A Vision-Language Model (VLM) learns associations between images and text through a two-step process: feature extraction and alignment. Initially, the model processes images and text separately to extract meaningful features. For images, convolutional neural networks (CNNs) are often used to identify various patterns, shapes, and objects, translating visual data into a numerical format. For text, recurrent neural networks (RNNs) or transformers can be utilized to convert sentences into numerical representations that capture the semantics of the words. This creates a rich feature set for both modalities, allowing the model to understand the context and components of each input type.
Once the features are extracted, the model moves to the alignment phase. Here, the key task is to establish connections between the visual features and the textual features. This is often done through training techniques like cross-modal contrastive learning, where the model learns to minimize the distance between corresponding image-text pairs while maximizing the distance between non-paired examples. For instance, if an image shows a dog and the corresponding text is “A dog playing in the park,” the model learns to associate the visual features of the dog in the image with the specific words in the sentence. Over time, as the model is exposed to a diverse dataset with many paired images and texts, it becomes better at recognizing and associating relevant aspects of each modality.
In addition to supervised learning, VLMs can also leverage transformer architectures that allow for attention mechanisms. This enables the model to focus on specific parts of the image when processing corresponding text, aiding the learning process. For instance, if the model is shown an image of a car with the caption “A red sports car,” it can concentrate on the specific image regions where the car is depicted while processing the words "red" and "sports car." Such mechanisms enhance the model's ability to create meaningful connections between images and text, making it more proficient in tasks such as image captioning, visual question answering, and other applications that require understanding the relationship between visual content and language.