Bibliography (7):

  1. 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

  2. ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

  3. https://paperswithcode.com/dataset/flickr30k

  4. LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

  5. Deep Residual Learning for Image Recognition

  6. Vision Transformer: An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale

  7. https://wukong-dataset.github.io/wukong-dataset/