Simone Alberto Peirone
EGO-T^3: Test Time Training for Egocentric videos.
Rel. Barbara Caputo, Mirco Planamente, Chiara Plizzari. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2022
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Abstract
In the last few years, the technological advancement of wearable cameras has led to an increasing interest in egocentric (first-person) vision. The ability to capture activities from the user's perspective has provided significant opportunities for a more in-depth study of human behavior compared to the third-person setting, as sensors are much closer to actions and embed a natural form of attention that stems from the human gaze direction. The research community highly benefited from egocentric vision for a variety of different tasks, such as human-object interaction, action prediction and anticipation, wearer pose estimation, and video anonymization. A crucial aspect for several video-related tasks is their multimodal nature.
Audio, RGB, and optical flow provide complementary insights that are critical to a thorough understanding of the real world
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