Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Litong Feng"'
Autor:
Yajie Guan, Xiufang Cui, Di Chen, Wennan Su, Yao Zhao, Jian Li, Litong Feng, Xinyao Li, Guo Jin
Publikováno v:
Materials Today Communications. 35:106098
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bfcfee19aa4e2a57e6f855bdecc9aa4b
https://doi.org/10.1016/j.mtcomm.2023.106098
https://doi.org/10.1016/j.mtcomm.2023.106098
Publikováno v:
Materials Today Communications. 35:106207
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35141e16805fa9d0e163b25d50a6b0b2
https://doi.org/10.1016/j.mtcomm.2023.106207
https://doi.org/10.1016/j.mtcomm.2023.106207
Publikováno v:
Surface and Coatings Technology. 461:129447
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e725e6118502296930584380ab193eb
https://doi.org/10.1016/j.surfcoat.2023.129447
https://doi.org/10.1016/j.surfcoat.2023.129447
Autor:
Yuzhi Zhao, Litong Feng, Yasar Abbas Ur Rehman, Lai-Man Po, Weifeng Ou, Zhang Yujia, Chang Zhou
Publikováno v:
IEEE Access, Vol 8, Pp 109758-109769 (2020)
In recent years, the angle-based softmax losses have significantly improved the performance of face recognition whereas these loss functions are all based on cosine logit. A potential weakness is that the nonlinearity of the cosine function may undes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d6a9670c0e08e67f02e8a4a92d7c0d7
https://doi.org/10.1109/access.2020.3002270
https://doi.org/10.1109/access.2020.3002270
Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability. However, the immense diversity of the OOD examples makes such methods fragile. There are OOD sample
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2f72a7f2a4234a98667f874300003a7
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Current out-of-distribution (OOD) detection benchmarks are commonly built by defining one dataset as in-distribution (ID) and all others as OOD. However, these benchmarks unfortunately introduce some unwanted and impractical goals, e.g., to perfectly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ded5894a56b9134d58ed8a878fcc3088
https://doi.org/10.1109/iccv48922.2021.00819
https://doi.org/10.1109/iccv48922.2021.00819
Publikováno v:
Applied Surface Science. 593:153381
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0354bf37b288b4e3d7f058a307a7e91f
https://doi.org/10.1016/j.apsusc.2022.153381
https://doi.org/10.1016/j.apsusc.2022.153381
Webly supervised learning becomes attractive recently for its efficiency in data expansion without expensive human labeling. However, adopting search queries or hashtags as web labels of images for training brings massive noise that degrades the perf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd1c61322b422e656ba067ccb1496ea6
http://arxiv.org/abs/2010.05864
http://arxiv.org/abs/2010.05864
Publikováno v:
Materials Chemistry and Physics. 280:125756
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::551ef8de48f76bb8ad8b3f9165fe6c7e
https://doi.org/10.1016/j.matchemphys.2022.125756
https://doi.org/10.1016/j.matchemphys.2022.125756
Publikováno v:
ACM Multimedia
Most advances in single image de-raining meet a key challenge, which is removing rain streaks with different scales and shapes while preserving image details. Existing single image de-raining approaches treat rain-streak removal as a process of pixel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::766d3586f88262ae3fb1440040201b04