In a nutshell, CLIP is a multimodal model that combines knowledge of English-language concepts with semantic knowledge of images.

It can just as easily distinguish between an image of a "cat" and a "dog" as it can between "an il...

Unfortunately, for many hyper-specific use-cases (eg examining the output of microchip lithography) or identifying things invented since CLIP was trained in 2020 (for example, the unique characteristics of CLIP+VQGAN creations), CLIP isn't capable of performing well out of the box for all problem...

We've used CLIP along with GANs to convert text into images; there's no reason we can't go in the other direction and create rich captions for images with creative usage of CLIP (possibly along with a language model like GPT-3).

One of the neatest aspects of CLIP is how versatile it is. When introduced by OpenAI they noted two use-cases: image classification and image generation . But in the 9 months since its release it has been us...

As an extension of image similarity, we've used CLIP to track objects across frames in a video . It uses an object detection model to find items of interest then crops the image and uses CLIP to determine if two detected objec...

If you can classify images, it should be doable to classify frames of videos. In this way you could automatically split videos into scenes and create search indexes. Imagine searching YouTube for your comp...

DALL-E was developed by OpenAI in tandem with CLIP. It's a generative model that can produce images based on a textual description; CLIP was used to evaluate its efficacy.

The DALL-E model has still not been released publicly, but CLIP has been...

CLIP is a gigantic leap forward, bringing many of the recent developments from the realm of natural language processing into the mainstream of computer vision: unsupervised learning, transformers, and multimodality to name a few. The burst of innovation it has inspired shows its versatility.

This is in contrast to traditional computer vision models which disregard the context of their labels (in other words, a "normal" image classifier works just as well if your labels are "cat" and "dog" or "foo" and "bar"; behind the scenes it just converts them into a numeric identifier with no pa...

OpenAI originally evaluated CLIP as a zero-shot image classifier. They compared it against traditional supervised machine learning models and it performed nearly on par with them without having to be trained on any specific dataset.

One challenge with traditional approaches to image classi...

Apple's Neuralhash semantic image similarity algorithm has been in the news a lot recently for how they're applying it to scanning user devices for CSAM. We showed h...

It's not just factual representations that are encoded in CLIP's memory. It also knows about qualitative concepts as well (as we learned from the Unreal engine trick ).

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