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Fei-Fei Li

What Fei-Fei Li thinks about computer vision

@fei-fei-li · 38 positions · On ppll ↗

Sequoia Professor of Computer Science at Stanford, co-director of the Stanford Institute for Human-Centered AI, and co-founder and CEO of World Labs.

Fei-Fei Li did not write this page.

We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

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Only Fei-Fei Li on computer vision. See everyone on computer vision

5 dated positions, 2009 to 2026, in their own words. Our reading of what Fei-Fei Li has said — not written or endorsed by them.

  1. Our readingAtlas is the first model to unify pixel generation and pixel reconstruction, two tracks computer vision has kept apart for over half a century.

    Their wordsIt's the first time we have a unification of pixel generation and pixel reconstruction. In the world of computer vision this field has been around for more than half a century.

    ↗Why Fei-Fei Li Is Betting on Spatial Intelligenceyoutube.com 1st of 13 in this recording

  2. 17 years earlier

  3. Our readingGrowth of internet image data can enable more sophisticated models for indexing and interacting with multimedia.

    Their wordsThe explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data.

    ↗ImageNet: A large-scale hierarchical image database (with 5 co-authors)doi.org All 4 from this paper

  5. Our readingHow large-scale internet image data can be harnessed and organized remains a critical problem.

    Their wordsBut exactly how such data can be harnessed and organized remains a critical problem.

    ↗ImageNet: A large-scale hierarchical image database (with 5 co-authors)doi.org All 4 from this paper

  7. Our readingImageNet is much larger in scale and diversity and more accurate than existing image datasets.

    Their wordsWe show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets.

    ↗ImageNet: A large-scale hierarchical image database (with 5 co-authors)doi.org All 4 from this paper

  9. Our readingImageNet's scale, accuracy, diversity and hierarchy could offer unparalleled opportunities to computer vision researchers.

    Their wordsWe hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.

    ↗ImageNet: A large-scale hierarchical image database (with 5 co-authors)doi.org All 4 from this paper