mlsanityData to 14 Oct 2026

When Does a Laugh Begin? Structured Annotator Disagreement in Temporal Laughter Localization

arXiv 2609.06646 ↗First posted 6 Sep 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Annotators routinely disagree on laughter boundaries and subtle chuckles, yet temporal laughter localization typically evaluates against a single reference annotation. We show that this disagreement is structured rather than random noise. Re-annotating the SMILE-Temporal benchmark (672 videos, 1,683 events) with 3-5 annotators per video (alpha = 0.757), we find systematic patterns: disagreement is 1.73 times larger at offsets than onsets, far more common for chuckles than full laughs (77% vs. 20%), and predictable from event attributes (AUC = 0.831). Evaluating against a single annotator breaks down under this structure: system scores shift by 0.246 F1 depending on the chosen ground truth, correctly ranking systems only 69.7% of the time (vs. 80% against all annotators). We propose a disagreement-calibrated evaluation that scores predictions against the full annotator distribution using conformally calibrated tolerance bands (wider at offsets, 0.727s, than onsets, 0.5s). The per-annotator annotations and analysis code are available at https://github.com/WSCSports/MTLLFM-temporal-laughter-localization.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 6 Sep 2026.

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17 of 31 references.

References · 17

  1. 2019Human uncertainty makes classification more robustcited by 235
  2. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  3. 2022The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluationcited by 139
  4. 2014Label Distribution Learningcited by 124
  5. 2018Diagnosing Error in Temporal Action Detectorscited by 71
  6. 2021Generic Event Boundary Detection: A Benchmark for Event Segmentationcited by 65
  7. 2019SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wildcited by 62
  8. 2023SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)cited by 52
  9. 2023Dense-Localizing Audio-Visual Events in Untrimmed Videos: A Large-Scale Benchmark and Baselinecited by 46
  10. 2023SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Modelscited by 28
  11. 2017Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Videocited by 14
  12. 2022Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videoscited by 10
5 more
  1. 2016Much Ado About Time: Exhaustive Annotation of Temporal Datacited by 6
  2. 2022Impact of annotation modality on label quality and model performance in the automatic assessment of laughter in-the-wildcited by 4
  3. 2024LEAD Dataset: How Can Labels for Sound Event Detection Vary Depending on Annotators?cited by 2
  4. 2026MTLLFM: Multimodal-Temporal Laughter Localization: UR-FUNNY-Temporal and SMILE-Temporal Benchmarks with an Adaptive Multimodal Fusion Modelcited by 2
  5. 2026SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughtercited by 1

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