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• W3035807560 abstract "Although there has been significant research in egocentric action recognition, most methods and tasks, including EPIC-KITCHENS, suppose a fixed set of action classes. Fixed-set classification is useful for benchmarking methods, but is often unrealistic in practical settings due to the compositionality of actions, resulting in a functionally infinite-cardinality label set. In this work, we explore generalization with an open set of classes by unifying two popular approaches: few- and zero-shot generalization (the latter which we reframe as cross-modal few-shot generalization). We propose a new set of splits derived from the EPIC-KITCHENS dataset that allow evaluation of open-set classification, and use these splits to show that adding a metric-learning loss to the conventional direct-alignment baseline can improve zero-shot classification by as much as 10%, while not sacrificing few-shot performance." @default.
• W3035807560 created "2020-06-25" @default.
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• W3035807560 date "2020-05-27" @default.
• W3035807560 modified "2023-10-01" @default.
• W3035807560 title "Unifying Few- and Zero-Shot Egocentric Action Recognition." @default.
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