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The most significant difference between the SCN-SAM model and the SCN model is whether it has a SAM module. Because the importance weight is determined according to the image¡¯s uncertainty, the weight of each image is not the same. SAM can classify high and low weights according to the importance weights inputted to the module.So it 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. The increase in blurred images and the influence of ambiguous annotations due to the presence of masks causes the number ¥ä of highly-importance samples to become very unstable during training instead remaining at a constant value. In model training, SCN-SAM mines high-importance samples and classifies high and low-importance samples so that the ¥ä value is no longer constant, thus solving this problem well. |
IEEE/ICACT20230035 Slide.15
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