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- W4313023218 abstract "Action anticipation involves predicting future actions having observed the initial portion of a video. Typically, the observed video is processed as a whole to obtain a video-level representation of the ongoing activity in the video, which is then used for future prediction. We introduce Anticipatr which performs long-term action anticipation leveraging segment-level representations learned using individual segments from different activities, in addition to a video-level representation. We propose a two-stage learning approach to train a novel transformer-based model that uses these two types of representations to directly predict a set of future action instances over any given anticipation duration. Results on Breakfast, 50Salads, Epic-Kitchens-55, and EGTEA Gaze+ datasets demonstrate the effectiveness of our approach." @default.
- W4313023218 created "2023-01-05" @default.
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- W4313023218 date "2022-01-01" @default.
- W4313023218 modified "2023-09-30" @default.
- W4313023218 title "Rethinking Learning Approaches for Long-Term Action Anticipation" @default.
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- W4313023218 doi "https://doi.org/10.1007/978-3-031-19830-4_32" @default.
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