mlsanityData to 7 Oct 2026

Two-Aspect Information Fusion Model For ABAW4 Multi-task Challenge

arXiv 2207.11389 ↗First posted 23 Jul 2022cs.CV cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

In this paper, we propose the solution to the Multi-Task Learning (MTL) Challenge of the 4th Affective Behavior Analysis in-the-wild (ABAW) competition. The task of ABAW is to predict frame-level emotion descriptors from videos: discrete emotional state; valence and arousal; and action units. Although researchers have proposed several approaches and achieved promising results in ABAW, current works in this task rarely consider interactions between different emotion descriptors. To this end, we propose a novel end to end architecture to achieve full integration of different types of information. Experimental results demonstrate the effectiveness of our proposed solution.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 23 Jul 2022.

Builds on

8 of 14 references.

References · 8

  1. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  2. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  3. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  4. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
  5. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  6. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  7. 2018Photorealistic Facial Synthesis in the Dimensional Affect Spacecited by 33
  8. 2022Multiple Emotion Descriptors Estimation at the ABAW3 Challengecited by 9

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2.

3 papers.