What kind of pre-processing is required for image and text data in VLMs?

Pre-processing for image and text data in Vision-Language Models (VLMs) involves transforming raw inputs into structured formats compatible with the model’s architecture. This ensures both modalities are aligned and can be processed jointly. The steps differ for images and text but share the goal of creating consistent, normalized inputs for training or inference.

Image Pre-processing Images are typically resized to a fixed resolution (e.g., 224x224 pixels) to match the input dimensions of the model’s vision encoder, such as a Vision Transformer (ViT) or CNN. Pixel values are normalized, often scaling them to a range like [0, 1] or applying mean and standard deviation adjustments (e.g., subtracting mean=[0.485, 0.456, 0.406] and dividing by std=[0.229, 0.224, 0.225] for models pretrained on ImageNet). Data augmentation techniques like random cropping, flipping, or color jittering may be applied during training to improve generalization. For frameworks like PyTorch, this is done using transforms:

transforms.Compose([
 transforms.Resize(256),
 transforms.CenterCrop(224),
 transforms.ToTensor(),
 transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]) 

Models like CLIP or ALIGN require these steps to ensure compatibility with their pretrained weights. Failure to apply the correct normalization can degrade performance.

Text Pre-processing Text is tokenized into subwords or words using methods like WordPiece (BERT) or Byte-Pair Encoding (GPT). Tokenization splits sentences into units the model understands (e.g., “playing” → ["play", “##ing”]). The tokens are then mapped to integer IDs using a predefined vocabulary. Inputs are padded or truncated to a fixed length (e.g., 77 tokens for CLIP) and augmented with special tokens like [CLS] or [SEP] for BERT-style models. For example, the text “A dog running” might become [101, 1037, 3899, 5598, 102] (including [CLS] and [SEP] IDs). Attention masks are created to indicate which tokens are real (1) versus padding (0). Libraries like Hugging Face’s transformers automate this:

tokenizer(text, padding="max_length", max_length=77, return_tensors="pt") 

This ensures text inputs align with the model’s expectations for length and structure.

Integration and Alignment VLMs require paired image-text data, so pre-processing must maintain correspondence between the two modalities. For example, in a dataset like COCO, each image ID is linked to its captions. During training, batches are structured as tuples (image_tensor, text_ids, attention_mask). The vision and text encoders process these inputs separately, and their outputs are fused via cross-attention or projection layers. For inference, tasks like retrieval require encoding images and text into a shared embedding space, so pre-processing must replicate the exact steps used during training (e.g., identical image resize policies or tokenizer settings). Mismatches in normalization or tokenization can disrupt alignment, leading to poor results. For instance, using a different tokenizer for text than the one the VLM was trained on would create embedding mismatches, breaking cross-modal interactions.