mlsanityData to 14 Oct 2026

Fact sheet: Automatic Self-Reported Personality Recognition Track

arXiv 2207.11012 ↗First posted 22 Jul 2022cs.CVcited by 0

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Abstract (arXiv)

We propose an informed baseline to help disentangle the various contextual factors of influence in this type of case studies. For this purpose, we analysed the correlation between the given metadata and the self-assigned personality trait scores and developed a model based solely on this information. Further, we compared the performance of this informed baseline with models based on state-of-the-art visual, linguistic and audio features. For the present dataset, a model trained solely on simple metadata features (age, gender and number of sessions) proved to have superior or similar performance when compared with simple audio, linguistic or visual features-based systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 10 references that are in the map. First posted on arXiv 22 Jul 2022.

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References · 3

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
  3. 2019On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signalscited by 11

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