Recognizing Micro-Actions and Reactions from Paired Egocentric Videos

By Ryo Yonetani, Kris M. Kitani, Yoichi Sato. 2016

94 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Computer Interaction

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Citations in the corpus

Ego4D: Around the World in 3,000 Hours of Egocentric Video (2021, neutral): “Aside from daily life activity, prior ego datasets focus on conversation [170], inter-person interactions [66,168,194,231], place localization [183,208], multimodal sensor data [124,166,204], human hands [16, 134] human-object interaction [106, 184], and object tracking [56].”
Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals (2017, background): “Motion-based approaches [33, 51, 55, 68, 57] assume that each activity creates coherent motion patterns.”
You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions (2019, methodology): “Existing egocentric datasets are either limited to visible hands and arms [29, 38], contain only single-person sequences [25, 5, 26], consist of synthetic test data [59], or lack body-pose joint labels [58].”
EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition (2019, background): “As a result, there has been a renewed interest from the computer vision community on collecting large-scale datasets [8, 35] as well as developing new or adapting existing methods to the firstperson point-of-view scenario [9, 17, 21, 32, 44, 46].”
In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Video (2018, background): “Other efforts included the modeling of conversations [5] and reactions [46] in social interactions.”
Egocentric Hand Track and Object-Based Human Action Recognition (2019, methodology): “Since then, egocentric vision has been applied to more elaborate applications including video summarization [3] and social interaction analysis [4].”
EgoCom: A Multi-Person Multi-Modal Egocentric Communications Dataset (2020, background): “EgoCom differs from (Fathi et al., 2012) and (Yonetani et al., 2016) in that it captures multi-perspective multi-modal communication signals that can be exploited beyond vision tasks.”
In the Eye of the Beholder: Gaze and Actions in First Person Video (2020, background): “Other efforts included the modeling of conversations [65] and reactions [8], [66] in social interactions.”