Learning Visual Actions Using Multiple Verb-Only Labels
This work addresses the challenge of handling ambiguous verb semantics in video analysis for computer vision researchers, offering an incremental improvement over existing methods.
The paper tackles the problem of visual action recognition and retrieval by using multiple verb-only labels to capture semantic ambiguities and contextual overlaps, outperforming conventional single verb labels on three action video datasets.
This work introduces verb-only representations for both recognition and retrieval of visual actions, in video. Current methods neglect legitimate semantic ambiguities between verbs, instead choosing unambiguous subsets of verbs along with objects to disambiguate the actions. We instead propose multiple verb-only labels, which we learn through hard or soft assignment as a regression. This enables learning a much larger vocabulary of verbs, including contextual overlaps of these verbs. We collect multi-verb annotations for three action video datasets and evaluate the verb-only labelling representations for action recognition and cross-modal retrieval (video-to-text and text-to-video). We demonstrate that multi-label verb-only representations outperform conventional single verb labels. We also explore other benefits of a multi-verb representation including cross-dataset retrieval and verb type manner and result verb types) retrieval.