Amirshayan Nasirimajd
Sequential Domain Generalisation for Egocentric Action Recognition.
Rel. Giuseppe Bruno Averta, Chiara Plizzari, Simone Alberto Peirone, Marco Ciccone. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024
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Abstract
Due to the widespread popularity and accessibility of wearable devices, a substantial volume of egocentric (first-person) video data has become readily accessible. This has resulted in a growing interest of researchers in the field of egocentric vision understanding. This field of study holds significant potential in several areas, especially in robotics and the analysis of human behaviour. Additionally, gaining insights into human behaviour from an egocentric perspective can offer valuable insights to robotics experts, facilitating the development of robots with more human-like visual capabilities and a deeper comprehension of their surroundings similar to humans. One of the main applications of egocentric vision is recognising the activities carried out by the wearer.
However, one limitation when deploying action recognition models to real-world scenarios is that visual appearance data such as RGB inputs vary a lot when presented with new data distributions different from the training set, which inevitably leads to a decline in model performance
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