Key Points

1. The authors express gratitude to the individuals and organizations involved in creating the data used for training CLIP.

2. They specifically acknowledge Susan Zhang for her work on image conditional language models at OpenAI.

3. Ishaan Gulrajani is recognized for identifying an error in the pseudocode.

4. Irene Solaiman, Miles Brundage, and Gillian Hadfield provided valuable feedback on the broader impacts section of the paper.

5. The Acceleration and Supercomputing teams at OpenAI are acknowledged for their crucial contributions to the software and hardware infrastructure utilized in the project.

6. The developers of various software packages, including Numpy, SciPy, ftfy, TensorFlow, PyTorch, pandas, and scikit-learn, are thanked for their role in the project.

7. The authors express gratitude towards all contributors for their collective input in enabling the research and development of the project.

8. Susan Zhang, Ishaan Gulrajani, Irene Solaiman, Miles Brundage, Gillian Hadfield, and the teams at OpenAI are specifically mentioned for their contributions.

9. The acknowledgement section highlights the collaborative and interdisciplinary nature of the project, underscoring the importance of teamwork and diverse expertise in its execution.

Summary

Breakthrough in Computer Vision Achieved by Scalable Pre-Training Methods
The research paper examines the potential breakthrough in computer vision achieved by scalable pre-training methods that learn directly from web text. The authors demonstrate that pre-training models to predict which caption goes with which image is an efficient and scalable way to learn state-of-the-art image representations from a dataset of 400 million (image, text) pairs collected from the internet. They show that after pre-training, natural language can be used to reference learned visual concepts, enabling zero-shot transfer of the model to downstream tasks. The paper compares the performance of this approach to existing computer vision datasets and systems, showing that the model transfers non-trivially to most tasks and is often competitive with fully supervised baselines without the need for any dataset-specific training.

Performance Comparison with Existing Computer Vision Models
The paper also studies the performance of the pre-trained models in comparison to other existing computer vision models such as EfficientNet, MoCo, SimCLRv2, and various ResNet models. It shows that the large CLIP model slightly outperforms the best existing model, Noisy Student EfficientNet-L2, on both overall score and compute efficiency, but that small CLIP models underperform existing models and that vision transformers are about 3x more compute efficient than CLIP ResNets.

Robustness of CLIP Model to Natural Distribution Shifts
Additionally, the paper discusses the robustness of the CLIP model to natural distribution shifts. It reviews the performance of ImageNet models on various natural distribution shift datasets, such as ImageNetV2, ImageNet Sketch, YouTube-BB, and ObjectNet, showing that the accuracy of CLIP models drops well below the expectation set by the ImageNet validation set across these datasets. The study concludes that CLIP models demonstrate potential in zero-shot transfer and efficient learning from natural language supervision, but their performance on natural distribution shifts raises questions about their robustness compared to existing models.

Revolution in Natural Language Processing and Flagship Systems
The research paper explores the potential breakthrough in computer vision achieved by scalable pre-training methods that learn directly from web text. The paper discusses the revolution in natural language processing (NLP) through task-agnostic objectives and the development of a standardized input-output interface known as "text-to-text." It also examines the competitive capabilities of flagship systems like GPT-3.

The paper compares the potential impact of pre-training methods in computer vision to the current standard practice and presents encouraging prior work in the field. Additionally, the authors examine the use of natural language supervision for image representation learning and analyze the scalability and efficiency of contrastive language-image pre-training (CLIP) in learning from natural language supervision. They compare the transfer performance, efficiency, and robustness of CLIP with traditional supervised models and discuss the policy and ethical implications of their findings.

Reference: https://arxiv.org/abs/2103.00020