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pro vyhledávání: '"Ha, Mai Lan"'
This paper examines the robustness of a multi-modal computer vision model, CLIP (Contrastive Language-Image Pretraining), in the context of unsupervised learning. The main objective is twofold: first, to evaluate the robustness of CLIP, and second, t
Externí odkaz:
http://arxiv.org/abs/2309.10361
Autor:
Franchi, Gianni, Belkhir, Nacim, Ha, Mai Lan, Hu, Yufei, Bursuc, Andrei, Blanz, Volker, Yao, Angela
Along with predictive performance and runtime speed, reliability is a key requirement for real-world semantic segmentation. Reliability encompasses robustness, predictive uncertainty and reduced bias. To improve reliability, we introduce Superpixel-m
Externí odkaz:
http://arxiv.org/abs/2108.00968
Autor:
Ha, Mai Lan, Blanz, Volker
We propose a simple modification from a fixed margin triplet loss to an adaptive margin triplet loss. While the original triplet loss is used widely in classification problems such as face recognition, face re-identification and fine-grained similari
Externí odkaz:
http://arxiv.org/abs/2107.06187
Discriminative features play an important role in image and object classification and also in other fields of research such as semi-supervised learning, fine-grained classification, out of distribution detection. Inspired by Linear Discriminant Analy
Externí odkaz:
http://arxiv.org/abs/2107.06209
Autor:
Ha, Mai-Lan
Publikováno v:
Consilience, 2011 Jan 01(5), 125-140.
Externí odkaz:
https://www.jstor.org/stable/26167806
Autor:
Ha, Mai Lan
Machine Learning and Computer Vision are often thought to relate only to machines, involving the development of algorithms and teaching computers to perform various tasks. However, human vision and perception are hidden aspects that influence how an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e86f2a866ecd2abd13bb0b300f125bbf
Akademický článek
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Autor:
Cooley, Heather, Ajami, Newsha, Ha, Mai-Lan, Srinivasan, Veena, Morrison, Jason, Donnelly, Kristina, Christian-Smith, Juliet
Publikováno v:
World's Water; 2014, p1-18, 18p