Vision-language models generate captions from images by processing both visual and textual inputs through a series of interconnected components. Firstly, the model extracts features from the image using a convolutional neural network (CNN) or a vision transformer. This step captures important visual information, such as objects, colors, and spatial relations. Simultaneously, the model utilizes a language component, typically an encoder-decoder structure, to understand and produce text. The features from the image are combined with language data, allowing the model to create coherent and contextually relevant captions.
Once the visual features are extracted, the model employs attention mechanisms to focus on specific areas of the image that correspond to relevant parts of the text. This means that if an image contains a dog and a ball, the model learns to highlight the dog when generating a caption that mentions it. For example, if the task is to describe an image of a sunset with palm trees, the model recognizes the colors in the sky and the silhouette of the trees, allowing it to produce a caption like "A vibrant sunset behind palm trees." This process involves training the model on large datasets, where paired images and captions help it learn the relationships between visual elements and language.
After the attention mechanism processes the image features, the model generates a caption by predicting words sequentially. It starts with a predefined token, often representing the beginning of a sentence, and then uses the image features and previously generated words to determine the next word in the caption. This continues until the model produces a complete and meaningful sentence. As an example, for an image of a cat sitting on a window sill, the model might produce the caption "A cat looking out the window." The combination of visual understanding and textual generation allows vision-language models to generate accurate and contextually appropriate captions for various images.