CLIP - Building a Bridge Between Text and Images Using Contrastive Learning

CLIP (Contrastive Language–Image Pre-training) is a vision-language contrastive learning model proposed by OpenAI in 2021. It learns visual concepts from 400 million image–text pairs collected from the internet without any manual annotations. The model consists of an image encoder and a text encoder. Through a contrastive learning objective, it brings matching images and texts closer together in the same embedding space while pushing non-matching pairs apart, thereby obtaining general representations capable of understanding semantics across modalities.

After training, CLIP can perform zero-shot classification: category labels are converted into natural language descriptions (e.g., “A photo of a dog”) and then compared with image features based on similarity. This allows the model to recognize new tasks or categories without any further training. By using natural language as a supervisory signal, CLIP gains strong generalization and task transfer abilities.

CLIP can be considered one of the foundational cornerstones of modern Vision-Language Models (VLMs). It was the first to systematically demonstrate that by unifying visual and linguistic representation spaces through contrastive learning, a model can achieve powerful cross-modal understanding and transfer capabilities without task-specific fine-tuning. This idea directly inspired the design of nearly all subsequent VLMs.

Within VLM architectures, CLIP typically serves as the aligner or encoder backbone between vision and language. It provides a shared semantic space in which textual descriptions and visual content can be directly matched, compared, or combined. For example, models such as BLIP, Flamingo, LLaVA, and GPT-4V often use CLIP’s image encoder as the visual input component, combined with a large language model (LLM) for reasoning and generation. In other words, CLIP is responsible for converting visual signals into semantic vectors understandable by the language model—allowing the LLM to effectively “see” and comprehend images.