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A New Framework for Multiple Deep Correlation Filters Based Object Tracking.

, , , , и . ICASSP, стр. 1670-1674. IEEE, (2022)

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Jinglan Wu

Recovery of citric acid from fermentation broth using simulated moving bed technology: = Reinigung von Zitronensäure aus Fermentationslösung durch kontinuierliche Chromatographie. Uni Erlangen-Nürnberg, (2009)

Jinjin Wu

Haiyan Wu

Changzhu Wu

Yinghua Wu

 

Другие публикации лиц с тем же именем

Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking., , , , и . CoRR, (2020)A New Framework for Multiple Deep Correlation Filters Based Object Tracking., , , , и . ICASSP, стр. 1670-1674. IEEE, (2022)Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking., , , , и . ACM Multimedia, стр. 473-481. ACM, (2019)Learning Quality Evaluation of MOOC Based on Big Data Analysis., , , и . SmartCom, том 10135 из Lecture Notes in Computer Science, стр. 277-286. Springer, (2016)Deep Correlation Filter Tracking With Shepherded Instance-Aware Proposals., , , , и . IEEE Trans. Intell. Transp. Syst., 23 (8): 11408-11421 (2022)Scalable Video Object Segmentation with Simplified Framework., , , и . ICCV, стр. 13833-13843. IEEE, (2023)Meta-Graph Adaptation for Visual Object Tracking., и . ICME, стр. 1-6. IEEE, (2021)Robust Unsupervised Crowd Counting and Localization with Adaptive Resolution SAM., , , и . CoRR, (2024)DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking., , , , и . ACCV (5), том 11365 из Lecture Notes in Computer Science, стр. 119-134. Springer, (2018)Learning Tracking Representations from Single Point Annotations., и . CoRR, (2024)