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When it comes to wild conditions, Facial Expression Recognition is oftenchallenged with low-quality data and imbalanced, ambiguous labels. This fieldhas much benefited from CNN based approaches; however, CNN models havestructural 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 enjoytheir strong inductive bias at hands. We herein raise a question whether usingthe deterministic interpolation method is enough to feed low-resolution data toTransformer. In this work, we propose a novel training framework, NeuralResizer, to support Transformer by compensating information and downscaling ina data-driven manner trained with loss function balancing the noisiness andimbalance. Experiments show our Neural Resizer with F-PDLS loss functionimproves the performance with Transformer variants in general and nearlyachieves the state-of-the-art performance.

Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task