How do Vision-Language Models deal with labeled and unlabeled data?

Vision-Language Models (VLMs) handle labeled and unlabeled data through a mix of supervised learning, self-supervised pretraining, and semi-supervised techniques. Labeled data—such as images paired with text descriptions—is used to train models to align visual and textual representations. For example, a VLM might learn to associate a photo of a dog with the caption “a brown dog running in a park” by minimizing the difference between the image and text embeddings. This supervised phase often relies on datasets like COCO or Flickr30K, where explicit annotations provide ground truth for tasks like image-text matching or classification. During this stage, models use contrastive loss or cross-entropy objectives to refine their understanding of relationships between modalities.

Unlabeled data, such as raw images or text without direct pairings, is leveraged through self-supervised pretraining. Techniques like masked language modeling (adapted from text-only models) are extended to both modalities. For instance, a model might mask parts of an image (e.g., pixel regions) or words in a caption and learn to predict the missing elements. CLIP, a widely known VLM, uses a contrastive approach on noisy web-scraped image-text pairs, treating the pairs as weak labels. While these pairs aren’t manually curated, the model still assumes a loose correlation between images and their accompanying text. Truly unpaired data, like standalone images or unrelated text corpora, can also be used through generative tasks, such as reconstructing images from synthetic captions or vice versa, though this is less common in practice.

Semi-supervised methods bridge the gap by combining limited labeled data with abundant unlabeled examples. For example, a VLM pretrained on web data (with noisy labels) might be fine-tuned on a smaller, high-quality labeled dataset for a specific task like visual question answering. Pseudo-labeling is another approach: the model generates labels for unlabeled data (e.g., predicting captions for images) and retrains on these inferred pairs. Consistency regularization—applying slight perturbations to inputs and ensuring consistent predictions—is also used to improve robustness. These strategies allow VLMs to scale effectively, balancing the precision of labeled data with the breadth of unlabeled sources while avoiding overfitting to small annotated datasets.