Image embedding models and text embedding models both convert raw data into compact vector representations, but they differ significantly in their architectures, input processing, and use cases. The key distinction lies in how they handle the inherent structure of their data: images require spatial and hierarchical feature extraction, while text relies on sequential and contextual understanding. These differences influence the design of neural networks, training methods, and practical applications.
Architecturally, image models typically use convolutional neural networks (CNNs) or vision transformers (ViTs). CNNs apply filters to local regions of an image to detect edges, textures, and shapes, gradually building up hierarchical features through pooling and stacking layers. For example, ResNet-50 uses residual connections to train deeper networks for tasks like object recognition. In contrast, text models like BERT or GPT use transformer architectures, which process sequences of tokens (words or subwords) through self-attention mechanisms to capture contextual relationships. For instance, the word "bank" in "river bank" versus "bank account" gets different embeddings based on surrounding words. While image models focus on spatial invariance (e.g., recognizing a cat regardless of its position), text models prioritize sequential dependencies (e.g., pronoun references across sentences).
Input processing also differs. Images are represented as grids of pixel values, often normalized and augmented (e.g., cropping, rotating) to improve generalization. A 224x224 RGB image might be processed through a CNN’s convolutional layers to produce a 512-dimensional embedding. Text requires tokenization (splitting text into words or subwords) and embedding lookup tables to map discrete tokens to vectors. For example, the word "apple" might be converted to a 768-dimensional vector in a BERT model. Positional encodings are added to text embeddings to preserve word order, whereas CNNs inherently capture spatial relationships through filters. Additionally, pretraining data varies: image models often use labeled datasets like ImageNet, while text models train on large unlabeled corpora (e.g., Wikipedia) via self-supervised tasks like masked language modeling.
Use cases highlight their specialization. Image embeddings excel in tasks like similarity search (e.g., finding product images matching a query photo) or classification (e.g., identifying medical anomalies in X-rays). For example, a recommendation system might use embeddings from a ViT to find visually similar fashion items. Text embeddings power semantic search (e.g., matching user queries to support articles) or machine translation (e.g., aligning multilingual sentence meanings). A chatbot might use sentence embeddings from a model like Sentence-BERT to gauge user intent. Hybrid approaches like CLIP bridge the gap by training on image-text pairs, enabling cross-modal tasks like searching images with text descriptions. However, the core architectures and training strategies remain distinct, reflecting the unique challenges of visual versus linguistic data.