Unifying Few- and Zero-Shot Egocentric Action Recognition

• URL: http://arxiv.org/abs/2006.11393v1
• Date: Wed, 27 May 2020 02:23:38 GMT
• Title: Unifying Few- and Zero-Shot Egocentric Action Recognition
• Authors: Tyler R. Scott, Michael Shvartsman and Karl Ridgeway
• Abstract summary: We propose a new set of splits derived from the EPIC-KITCHENS dataset that allow evaluation of open-set classification. We show that adding a metric-learning loss to the conventional direct-alignment baseline can improve zero-shot classification by as much as 10%.
• Score: 3.1368611610608848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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