Bibliography (6):

1. https://research.google/blog/align-scaling-up-visual-and-vision-language-representation-learning-with-noisy-text-supervision/

2. ImageNet Large Scale Visual Recognition Challenge

3. Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning

4. Microsoft COCO: Common Objects in Context

5. 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

6. Contrastive Representation Learning: A Framework and Review