  When it comes to wild conditions, Facial Expression Recognition is often
challenged with low-quality data and imbalanced, ambiguous labels. This field
has much benefited from CNN based approaches; however, CNN models have
structural limitation to see the facial regions in distant. As a remedy,
Transformer has been introduced to vision fields with global receptive field,
but requires adjusting input spatial size to the pretrained models to enjoy
their strong inductive bias at hands. We herein raise a question whether using
the deterministic interpolation method is enough to feed low-resolution data to
Transformer. In this work, we propose a novel training framework, Neural
Resizer, to support Transformer by compensating information and downscaling in
a data-driven manner trained with loss function balancing the noisiness and
imbalance. Experiments show our Neural Resizer with F-PDLS loss function
improves the performance with Transformer variants in general and nearly
achieves the state-of-the-art performance.
