mlsanityData to 14 Oct 2026

Rethinking the Learning Paradigm for Facial Expression Recognition

arXiv 2209.15402 ↗First posted 30 Sep 2022cs.CVcited by 3

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Abstract (arXiv)

Due to the subjective crowdsourcing annotations and the inherent inter-class similarity of facial expressions, the real-world Facial Expression Recognition (FER) datasets usually exhibit ambiguous annotation. To simplify the learning paradigm, most previous methods convert ambiguous annotation results into precise one-hot annotations and train FER models in an end-to-end supervised manner. In this paper, we rethink the existing training paradigm and propose that it is better to use weakly supervised strategies to train FER models with original ambiguous annotation.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 37 of its 72 references that are in the map. First posted on arXiv 30 Sep 2022.

Builds on

37 of 72 references.

References · 37

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25 more
  1. 2020Understanding the Behaviour of Contrastive Losscited by 398
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024Faceptor: A Generalist Model for Face Perceptioncited by 13
  2. 2024D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognitioncited by 3
  3. 2026Medoid Prototype Alignment for Cross-Plant Unknown Attack Detection in Industrial Control Systemscited by 0