Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Ambareesh Revanur"'
Publikováno v:
IEEE Transactions on Biomedical Engineering. :1-19
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e74f3becaf7da19eb7139a19e42f418
https://doi.org/10.1109/tbme.2023.3247910
https://doi.org/10.1109/tbme.2023.3247910
Publikováno v:
Multimodal AI in Healthcare ISBN: 9783031147708
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0dc1b5fad8b07a016ce63877ac8ca7d8
https://doi.org/10.1007/978-3-031-14771-5_22
https://doi.org/10.1007/978-3-031-14771-5_22
Autor:
Ambareesh Revanur, László A. Jeni
Publikováno v:
2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3bc55aa63c6e2a7289aaf0f99ec4b59
https://doi.org/10.1109/aciiw57231.2022.10086024
https://doi.org/10.1109/aciiw57231.2022.10086024
Telehealth has the potential to offset the high demand for help during public health emergencies, such as the COVID-19 pandemic. Remote Photoplethysmography (rPPG) - the problem of non-invasively estimating blood volume variations in the microvascula
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9488e49d4d5179be87cf0bd90acd9cc
http://arxiv.org/abs/2109.10471
http://arxiv.org/abs/2109.10471
Publikováno v:
Fifteenth ACM Conference on Recommender Systems.
We consider the problem of complementary fashion prediction. Existing approaches focus on learning an embedding space where fashion items from different categories that are visually compatible are closer to each other. However, creating such labeled
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11eeabdac0f518557f82c81192429c9b
https://doi.org/10.1145/3460231.3474233
https://doi.org/10.1145/3460231.3474233
Publikováno v:
CVPR Workshops
Adversarial Training (AT) is a straight forward solution to learn robust models by augmenting the training mini-batches with adversarial samples. Adversarial attack methods range from simple non-iterative (single-step) methods to computationally comp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::707f6c20025350a007c8d5612f26a044
https://doi.org/10.1109/cvprw50498.2020.00023
https://doi.org/10.1109/cvprw50498.2020.00023
Publikováno v:
CVPR
There has been a tremendous progress in Domain Adaptation (DA) for visual recognition tasks. Particularly, open-set DA has gained considerable attention wherein the target domain contains additional unseen categories. Existing open-set DA approaches
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::922d4e4c553c838860be719e0a2055a6
https://doi.org/10.1109/cvpr42600.2020.01239
https://doi.org/10.1109/cvpr42600.2020.01239
Autor:
Naveen Venkat, Jogendra Nath Kundu, Ambareesh Revanur, Rahul Mysore Venkatesh, R. Venkatesh Babu
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030586003
ECCV (13)
ECCV (13)
We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for learning novel target-domain classes. Meanwhile, class-incremental (CI) methods
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea5682f09a1a22a1ecf9222160bda4af
https://doi.org/10.1007/978-3-030-58601-0_4
https://doi.org/10.1007/978-3-030-58601-0_4
Autor:
Rahul Mysore Venkatesh, Govind Vitthal Waghmare, Ambareesh Revanur, Jogendra Nath Kundu, R. Venkatesh Babu
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030586003
ECCV (13)
ECCV (13)
We present a deployment friendly, fast bottom-up framework for multi-person 3D human pose estimation. We adopt a novel neural representation of multi-person 3D pose which unifies the position of person instances with their corresponding 3D pose repre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ef66540cf658c5253ddb4848a4b41d6
https://doi.org/10.1007/978-3-030-58601-0_3
https://doi.org/10.1007/978-3-030-58601-0_3
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