In Vision-Language Models (VLMs), pre-processing for image and text data is crucial to ensure that the data is in a suitable format for model training and inference. For image data, this typically includes resizing images to a uniform dimension, normalizing pixel values to a specific range (usually between 0 and 1 or -1 and 1), and possibly augmenting the images to increase diversity in the training set. For instance, images might be randomly rotated, flipped, or adjusted in brightness and contrast. This helps the model generalize better by learning to recognize the same object in different conditions.
On the text side, pre-processing involves several steps as well. First, tokenization is necessary, which means breaking down the text into smaller components like words or subwords, depending on the tokenizer used. After this, it’s important to convert these tokens into numerical formats that the model can work with, often done through embeddings. Additionally, text may need to be cleaned to remove unnecessary characters or stopwords, and consistent casing might be enforced (e.g., converting all text to lowercase). This step helps streamline the text and enhances the model's ability to understand context by focusing on meaningful words.
Finally, integrating the processed image and text data is important for VLMs. This often involves aligning text with specific regions of the image if the model requires it, such as associating captions with their corresponding images. In some cases, a special token or separator may be used to distinguish between image and text inputs. By ensuring that both modalities are pre-processed correctly, developers can create a more effective model that can learn meaningful relationships between the visual and textual data, ultimately improving its performance in tasks like image captioning or visual question answering.