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pro vyhledávání: '"Xie, Rongchang"'
We introduce MUSE-VL, a Unified Vision-Language Model through Semantic discrete Encoding for multimodal understanding and generation. Recently, the research community has begun exploring unified models for visual generation and understanding. However
Externí odkaz:
http://arxiv.org/abs/2411.17762
Autor:
Mo, Lufeng1,2 (AUTHOR) molufeng@zafu.edu.cn, Xie, Rongchang1 (AUTHOR) 2021611011064@stu.zafu.edu.cn, Ye, Fujun3 (AUTHOR) yefuj@cuz.edu.cn, Wang, Guoying1 (AUTHOR) wp@zafu.edu.cn, Wu, Peng1 (AUTHOR) yxm@zafu.edu.cn, Yi, Xiaomei1 (AUTHOR)
Publikováno v:
Agronomy. Jun2024, Vol. 14 Issue 6, p1197. 22p.
Autor:
Wang, Guoying1 (AUTHOR) wgy@zafu.edu.cn, Xie, Rongchang1 (AUTHOR) 2021611011064@stu.zafu.edu.cn, Mo, Lufeng1,2 (AUTHOR) molufeng@zafu.edu.cn, Ye, Fujun3 (AUTHOR) yefuj@cuz.edu.cn, Yi, Xiaomei1 (AUTHOR) wp@zafu.edu.cn, Wu, Peng1 (AUTHOR)
Publikováno v:
Symmetry (20738994). Jun2024, Vol. 16 Issue 6, p723. 21p.
Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images und
Externí odkaz:
http://arxiv.org/abs/2011.12498
Cross view feature fusion is the key to address the occlusion problem in human pose estimation. The current fusion methods need to train a separate model for every pair of cameras making them difficult to scale. In this work, we introduce MetaFuse, a
Externí odkaz:
http://arxiv.org/abs/2003.13239
In recent years, object detection has shown impressive results using supervised deep learning, but it remains challenging in a cross-domain environment. The variations of illumination, style, scale, and appearance in different domains can seriously a
Externí odkaz:
http://arxiv.org/abs/1907.11484
Akademický článek
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Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images und
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f11624cdb3ccfdbf100d315bcd229622
https://doi.org/10.1109/iccv48922.2021.01105
https://doi.org/10.1109/iccv48922.2021.01105
Akademický článek
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Autor:
Erbertseder, Thilo, Esch, Thomas, Chrysoulakis, Nektarios, Wang, Guozhi, Huang, Yuchun, Xie, Rongchang, Zhang, Hongchang
Publikováno v:
Proceedings of SPIE; October 2016, Vol. 10008 Issue: 1 p100080S-100080S-10, 9907931p