Yes, Vision-Language Models (VLMs) can be trained on small datasets, but the effectiveness of training largely depends on how those datasets are structured and utilized. Training VLMs generally requires a significant amount of paired visual and textual data to capture the complex relationships between images and language. However, when working with small datasets, developers can adopt strategies to enhance the model's performance.
One common approach is to employ data augmentation techniques. For example, if the dataset consists of images and captions, developers can create variations of images through rotations, cropping, or color adjustments. Similarly, caption rewriting techniques can generate paraphrased descriptions. This way, you broaden the dataset and provide more examples for the model to learn from, thus making the small dataset feel more substantial.
Another important method is transfer learning, which involves using a pre-trained model and fine-tuning it with your smaller dataset. Pre-trained models usually have already learned many useful features from larger datasets, and by exposing them to a small amount of specialized data, they can adapt to specific tasks effectively. For instance, a model pre-trained on a broader dataset can be fine-tuned on medical images and descriptions, enabling it to perform well even with limited data. This combination of techniques can make training VLMs on smaller datasets both feasible and fruitful.