mlsanityData to 7 Oct 2026

EventFormer: AU Event Transformer for Facial Action Unit Event Detection

arXiv 2203.06355 ↗First posted 12 Mar 2022cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Facial action units (AUs) play an indispensable role in human emotion analysis. We observe that although AU-based high-level emotion analysis is urgently needed by real-world applications, frame-level AU results provided by previous works cannot be directly used for such analysis. Moreover, as AUs are dynamic processes, the utilization of global temporal information is important but has been gravely ignored in the literature. To this end, we propose EventFormer for AU event detection, which is the first work directly detecting AU events from a video sequence by viewing AU event detection as a multiple class-specific sets prediction problem. Extensive experiments conducted on a commonly used AU benchmark dataset, BP4D, show the superiority of EventFormer under suitable metrics.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 12 Mar 2022.

Builds on

14 of 35 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020End-to-End Object Detection with Transformerscited by 7,384
  4. 2016Layer Normalizationcited by 5,579
  5. 2020On Layer Normalization in the Transformer Architecturecited by 906
  6. 2017Soft-NMS -- Improving Object Detection With One Line of Codecited by 380
  7. 2020Visual Transformers: Token-based Image Representation and Processing for Computer Visioncited by 333
  8. 2017Temporal Action Detection with Structured Segment Networkscited by 265
  9. 2019Pyramid Feature Attention Network for Saliency detectioncited by 128
  10. 2019Fast Learning of Temporal Action Proposal via Dense Boundary Generatorcited by 89
  11. 2021Modeling Multi-Label Action Dependencies for Temporal Action Localizationcited by 32
  12. 2017Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusingcited by 28
2 more
  1. 2020Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolutioncited by 21
  2. 2020Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderercited by 2

Built on by

Not cited yet.