In this paper, we introduce a framework ARBEx, a novel attentive feature
extraction framework driven by Vision Transformer with reliability balancing to
cope against poor class distributions, bias, and uncertainty in the facial
expression learning (FEL) task. We reinforce several data pre-processing and
refinement methods along with a window-based cross-attention ViT to squeeze the
best of the data. We also employ learnable anchor points in the embedding space
with label distributions and multi-head self-attention mechanism to optimize
performance against weak predictions with reliability balancing, which is a
strategy that leverages anchor points, attention scores, and confidence values
to enhance the resilience of label predictions. To ensure correct label
classification and improve the models' discriminative power, we introduce
anchor loss, which encourages large margins between anchor points.
Additionally, the multi-head self-attention mechanism, which is also trainable,
plays an integral role in identifying accurate labels. This approach provides
critical elements for improving the reliability of predictions and has a
substantial positive effect on final prediction capabilities. Our adaptive
model can be integrated with any deep neural network to forestall challenges in
various recognition tasks. Our strategy outperforms current state-of-the-art
methodologies, according to extensive experiments conducted in a variety of
contexts.

本文介绍了一种新的注意力特征提取框架 ARBEx，该框架由 Vision Transformer 驱动，具有可靠性平衡功能，可以应对面部表情学习任务中的类别分布不佳、偏差和不确定性。与基于窗口的交叉注意力 ViT 一起，我们加强了几种数据预处理和细化方法，同时在嵌入空间中引入了学习型锚点、标签分布和多头自我注意机制，以优化性能，减弱标签预测中的弱预测，从而增强标签预测的韧性。同时，引入了锚损失，以鼓励锚点之间的大间隔，提高模型的辨别能力。通过大量实验，在各种情境下，我们的方法优于目前最先进的方法。