An AutoML Approach for Predicting Risk of Progression to Active Tuberculosis based on Its Association with Host Genetic Variations

Dou, Wanying; Liu, Yihang; Liu, Zehai; Yerezhepov, Dauren; Kozhamkulov, Ulan; Akilzhanova, Ainur; Dib, Omar; Chan, Chee–Kai

doi:10.1145/3498731.3498743

Cited by 3 publications

(1 citation statement)

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“…Recently, a research trend known as Automated Machine Learning (AutoML) has been intensively studied to automate the processes of finding an optimal architecture for a given problem [ 48 , 49 ]. AutoML has given remarkable results in challenging fields such the medical one [ 50 ] and has been also successfully adopted for action recognition [ 51 ]. This could be an interesting starting point for future developments to further improve our proposed approach.…”

Section: Related Workmentioning

confidence: 99%

Joint-Based Action Progress Prediction

Pucci

Becattini

Bimbo

2023

Sensors

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Action understanding is a fundamental computer vision branch for several applications, ranging from surveillance to robotics. Most works deal with localizing and recognizing the action in both time and space, without providing a characterization of its evolution. Recent works have addressed the prediction of action progress, which is an estimate of how far the action has advanced as it is performed. In this paper, we propose to predict action progress using a different modality compared to previous methods: body joints. Human body joints carry very precise information about human poses, which we believe are a much more lightweight and effective way of characterizing actions and therefore their execution. Estimating action progress can in fact be determined based on the understanding of how key poses follow each other during the development of an activity. We show how an action progress prediction model can exploit body joints and integrate it with modules providing keypoint and action information in order to be run directly from raw pixels. The proposed method is experimentally validated on the Penn Action Dataset.

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Section: Related Workmentioning

confidence: 99%