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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. - PowerPoint PPT Presentation
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Relative Attributes
Speaker DengLei
At I-VisionGroup
i - VisionGroup
Devi Parikh & Kristen Grauman
ICCV 2011 Marr Prize
i - VisionGroup
Publication—Devi Parikh
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… last 3 years: ICCV 3(one only her) ECCV 1 CVPR 9 IJCV 1 NIPS 1
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Outline
Introduction
Algorithms
Experiments
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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
i - VisionGroup
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
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Outline
Introduction
Algorithms
Experiments
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Algorithms— learning relative attrs
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wide-margin {ranking VS binary} classifier
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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
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Auto gen relative textual desc of images
Img -> Img Img -> Class More info
than bin
i - VisionGroup
Outline
Introduction
Algorithms
Experiments
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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
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Database — relative attrs
Marked
By a
colleague
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zero-shot learning
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i - VisionGroup
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Conclusion
Idea to learn relative visual attrs. Two new tasks
Zero-shot learning Img description Based on relative description
i - VisionGroup
Thanks