by":"crossref","unstructured":"Yvinec, E., Dapogny, A., Cord, M., Bailly, K., 2023. Spiq: Data-free per-channel static input quantization. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 3869\u20133878.","DOI":"10.1109\/WACV56688.2023.00386"},{"key":"10.1016\/j.cviu.2026.104857_b66","series-title":"Sageattention: Accurate 8-bit attention for plug-and-play inference acceleration","author":"Zhang","year":"2024"},{"key":"10.1016\/j.cviu.2026.104857_b67","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"18602","article-title":"Rmem: Restricted memory banks improve video object segmentation","author":"Zhou","year":"2024"},{"issue":"6","key":"10.1016\/j.cviu.2026.104857_b68","doi-asserted-by":"crossref","first-page":"7099","DOI":"10.1109\/TPAMI.2022.3225573","article-title":"A survey on deep learning technique for video segmentation","volume":"45","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Computer Vision and Image Understanding"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1077314226002249?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1077314226002249?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T23:17:25Z","timestamp":1782861445000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1077314226002249"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":68,"alternative-id":["S1077314226002249"],"URL":"https:\/\/doi.org\/10.1016\/j.cviu.2026.104857","relation":{},"ISSN":["1077-3142"],"issn-type":[{"value":"1077-3142","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Redundancy-aware memory update for improved video object segmentation","name":"articletitle","label":"Article Title"},{"value":"Computer Vision and Image Understanding","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cviu.2026.104857","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":"104857"}}