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Found 6 papers, 2 papers with code
AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space
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.
Adaptation of Surgical Activity Recognition Models Across Operating Rooms
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.
An Empirical Study on Activity Recognition in Long Surgical Videos
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.
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions
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).
Adversarial Representation Active Learning
Active learning aims to develop label-efficient algorithms by querying the most informative samples to be labeled by an oracle.
OBTAIN: Real-Time Beat Tracking in Audio Signals
In this paper, we design a system in order to perform the real-time beat tracking for an audio signal.
