Perception Encoder: The best visual embeddings are not at the output of the network "we find that contrastive vision-language training alone can produce strong, general embeddings for all of these downstream tasks. There is only one caveat: these embeddings are hidden within the intermediate layers of the network."

@iScienceLuvr Best example is the matryoshka embeddings principle the start dimensions are most important than others. It's like super resolution image where the base img is important right. I made a small WebApp for understanding the concept. Pls checkout 🙏

@iScienceLuvr spectrum-craft.streamlit.app