Comparing CLIP with Traditional Models
- Traditional models like CNN focused on processing images and RNN / transformer focused on processing text. CLIP combined them to get a multimodal understanding.
- Traditional image classifiers were limited in the class categories they were trained on. But CLIP exhibited zero-shot learning meaning they could be used for any class categories.
- The trained CLIP model was able to perform a wide variety of tasks on many existing datasets without any further training.
In this article, we saw an overview of the CLIP model, understood its working in detail, its application and how it has become a part of many current SOTA models.
CLIP (Contrastive Language-Image Pretraining)
CLIP is short for Contrastive Language-Image Pretraining. CLIP is an advance AI model that is jointly developed by OpenAI and UC Berkeley. The model is capable of understanding both textual descriptions and images, leveraging a training approach that emphasizes contrasting pairs of images and text. In this article, we are going to explore the fundamentals and working for CLIP. We are also going to explore its applications.
Table of Content
- Origins and Development of CLIP
- How CLIP Works?
- CLIP’s Unique Approach
- Key Applications and Uses of CLIP in Real-World Scenarios
- Comparing CLIP with Traditional Models
Pre-training is a neural network that learns visual concepts through natural language supervision. What it essentially means is that it can understand the relationship between images and text, i.e., given an image and a set of different text descriptions, the model can accurately tell which description describes the best. One must note that the model does not provide the caption for an image. It tells whether a given description is a good fit or not for a given image. The model has been revolutionary since its introduction, as it has become part of many text-to-image and text-to-video models that have become popular recently.