#19.07 - Activities recognition in videos - Automated machine learning

• "Spatio-temporal attention mechanism for Activities of Daily Living", Srijan Das (Inria - Stars)

• "Inside TADA: a peek inside MyDataModels' platform for Small Data analysis and prediction", Carlo Fanara (MyDataModels)

S. Das:
Action Recognition has been a popular problem statement in the vision community because of its large scale applications. We particularly focus on Activities of Daily Living (ADL) which can be used for monitoring hospital patients, smarthome applications and so on. In real-world videos, ADL look simple but their recognition are often more challenging than sport, Youtube or movie videos. These actions have often very low inter-class variance making the task of discriminating them from one another very challenging. The recent spatio-temporal 3D ConvNets are too rigid to capture the subtle visual patterns across an action, so we propose a novel pose driven spatio-temporal attention mechanism through 3D ConvNets. We show that our method outperforms state-of-the-art methods on large-scale NTU-RGB+D, a human-object interaction dataset - Northwestern-UCLA, and on a real-world challenging human activity dataset: Toyota Smarthome.

Das, Chaudhary, Bremond and Thonnat (2019): "Where to Focus on for Human Action Recognition?", in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Waikoloa Village, Hawaii, January 7-11, 2019.

Das, Dai, Koperski, Minciullo, Garattoni, Bremond and Francesca (2019): "Toyota Smarthome: Real-World Activities of Daily Living", in Proceedings of the 17th International Conference on Computer Vision, ICCV 2019, in Seoul, Korea, October 27 to November 2, 2019.

Das, Bremond and Thonnat (2020): "Looking deeper into Time for Activities of Daily Living Recognitioné, in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, WACV 2020, Snowmass village, Colorado, March 2-5, 2020.

C. Fanara:
Since its foundation, MyDataModels (MDM), has been specializing on small data, showing how these 'democratize' the field, allowing domain experts and professionals to access machine learning results in an unprecedented way.

Machine Learning models can be generated with artificial neural network, deep learning, but also – like in the case of MDM – using evolutionary programming (EP) and genetic algorithms (GA).

An outline about EP and GA is given. Whilst the latter may occasionally be costly in terms of execution time, ways to define early convergence can be found. This is one of the efforts currently undertaken at MDM, and it is worth, because the outcome is given with mathematical formulae which include the variables from the original dataset: thus, models become explainable and exploitable.