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In this research work, to effectively suppress the problem of annotation ambiguity of images of people wearing masks, we propose SCN-SAM and design a self-adjustment module. This module can mine high-importance-weight samples in training samples and change the proportion of high- and low-importance-weight samples so that the model can pay more attention to learning the sample features of high importance weight. Experiments on masked datasets and unmasked synthetic ambiguous datasets show that our SCN-SAM can suppress the uncertainty in recognizing masked facial expressions and suppress annotation ambiguity in the facial expression datasets without masks. In the future, we will use SCN-SAM to investigate further different computer vision fields to improve the model¡¯s generality. In addition to facial expression recognition, there are many other fields in computer vision where ambiguous annotation is a problem, such as object detection or face recognition. We believe that the proposed method will also be effective in these fields. After completing the improvement of the model¡¯s generality, we will extend the model to real life to develop the application. |
IEEE/ICACT20230035 Slide.20
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