Egocentric Activity Recognition
13 papers with code • 2 benchmarks • 4 datasets
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Second, frame-based models perform quite well on action recognition; is pre-training for good image features sufficient or is pre-training for spatio-temporal features valuable for optimal transfer learning?
What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention
Our method is ranked first in the public leaderboard of the EPIC-Kitchens egocentric action anticipation challenge 2019.
Our dataset and experiments can be of interest to communities of 3D hand pose estimation, 6D object pose, and robotics as well as action recognition.
The per-frame (per-segment) extracted features are considered as a set of time series, and inter and intra-time series relations are employed to represent the video descriptors.
Our model is built on the observation that egocentric activities are highly characterized by the objects and their locations in the video.
We focus on multi-modal fusion for egocentric action recognition, and propose a novel architecture for multi-modal temporal-binding, i. e. the combination of modalities within a range of temporal offsets.
We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets.