Long-term Behaviour Recognition in Videos with Actor-focused Region Attention
Long-Term activities involve humans performing complex, minutes-long actions. Differently than in traditional action recognition, complex activities are normally composed of a set of sub-actions, that can appear in different order, duration, and quantity. These aspects introduce a large intra-class variability, that can be hard to model. Our approach aims to adaptively capture and learn the importance of spatial and temporal video regions for minutes-long activity classification. Inspired by previous work on Region Attention, our architecture embeds the spatio-temporal features from multiple v... Mehr ...
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Dokumenttyp: | conferencePaper |
Erscheinungsdatum: | 2021 |
Schlagwörter: | Netherlands / Knowmad Institut |
Sprache: | unknown |
Permalink: | https://search.fid-benelux.de/Record/base-27591205 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://www.openaccessrepository.it/record/131971 |