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- W4387151330 abstract "ENIGMA-51 is a new egocentric dataset acquired in a real industrial domain by 19 subjects who followed instructions to complete the repair of electrical boards using industrial tools (e.g., electric screwdriver) and electronic instruments (e.g., oscilloscope). The 51 sequences are densely annotated with a rich set of labels that enable the systematic study of human-object interactions in the industrial domain. We provide benchmarks on four tasks related to human-object interactions: 1) untrimmed action detection, 2) egocentric human-object interaction detection, 3) short-term object interaction anticipation and 4) natural language understanding of intents and entities. Baseline results show that the ENIGMA-51 dataset poses a challenging benchmark to study human-object interactions in industrial scenarios. We publicly release the dataset at: https://iplab.dmi.unict.it/ENIGMA-51/." @default.
- W4387151330 created "2023-09-29" @default.
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- W4387151330 date "2023-09-26" @default.
- W4387151330 modified "2023-09-30" @default.
- W4387151330 title "ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios" @default.
- W4387151330 doi "https://doi.org/10.48550/arxiv.2309.14809" @default.
- W4387151330 hasPublicationYear "2023" @default.
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