7ABAW-Compound Expression Recognition via Curriculum Learning
Journal:
arXiv
Published Date:
Mar 11, 2025
Abstract
With the advent of deep learning, expression recognition has made significant
advancements. However, due to the limited availability of annotated compound
expression datasets and the subtle variations of compound expressions, Compound
Emotion Recognition (CE) still holds considerable potential for exploration. To
advance this task, the 7th Affective Behavior Analysis in-the-wild (ABAW)
competition introduces the Compound Expression Challenge based on C-EXPR-DB, a
limited dataset without labels. In this paper, we present a curriculum
learning-based framework that initially trains the model on single-expression
tasks and subsequently incorporates multi-expression data. This design ensures
that our model first masters the fundamental features of basic expressions
before being exposed to the complexities of compound emotions. Specifically,
our designs can be summarized as follows: 1) Single-Expression Pre-training:
The model is first trained on datasets containing single expressions to learn
the foundational facial features associated with basic emotions. 2) Dynamic
Compound Expression Generation: Given the scarcity of annotated compound
expression datasets, we employ CutMix and Mixup techniques on the original
single-expression images to create hybrid images exhibiting characteristics of
multiple basic emotions. 3) Incremental Multi-Expression Integration: After
performing well on single-expression tasks, the model is progressively exposed
to multi-expression data, allowing the model to adapt to the complexity and
variability of compound expressions. The official results indicate that our
method achieves the \textbf{best} performance in this competition track with an
F-score of 0.6063. Our code is released at https://github.com/YenanLiu/ABAW7th.
