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Article overview
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Multi-Label Activity Recognition using Activity-specific Features | | Yanyi Zhang
; Xinyu Li
; Ivan Marsic
; | | Date: |
16 Sep 2020 | | Abstract: | We introduce an approach to multi-label activity recognition by extracting
independent feature descriptors for each activity. Our approach first extracts
a set of independent feature snippets, focused on different spatio-temporal
regions of a video, that we call "observations". We then generate independent
feature descriptors for each activity, that we call "activity-specific
features" by combining these observations with attention, and further make
action prediction based on these activity-specific features. This structure can
be trained end-to-end and plugged into any existing network structures for
video classification. Our method outperformed state-of-the-art approaches on
three multi-label activity recognition datasets. We also evaluated the method
and achieved state-of-the-art performance on two single-activity recognition
datasets to show the generalizability of our approach. Furthermore, to better
understand the activity-specific features that the system generates, we
visualized these activity-specific features in the Charades dataset. | | Source: | arXiv, 2009.07420 | | Services: | Forum | Review | PDF | Favorites | |
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