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In order to verify whether the model also works in the annotation ambiguity of unmasked facial expression datasets, we conduct the following experiments. The annotation ambiguity in the dataset is mainly manifested in the uncertain annotation of the annotator caused by the annotation error and the uncertainty of the image. We quantitatively evaluate the improvement of SCN-SAM against the mislabeled annotations on RAF-DB. In detail, we randomly select a part of the training samples and modify the labels to other categories. As summarized in Table 5, our proposed SCN-SAM achieves the best results after adding 10%, 20%, or 30% synthetic noise samples to the training samples. |
IEEE/ICACT20230035 Slide.18
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