“CLIP: Connecting Text and Images: We’re Introducing a Neural Network Called CLIP Which Efficiently Learns Visual Concepts from Natural Language Supervision. CLIP Can Be Applied to Any Visual Classification Benchmark by Simply Providing the Names of the Visual Categories to Be Recognized, Similar to the ‘Zero-Shot’ Capabilities of GPT-2 and GPT-3”, 2021-01-05 (; backlinks; similar):
[CLIP paper] We present a neural network that aims to address these problems: it is trained on a wide variety of images with a wide variety of natural language supervision that’s abundantly available on the internet. By design, the network can be instructed in natural language to perform a great variety of classification benchmarks, without directly optimizing for the benchmark’s performance, similar to the “zero-shot” capabilities of GPT-2 and GPT-3. This is a key change: by not directly optimizing for the benchmark, we show that it becomes much more representative: our system closes this “robustness gap” by up to 75% while matching the performance of the original ResNet-50 on ImageNet zero-shot without using any of the original 1.28M labeled examples.
Approach: We show that scaling a simple pre-training task is sufficient to achieve competitive zero-shot performance on a great variety of image classification datasets. Our method uses an abundantly available source of supervision: the text paired with images found across the internet. This data is used to create the following proxy training task for CLIP: given an image, predict which out of a set of 32,768 randomly sampled text snippets, was actually paired with it in our dataset.
In order to solve this task, our intuition is that CLIP models will need to learn to recognize a wide variety of visual concepts in images and associate them with their names. As a result, CLIP models can then be applied to nearly arbitrary visual classification tasks. For instance, if the task of a dataset is classifying photos of dogs vs cats we check for each image whether a CLIP model predicts the text description “a photo of a dog” or “a photo of a cat” is more likely to be paired with it.
CLIP is highly efficient…In the end, our best performing CLIP model trains on 256 GPUs for 2 weeks which is similar to existing large scale image models.
CLIP is flexible and general: Because they learn a wide range of visual concepts directly from natural language, CLIP models are substantially more flexible and general than existing ImageNet models. We find they are able to zero-shot perform many different tasks. To validate this we have measured CLIP’s zero-shot performance on over 30 different datasets including tasks such as fine-grained object classification, geo-localization, action recognition in videos, and OCR. [While CLIP’s zero-shot OCR performance is mixed, its semantic OCR representation is quite useful. When evaluated on the SST-2 NLP dataset rendered as images, a linear classifier on CLIP’s representation matches a CBoW model with direct access to the text. CLIP is also competitive at detecting hateful memes without needing ground truth text.] In particular, learning OCR is an example of an exciting behavior that does not occur in standard ImageNet models.
…CLIP allows people to design their own classifiers and removes the need for task-specific training data. [See also “AudioCLIP: Extending CLIP to Image, Text and Audio”, et al 2021; CLIP notebook compilation for art, “Alien Dreams: An Emerging Art Scene”/“AI Generated Art Scene Explodes as Hackers Create Groundbreaking New Tools”.]