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Relative Attributes

Relative Attributes. Speaker DengLei At I-VisionGroup. Devi Parikh & Kristen Grauman. ICCV 2011 Marr Prize. Publication—Devi Parikh. … last 3 years: ICCV 3(one only her) ECCV 1 CVPR 9 IJCV 1 NIPS 1. Outline. Introduction Algorithms Experiments.

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Relative Attributes

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  1. Relative Attributes Speaker DengLei At I-VisionGroup

  2. Devi Parikh & Kristen Grauman • ICCV 2011 Marr Prize

  3. Publication—Devi Parikh

  4. … last 3 years: • ICCV 3(one only her) ECCV 1 CVPR 9 IJCV 1 NIPS 1

  5. Outline • Introduction • Algorithms • Experiments

  6. Introduction—Backgrounds • Visual attributes • Benefit various recognition tasks • Restrict on categorical label • Binaries are unnatural • Motivation • How to describe middle image • Relative description —  one image’s attribute strength with respect to others • E.g. less natural than left, more nature than right • Richer mode of communication • Allow more detailed human supervision (maybe higher recognition accuracy) • More informative descriptions of novels

  7. Proposal • Steps • Training – learn ranking function per attribute • Testing – predict the relative strength per attribute on novel image • New Tasks • Build generative model over joint space of ranking output • Zero-shot learning relates unseen to seen • E.g. 'bears are furrier than giraffes‘ • Enable richer textual description for new images • More precise • Tested on faces and natural scenes compared with binaries

  8. Outline • Introduction • Algorithms • Experiments

  9. Algorithms— learning relative attrs

  10. wide-margin {ranking VS binary} classifier

  11. Novel zero-shot learning • Setup • N total categories: S seen, U unseen (no images available) • S: described relative to each other via attrs (no need all pairs) • U: described relative to seen in (subset of ) attrs • Gaussian • Test by Max-likehood

  12. Auto gen relative textual desc of images • Img -> Img • Img -> Class • More info than bin

  13. Outline • Introduction • Algorithms • Experiments

  14. Experiments • Setup • Outdoor Scene Recognition (OSR) • I: 2668, C: 8, • Coast, forest, highway, inside-city, mountain, open-country, street, tall-building • Gist • Public Figures Face (PubFig) • I: 772, C: 8 • Alex, Clive, Hugh, Jared, Miley, Scarlett, Viggo, Zac • Concatenated gist and color feature

  15. Database — relative attrs • Marked By a colleague

  16. zero-shot learning

  17. Conclusion • Idea to learn relative visual attrs. • Two new tasks • Zero-shot learning • Img description • Based on relative description

  18. Thanks

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