CvS: Classification via Segmentation For Small Datasets
This addresses the challenge of limited data in computer vision applications, though it appears incremental as it builds on segmentation and label propagation methods.
The paper tackles the problem of deep learning models overfitting on small datasets by proposing CvS, a classifier that derives labels from segmentation maps, achieving much higher classification results compared to previous methods with only a handful of examples.
Deep learning models have shown promising results in a wide range of computer vision applications across various domains. The success of deep learning methods relies heavily on the availability of a large amount of data. Deep neural networks are prone to overfitting when data is scarce. This problem becomes even more severe for neural network with classification head with access to only a few data points. However, acquiring large-scale datasets is very challenging, laborious, or even infeasible in some domains. Hence, developing classifiers that are able to perform well in small data regimes is crucial for applications with limited data. This paper presents CvS, a cost-effective classifier for small datasets that derives the classification labels from predicting the segmentation maps. We employ the label propagation method to achieve a fully segmented dataset with only a handful of manually segmented data. We evaluate the effectiveness of our framework on diverse problems showing that CvS is able to achieve much higher classification results compared to previous methods when given only a handful of examples.