no code implementations • 23 Jan 2024 • Ali Mottaghi, Mohammad Abdullah Jamal, Serena Yeung, Omid Mohareri
Our method's effectiveness is validated through extensive experiments on benchmark datasets such as DomainNet, Office-Home, and VisDA-C, where AdaEmbed consistently outperforms all the baselines, setting a new state of the art for SSDA.
no code implementations • 7 Jul 2022 • Ali Mottaghi, Aidean Sharghi, Serena Yeung, Omid Mohareri
We propose a new domain adaptation method to improve the performance of the surgical activity recognition model in a new operating room for which we only have unlabeled videos.
no code implementations • 5 May 2022 • Zhuohong He, Ali Mottaghi, Aidean Sharghi, Muhammad Abdullah Jamal, Omid Mohareri
In this paper, we investigate many state-of-the-art backbones and temporal models to find architectures that yield the strongest performance for surgical activity recognition.
no code implementations • 12 Nov 2020 • Ali Mottaghi, Prathusha K Sarma, Xavier Amatriain, Serena Yeung, Anitha Kannan
We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking).
1 code implementation • 20 Dec 2019 • Ali Mottaghi, Serena Yeung
Active learning aims to develop label-efficient algorithms by querying the most informative samples to be labeled by an oracle.
1 code implementation • 7 Apr 2017 • Ali Mottaghi, Kayhan Behdin, Ashkan Esmaeili, Mohammadreza Heydari, Farokh Marvasti
In this paper, we design a system in order to perform the real-time beat tracking for an audio signal.