{"paper":{"title":"An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE","stat.ML"],"primary_cat":"cs.AI","authors_text":"Alexis Lechervy, Corentin Kervadec, Fr\\'ed\\'eric Jurie, St\\'ephane Pateux, Valentin Vielzeuf","submitted_at":"2018-08-08T08:43:43Z","abstract_excerpt":"This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition. To design this model, the authors  followed a philosophy of simplicity, drastically limiting the number of parameters to learn from the target datasets, always choosing the simplest  earning methods: i) transfer learning and low-dimensional space embedding allows to reduce the dimensionality of the representations. ii) The  isual temporal information is handled by a simple score-per-frame selection process, averaged across time. iii) A simple frame selection  echanism is also proposed to weig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.02668","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}