Cutting-edge computer vision models, often referred to as state-of-the-art (SotA) models, exhibit a limitation in their comprehension of the visual world, primarily shaped by their training data [1]. While these models excel in specific tasks and datasets, their ability to generalize is limited. They struggle with novel categories or images that fall outside the scope of their original training domain. This brittleness can pose challenges when creating specialized image classification applications, such as identifying defects in agricultural products or detecting counterfeit banknotes to combat fraud. Gathering sufficiently large labeled datasets for fine-tuning conventional computer vision models in these niche areas can be exceptionally challenging. Ideally, a computer vision model should learn to grasp the content of images without fixating excessively on the specific labels it was initially trained on. For instance, when presented with an image of a dog, the model should not only