LEAF: Unveiling Two Sides of the Same Coin in Semi-supervised Facial Expression Recognition

arXiv:2404.15041v1 Announce Type: new
Abstract: Semi-supervised learning has emerged as a promising approach to tackle the challenge of label scarcity in facial expression recognition (FER) task. However, current state-of-the-art methods primarily focus on one side of the coin, i.e., generating high-quality pseudo-labels, while overlooking the other side: enhancing expression-relevant representations. In this paper, we unveil both sides of the coin by proposing a unified framework termed hierarchicaL dEcoupling And Fusing (LEAF) to coordinate expression-relevant representations and pseudo-labels for semi-supervised FER. …

abstract art arxiv challenge cs.cv current facial expression focus however labels quality recognition semi-supervised semi-supervised learning state supervised learning type