mlsanityData to 7 Oct 2026

ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios

arXiv 2309.14809 ↗First posted 26 Sep 2023cs.CVcited by 11

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Abstract (arXiv)

ENIGMA-51 is a new egocentric dataset acquired in an industrial scenario by 19 subjects who followed instructions to complete the repair of electrical boards using industrial tools (e.g., electric screwdriver) and equipments (e.g., oscilloscope). The 51 egocentric video sequences are densely annotated with a rich set of labels that enable the systematic study of human behavior in the industrial domain. We provide benchmarks on four tasks related to human behavior: 1) untrimmed temporal detection of human-object interactions, 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 behavior in industrial scenarios. We publicly release the dataset at https://iplab.dmi.unict.it/ENIGMA-51.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 48 of its 67 references that are in the map. First posted on arXiv 26 Sep 2023.

Builds on

48 of 67 references.

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36 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 3; 2026: 3.

11 papers.