Vision-Language Models (VLMs) handle labeled and unlabeled data through different approaches tailored to the nature of the data. Labeled data consists of images paired with descriptive text, which helps the model learn the relationship between visual content and language. For instance, a labeled example might include an image of a cat along with the caption "A cat sitting on a couch." By training on these datasets, the model develops the ability to understand and generate contextually relevant descriptions for new images.
Unlabeled data, on the other hand, lacks explicit annotations but can still provide valuable information. VLMs often use techniques like self-supervised learning to create useful representations from this type of data. For example, a model might be trained to predict a portion of an image from its accompanying text or vice versa. This predictive task can help the model learn generalized features from a broader set of images and texts, thereby improving its performance when faced with new labeled data or real-world scenarios.
Additionally, combining both labeled and unlabeled data can enhance the training process. Many VLMs utilize transfer learning, where the model starts with a large amount of unlabeled data to learn general features, and then fine-tunes on a smaller set of labeled examples. This approach allows developers to leverage vast amounts of online images and descriptions, streamlining the model training process while still achieving high performance on specific tasks. In summary, VLMs effectively utilize both labeled and unlabeled data through a mix of supervised learning, self-supervised tasks, and transfer learning strategies.