Bibliography (7):

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

2. LVIS: A Dataset for Large Vocabulary Instance Segmentation

3. Microsoft COCO: Common Objects in Context

4. 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

5. https://github.com/baaivision/EVA

6. Wikipedia Bibliography:

   1. Object detection

   2. Image segmentation